This PDF file contains the front matter associated with SPIE Proceedings Volume 9061 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

We describe lithium niobate SAW devices and their wave structure at different resonant frequencies, and we discuss the difference between PDMS and PMMA as the material for the microfluidic channel. We discuss the different wave structure for SAW devices operating at different resonant frequencies, showing simulation results and laboratory measurements. We discuss our recent studies to sort microparticles by size.

The low weight, robustness and fatigue resistance of adhesive joints make them suitable for structural joints. A fully developed nondestructive evaluation technique however is needed to monitor and assess the quality of bonded joints. In the present paper the application of the electromechanical impedance (EMI) technique is proposed. In the EMI method a piezoelectric transducer (PZT) is attached to the structure of interest. The high sensitivity and low power consumption make the EMI method feasible for real time structural health monitoring. In this study we investigated the sensitivity of the electromechanical response of a PZT to the curing and the quality of the adhesive used for bonded joints. A PXI unit running under LabView and an auxiliary circuit were employed to measure the electric impedance of a PZT glued to an aluminum plate. The system aimed at monitoring the bond line between an aluminum strip and the plate. The conductive signature of the PZT was measured and analyzed during the curing. The experimental results show that the electromechanical impedance technique is sensitive to the curing time and variations are observed for adhesives of different quality.

This contribution is focused on investigations concerning the damage diagnostics of surface structures using Lamb waves. Based on finite element calculations the potential feasibility for the detection of delaminations, inclusions or cracks have been investigated. For the actuator and sensor material a thin PVDF foil has been used, which is applied to the corresponding component. The advantage of these foils over piezoceramics is that they are also applicable to arbitrarily curved surfaces and their stiffness is compatible with polymer laminates. By evaluating the signals of well-defined arrangements of the electrodes and the generated Lamb waves, existing defects can be identidied. Their exact localization becomes possible with the help of runtime calculations.

In this paper, we present an approach to the generation of steerable ultrasonic beams in structures for possible application to damage detection. The proposed approach is based on the design of embedded acoustic lenses that exploit fundamental principles of wave propagation in acoustic metamaterials. In particular, the lens design relies on the concept of acoustic drop-channel where multiple waveguides can be coupled and selectively activated by simply tuning the frequency of the excitation. Numerical analyses will show that this design allows generating highly directional excitation by using a single ultrasonic transducer. Plane Wave Expansion and Finite Difference Time Domain methods are used to evaluate the dispersion characteristics of the metamaterial lens as well as to simulate the transient response when the lens is embedded in a plate-like structure. The lens design is then experimentally validated on an aluminum plate where the lens is implemented by through the thickness notches. The overall performances are estimated by reconstructing the dynamic displacement field via Laser Vibrometry.

The electromechanical impedance (EMI) method for NDE uses a single piezoelectric material to act as an actuator and a sensor simultaneously, and the EMI method is suitable for structures with complex surfaces. However, this technique still has wide range of problems which needs to be investigated. For one, locating damaged areas on a host structure precisely is known to be extremely difficult as this non-model based technique heavily relies on the variations in the impedance signatures. In this study, an attempt to locate the damaged areas on an ordinary concrete panel and a lightweight concrete panel using bottom ash is carried out by using different frequency ranges. Since the sensing range decreases as the excitation frequency of piezoelectric material increases, one can possibly predict the damaged areas by analyzing the impedance signatures from different frequency ranges. Statistical analysis method such as root mean square deviation (RMSD) is applied to determine the changes of the experimental structures, and the RMSD values of low frequency range and high frequency range are compared to verify the relationship between the frequency range and sensing range. Furthermore, the applicability of this method to locating the damaged areas is investigated on various materials including the lightweight concrete.

Overloading truck loads have long been one of the key reasons for accelerating road damage, especially in rural regions where the design loads are expected to be small and in the cold regions where the wet-and-dry cycle places a significant role. To control the designed traffic loads and further guide the road design in future, periodical weight stations have been implemented for double check of the truck loads. The weight stations give chances for missing measurement of overloaded vehicles, slow down the traffic, and require additional labors. Infrastructure weight-in-motion sensors, on the other hand, keep consistent traffic flow and monitor all types of vehicles on roads. However, traditional electrical weight-in-motion sensors showed high electromagnetic interference (EMI), high dependence on environmental conditions such as moisture, and relatively short life cycle, which are unreliable for long-term weigh-inmotion measurements. Fiber Bragg grating (FBG) sensors, with unique advantages of compactness, immune to EMI and moisture, capability of quasi-distributed sensing, and long life cycle, will be a perfect candidate for long-term weigh-in-motion measurements. However, the FBG sensors also surfer from their frangible nature of glass materials for a good survive rate during sensor installation. In this study, the FBG based weight-in-motion sensors were packaged by fiber reinforced polymer (FRP) materials and further validated at MnROAD facility, Minnesota DOT (MnDOT). The design and layout of the FRP-FBG weight-in-motion sensors, their field test setup, data acquisition, and data analysis will be presented. Upon validation, the FRP-FBG sensors can be applied weigh-in-motion measurement to assistant road managements.

Rough roads increase vehicle operation and road maintenance costs. Consequently, transportation agencies spend a significant portion of their budgets on ride-quality characterization to forecast maintenance needs. The ubiquity of smartphones and social media, and the emergence of a connected vehicle environment present lucrative opportunities for cost-reduction and continuous, network-wide, ride-quality characterization. However, there is a lack of models to transform inertial and position information from voluminous data flows into indices that transportation agencies currently use. This work expands on theories of the Road Impact Factor introduced in previous research. The index characterizes road roughness by aggregating connected vehicle data and reporting roughness in direct proportion to the International Roughness Index. Their theoretical relationships are developed, and a case study is presented to compare the relative data quality from an inertial profiler and a regular passenger vehicle. Results demonstrate that the approach is a viable alternative to existing models that require substantially more resources and provide less network coverage. One significant benefit of the participatory sensing approach is that transportation agencies can monitor all network facilities continuously to locate distress symptoms, such as frost heaves, that appear and disappear between ride assessment cycles. Another benefit of the approach is continuous monitoring of all high-risk intersections such as rail grade crossings to better understand the relationship between ride-quality and traffic safety.

Impact event identification is a primary concern in many structural health monitoring applications. Model-based inverse analysis is a common approach for system identification as long as the physical model can accurately capture the behavior of structure. A layered analysis including estimation of impact location (IL) in the first layer and reconstruction of impact load time history (ILTH) in the second layer was proposed. An implementation of the theory on a simply supported plate structure is conducted in this study. The results indicate that the proposed inverse scheme is capable of detecting impact location and reconstructing impact load time history with a satisfactory precision. Due to presence of system error, a suitable cost function has to be chosen to guide the fitting process toward the desired parameters.

The large span and heterogeneous components of multi-layered pavement structure usually bring about stochastic damage, and many modern approaches, such as ground penetrating radar, integral imaging and optical fiber sensing technology, have been employed to detect the degeneration mechanism. Restricted by the cost and universality, novel elements for pavement monitoring are in high demand. Optical fiber sensing technology for high sensitivity, long stability, anti-corrosion and resistance to water erosion then is considered. Therefore, a movable FBG sensor located in flexible pipe is developed, which has long stroke inside inner wall of the hollow pipe, and a full-scale shape of the structure could be sketched just with one FBG. Theoretical and experimental methods about establishing the relationship between wavelength variable and curvature have been provided, and function about reconfiguring the coordinate is converted to a mathematic question. Move over, transfer error modification has been taken into account for modify related error. Multi-layered pavement model embedded with this sensor will be accomplished to inspect its performance in later work. The work in the paper affords a feasible method for shape monitoring and would be potentially valuable for the maintenance and inverse design of pavement structure.

Alkali-silica reaction (ASR) is a chemical reaction that can occur between alkaline components in cement paste and reactive forms of silica in susceptible aggregates when sufficient moisture is present. The ASR product, known as ASR gel, can cause expansion and cracking that damages the structure. We pass ultrasonic signals through concrete laboratory specimens and use three different ultrasonic methods to detect the onset of ASR damage, or the presence of ASR damage while still at the microscale. Our test specimens are fabricated with aggregate known to be reactive and are then exposed to an aggressive environment to accelerate ASR development. We use swept-sine excitations and obtain pitch-catch records from specimens that have been exposed to the accelerated environment. From this data, we demonstrate an ultrasonic passband method shows high frequency components diminish faster than low frequency components, and therefore the ultrasonic passband shifts to the low frequency side due to ASR damage. The test results also show that the ultrasonic passband is logically related to specimen size. We also demonstrate a stretching factor method is able to track the progress of ASR damage in concrete very well. These methods are shown to be more reliable than attenuation spectrum or attenuation methods that do not detect the ASR damage in concrete at early stages.

A study is presented to investigate a magnetostrictive flow sensor for use in a scour monitoring system. Using a thin Galfenol “whisker” a sensor is constructed to detect the presence of water flow. Due to the desire for the whisker to respond dynamically rather than with just a quasi-static deflection when in a steady stream of flowing water, two configurations of the sensor are tested, one in which only the bare whisker is exposed to the water flow and one in which an unstable airfoil is fixed to the whisker. Three primary conclusions are inferred. First, the study confirms that Galfenol has the structural properties necessary to create a tactile sensor. Second it has been demonstrated that this tactile sensor is capable of being sensitive to water flow. Finally, it has been determined that alterations to the geometry of the whisker, specifically the addition of an unstable airfoil, can create the necessary dynamic response required for such a sensor.

Post-tensioned segmental bridges are common throughout the US; however, in recent years, the incidence of tendon failure in bonded post-tensioned bridges has raised questions regarding their design, construction, and maintenance. These failures have led to the investigation of the applicability of using replaceable unbonded tendons in segmental construction and new methods for monitoring their condition. This paper presents a damage detection algorithm to identify strand breakage in unbonded tendons based on the relative variation of strains in the anchorage. In unbonded construction, the anchorage assembly usually undergoes a severe stress-state condition as the entire prestressing force only passes through the deviator and end anchorage locations. The strain distribution in the anchorage mechanism, therefore, goes through significant changes in response to the breakage of an individual wire or an entire strand in a multi-strand arrangement. In this way, breakage of a post-tensioning strand can be identified by observing a non-uniform variation of the strain field over the anchorage region in contrast to a uniform variation of strains due to environmental or traffic loading. A reduced scale laboratory experiment is performed followed by an extensive finite element simulation to conduct a parametric study with wire/strand breakages at different locations on multi-strand anchorages commonly used in industry. Based on the observed strain variations from simulation, a damage detection model is proposed that enables the adoption of an automated monitoring strategy to characterize the breakage programmatically.

The increasing worldwide efforts in securing renewable energy sources increase incentive for civil engineers to investigate whether the kinetic energy associated with the vibration of larger-scale structures can be harvested. Such a research remains challenging and incomplete despite that hundreds of related articles have been published in the last decade. Base isolation is one of the most popular means of protecting a civil engineering structure against earthquake forces. Seismic isolation hinges on the decoupling of the structure from the shaking ground, hence protecting the structure from stress and damage during an earthquake excitation. The low stiffness isolator inserted between the structure and the ground dominates the response leading to a structural system of longer vibration period. As a consequence of this period shift, the spectral acceleration is reduced, but higher response displacements are produced. To mitigate this side effect, usually isolators are combined with the use of additional energy dissipation. In this study, the feasibility of scavenging the need-to-be dissipated energy from the isolator installed in a seismically isolated bridge using an electromagnetic (EM) energy harvester is investigated. The EM energy harvester consists of an energy harvesting circuit and a capacitor for energy storage. A mathematical model for this proposed EM energy harvester is developed and implemented on an idealized base-isolated single-degree-of-freedom system. The effect of having this EM energy harvester on the performance of this seismic isolated system is analyzed and discussed. The potential of installing such an EM energy harvester on a seismically isolated bridge is also addressed.

In both high speed and freight rail systems, the modern construction method is Continuous Welded Rail (CWR). The purpose of the CWR method is to eliminate joints in order to reduce the maintenance costs for both the rails and the rolling stock. However the elimination of the joints increases the risk of rail breakage in cold weather and buckling in hot weather. In order to predict the temperature at which the rail will break or buckle, it is critical to have knowledge of the temperature at which the rail is stress free, namely, the Rail Neutral Temperature (Rail-NT).The University of California at San Diego has developed an innovative technique based on non-linear ultrasonic guided waves, under FRA research and development grants for the non-destructive measurement of the neutral temperature of railroad tracks. Through the licensing of this technology from the UCSD and under the sponsorship of the FRA Office of Research and Development, a field deployable prototype system has been developed and recently field tested at cooperating railroad properties. Three prototype systems have been deployed to the Union Pacific (UP), Burlington Northern Santa Fe (BNSF), and AMTRAK railroads for field testing and related data acquisition for a comprehensive evaluation of the system, with respect to both performance and economy of operation. The results from these tests have been very encouraging. Based on the lessons learned from these field tests and the feedback from the railroads, it is planned develop a compact 2^nd generation Rail-NT system to foster deployment and furtherance of FRA R&D grant purpose of potential contribution to the agency mission of US railroad safety. In this paper, the results of the field tests with the railroads in summer of 2013 are reported.

In this paper, we investigate the use of dynamic structural tailoring via the concept of an Acoustic Black Hole (ABH) to enhance the performance of piezoelectric based energy harvesting from operational mechanical vibrations. The ABH is a variable thickness structural feature that can be embedded in the host structure allowing a smooth reduction of the phase velocity while minimizing the amplitude of reflected waves. The ABH thickness variation is typically designed according to power-law profiles. As a propagating wave enters the ABH, it is progressively slowed down while its wavelength is compressed. This effect results in structural areas with high energy density that can be exploited effectively for energy harvesting. The potential of ABH for energy harvesting is shown via a numerical study based on fully coupled finite element electromechanical models of an ABH tapered plate with surface mounted piezo-transducers. The performances of the novel design are evaluated by direct comparison with a non-tapered structure in terms of energy ratios and attenuation indices. Results show that the tailored structural design allows a drastic increase in the harvested energy both for steady state and transient excitation. Performance dependencies of key design parameters are also investigated.

This paper presents an analytical model that simulates ultrasound generation and sensing in a slender structure using bonded piezoelectric wafer active sensors (PWAS). To account for multi-mode ultrasound generation, both the longitudinal and flexural vibrations of the slender structure were considered. The effect of adhesive thickness on the ultrasound pitch-catch signals was investigated. The simulation results predict that there exists an optimal adhesive layer thickness for both ultrasound modes. Two types of experiments were carried out to validate the simulation predictions. Quantitative agreements between simulation and measurement results were achieved.

The piezoelectric impedance-based method for damage detection has been explored extensively for its high sensitivity to small-sized damages with low-cost measurement circuit which enables remote damage monitoring. While the method has good potential, the amount of feasible impedance data is usually much less than the number of required system parameters to accurately identify the damage location/severity via an inverse formulation. This data incompleteness forms a highly underdetermined problem and because of this numerical ill-conditioning, the predicted damage parameters will be significantly influenced by unavoidable measurement noise and the accuracy of the base-line model. In this study, the state of the art of impedance-based damage identification is advanced by incorporating a tunable piezoelectric circuitry with the structure to enrich the impedance measurements. This piezoelectric circuitry introduces additional degrees of freedom to the structure and changes the dynamics of the coupled system. By tuning the inductance value, it is possible to perform various measurements under different system dynamics which reflects the damage effect. Therefore, if performed systematically, notably increased sets of measurement can be obtained, which will improve the inverse problem to be less underdetermined. Clearly, we can expect the accuracy and robustness in damage identification to be significantly enhanced. Numerical case study on localizing damage in a fixed-fixed beam using spectral element method is performed to demonstrate the effectiveness of the new method for structural damage identification.

Health monitoring of rotorcraft components, which is currently being performed by Health and Usage Monitoring Systems (HUMS) through analyzing vibration signatures of dynamic mechanical components, is very important for their safe and economic operation. Vibration diagnostic algorithms in HUMS analyze vibration signatures associated with faults and quantify them as condition indicators (CI) to predict component behavior. Vibration transfer paths (VTP) play important roles in CI response and are characterized by frequency response functions (FRF) derived from vibration signatures of dynamic mechanical components of a helicopter. With an objective to investigate the difference in VTP of a component in a helicopter and test stand, and to relate that to the CI response, VTP measurements were recorded from 0–50 kHz under similar conditions in the left and right nose gearboxes (NGBs) of an AH-64 Apache and an isolated left NGB in a test stand at NASA Glenn Research Center. The test fixture enabled the application of measured torques – common during an actual operation. Commercial and lab piezo shakers, and an impact hammer were used in both systems to collect the vibration response using two types of commercially available accelerometers under various test conditions. The FRFs of both systems were found to be consistent, and certain real-world installation and maintenance issues, such as sensor alignments, locations and installation torques, had minimal effect on the VTP. However, gear vibration transfer path dynamics appeared to be somewhat dependent on presence of oil, and the lightly-damped ring gear produced sharp and closer transfer path resonances.

Magnetic transducers have been applied in impedance-based damage detection recently. Owing to the magneto-mechanical coupling characteristics between a magnetic transducer and the underneath metallic structure, a magnetic transducer can excite the host structure by means of the Lorenz force, and its electrical impedance is directly related to the host structure’s mechanical impedance. Therefore, the change of electrical impedance before and after damage occurrence can be used as damage indicator. Since there is no direct contact between the magnetic transducer and the host structure, it appears that the magnetic transducer has advantage in online health monitoring of many structures with complex geometries and boundaries. However, one key issue is that the coupling between the magnetic transducer and the host structure is strongly influenced by the lift-off distance (i.e. the distance from the transducer to the host structure) which changes as the structure is inevitably subject to oscillation/movement due to environment disturbance. In this research, we propose a new approach of transformed impedance that can explicitly take the lift-off distance change into consideration to facilitate efficient and robust decision making. This algorithm takes advantage of the lift-off distance embedded in the impedance measurement, and is capable of removing the lift-off variation without explicitly measuring the lift-off variation. Numerical simulations and experimental validations are carried out to demonstrate the effectiveness.

Structural health monitoring (SHM) relies on collection and interrogation of operational data from the monitored structure. To make this data meaningful, a means of understanding how damage sensitive data features relate to the physical condition of the structure is required. Model-driven SHM applications achieve this goal through model updating. This study proposed a novel approach for updating of aero-elastic turbine blade vibrational models for operational horizontal-axis wind turbines (HAWTs). The proposed approach updates estimates of modal properties for spinning HAWT blades intended for use in SHM and load estimation of these structures. Spinning structures present additional challenges for model updating due to spinning effects, dependence of modal properties on rotational velocity, and gyroscopic effects that lead to complex mode shapes. A cyclo-stationary stochastic-based eigensystem realization algorithm (ERA) is applied to operational turbine data to identify data-driven modal properties including frequencies and mode shapes. Model-driven modal properties are derived through modal condensation of spinning finite element models with variable physical parameters. Complex modes are converted into equivalent real modes through reduction transformation. Model updating is achieved through use of an adaptive simulated annealing search process, via Modal Assurance Criterion (MAC) with complex-conjugate modes, to find the physical parameters that best match the experimentally derived data.

The breathing cracks in truss system are detected by Frequency Response Function (FRF) based damage identification method. This method utilizes damage-induced changes of frequency response functions to estimate the severity and location of structural damage. This approach enables the possibility of arbitrary interrogation frequency and multiple inputs/outputs which greatly enrich the dataset for damage identification. The dynamical model of truss system is built using the finite element method and the crack model is based on fracture mechanics. Since the crack is driven by tensional and compressive forces of truss member, only one damage parameter is needed to represent the stiffness reduction of each truss member. Assuming that the crack constantly breathes with the exciting frequency, the linear damage detection algorithm is developed in frequency/time domain using Least Square and Newton Raphson methods. Then, the dynamic response of the truss system with breathing cracks is simulated in the time domain and meanwhile the crack breathing status for each member is determined by the feedback from real-time displacements of member’s nodes. Harmonic Fourier Coefficients (HFCs) of dynamical response are computed by processing the data through convolution and moving average filters. Finally, the results show the effectiveness of linear damage detection algorithm in identifying the nonlinear breathing cracks using different combinations of HFCs and sensors.

This paper provides a unified framework for minimal mass design of tensegrity systems. For any given configuration and any given set of external forces, we design force density (member force divided by length) and cross-section area to minimize the structural mass subject to an equilibrium condition and a maximum stress constraint. The answer is provided by a linear program. Stability is assured by a positive definite stiffness matrix. This condition is described by a linear matrix inequality. Numerical examples are shown to illustrate the proposed method.

In this research, two radiofrequency identification (RFID) antenna sensor designs are tested for compressive strain measurement. The first design is a passive (battery-free) folded patch antenna sensor with a planar dimension of 61mm × 69mm. The second design is a slotted patch antenna sensor, whose dimension is reduced to 48mm × 44mm by introducing slots on antenna conducting layer to detour surface current path. A three-point bending setup is fabricated to apply compression on a tapered aluminum specimen mounted with an antenna sensor. Mechanics-electromagnetics coupled simulation shows that the antenna resonance frequency shifts when each antenna sensor is under compressive strain. Extensive compression tests are conducted to verify the strain sensing performance of the two sensors. Experimental results confirm that the resonance frequency of each antenna sensor increases in an approximately linear relationship with respect to compressive strain. The compressive strain sensing performance of the two RFID antenna sensors, including strain sensitivity and determination coefficient, is evaluated based on the experimental data.

A sensing sheet based on large-area electronics consists of a dense array of discrete short-gauge sensor units, integrated circuits for collecting, analyzing and communicating data, and a flexible photovoltaic system that serves as both a power harvester and a protective layer. We investigated the sensitivity of thin-film full-bridge strain sensors to cracks, and the effect of crack position with respect to the sensor. In general, the results of this research show that full-bridge strain sensors can successfully detect and characterize cracks in structural materials and are therefore good candidates to utilize in a sensing sheet.

A process for the design and optimization of a morphing aileron using flexible matrix composite (FMC) actuators is developed. Design requirements were based on the use of a morphing aileron with an existing medium size commercial transport aircraft limiting the planform to the existing conventional aileron. The design uses two sets of contracting FMC actuators operating independently to actuate the deformable aileron in both trailing edge up and down cases while matching coefficient of lift values of a conventional aileron. The design was optimized by developing a series of response models which were then used to understand the sensitivity to material, geometric and loading design variables while minimizing required force from the FMC actuators. Using a combination of response models and sequential quadratic programming algorithm, a design is chosen that matched the conventional ailerons performance at multiple flight conditions.

The objective of this paper is to investigate the application of the photogrammetric approach to measuring the vibration of a research-scale wind turbine blade model (both damage and undamaged blade). In order to control the excitation (rotation of the wind turbine blade), a motor was used to spin the blades at controlled angular velocities. Two cameras are set in front of the turbine to tape the video images. Through a sequence of stereo image pairs acquired by high speed camera, the images are studied. The camera we used is the BASLER acA2000-340km (2048x1088, 340FPS). Before taking the photos camera calibration was conducted which include lens distortion and skew factor is examined. To analyze the displacement of the motion target on the turbine blade, after loading the 3D calibration, the 3D positions are calculated by using a stereo triangulation technique. Then the displacement fields by image template matching can be calculated. Application of the technique to track the 3D motion of the rotating wind turbine blade is demonstrated by using data from the research-scale wind turbine. Different from the image processing technique data from the contact sensors (accelerometers) is also used. Through Rodrigues' rotation formula to remove the rotation frequency it is easy to extract the out-of-plane motion of the blade, from which the model frequency of the blade can be identified.

This paper presents the first documented investigation of a flow-induced vibration phenomenon referred to as dual cantilever flutter (DCF). The purpose of this research is to introduce the concept of DCF and to help understand the key components that cause it. If unaccounted for, vibration caused by DCF has the potential to cause catastrophic structural damage or unwanted acoustic excitation. DCF may also be used as an effective energy harvesting method. The most attractive feature of DCF for energy harvesting is that it provides a robust type of broadband, flow-induced excitation. This paper will present an experimental and analytical study on a novel, solidstate, DCF energy harvesting device. Results will include CFD simulations that were setup and executed using ANSYS-CFX.

Delamination faults in composite plates are considered dangerous as they can cause catastrophic failure before being visually assessed. Effects of delaminations are particularly relevant in guided waves scattering, local resonances and mode conversion. Detecting and analyzing these phenomena is relevant for plate characterization. In this work, leaky guided waves are used to detect delamination in composite plates. To such purpose, a hybrid ultrasonic set-up and a dedicated signal processing are proposed. An air-probe with a proper lift-off is used to detect the leakage in terms of air pressure wave over the plate surface. A piezoelectric transducer is used to generate acoustic guided waves in the composite plate. Multiple acquisitions are averaged to increase the SNR for each position of the air-probe. Curvelet Transform (CT) domain processing of the projection coefficients of the acquired elastic wave is exploited to decompose waves that are overlapped both in the time/space and in the frequency/wavenumber domain. In fact, CT is a special member of the family of multiscale and multidimensional transforms whose spatial and temporal localization is very well suited for processing signals which are sparse in the above mentioned domains. In this work this sparsity is exploited to emphasize the information of leaky guided waves scattered by the delamination by removing from the data the information related to the incident wave field. As an application, the presence of a delamination generated by a 21 Joule impact performed on a 4.9 mm thickness composite laminate was detected contactless by exploiting guided wave leakage.

A novel thin film sensor consisting of a soft elastomeric capacitor (SEC) for meso-scale monitoring has been developed by the authors. Each SEC transduces surface strain into a measurable change in capacitance. In previous work, the authors have shown that the performance of the SEC compares well with conventional resistive strain gauges, providing a resolution of 25 με using an inexpensive off-the-shelf data acquisition system for capacitance measurements. Here, we further the understanding of the thin film sensor by characterizing its dynamic behavior. The SEC is subjected to dynamic loads in bending mode. The study of Fourier and wavelet transforms indicates that the sensor can be used to identify dynamic inputs. Overall results demonstrate the promising capabilities of the thin film sensor at dynamic monitoring of civil structures.

Existing sensing solutions facilitating continuous condition assessment of wind turbine blades are limited by a lack of scalability and clear link signal-to-prognosis. With recent advances in conducting polymers, it is now possible to deploy networks of thin film sensors over large areas, enabling low cost sensing of large-scale systems. Here, we propose to use a novel sensing skin consisting of a network of soft elastomeric capacitors (SECs). Each SEC acts as a surface strain gage transducing local strain into measurable changes in capacitance. Using surface strain data facilitates the extraction of physics-based features from the signals that can be used to conduct condition assessment. We investigate the performance of an SEC network at detecting damages. Diffusion maps are constructed from the time series data, and changes in point-wise diffusion distances evaluated to determine the presence of damage. Results are benchmarked against time-series data produced from off-the-shelf resistive strain gauges. This paper presents data from a preliminary study. Results show that the SECs are promising, but the capability to perform damage detection is currently reduced by the presence of parasitic noise in the signal.

Over the last few decades, carbon nanotube (CNT)-based thin films or nanocomposites have been widely investigated as a multifunctional material. The proposed applications extend beyond sensing, ultra-strong coatings, biomedical grafts, and energy harvesting, among others. In particular, thin films characterized by a percolated and random distribution of CNTs within a flexible polymeric matrix have been shown to change its electrical properties in response to applied strains. While a plethora of experimental work has been conducted, modeling their electromechanical response remains challenging. Furthermore, their design and optimization require the derivation of accurate electromechanical models that could predict thin film response to applied strains. Thus, the objective of this study is to implement a percolation-based piezoresistive model that could explain the underlying mechanisms for strain sensing. First, a percolation-based model with randomly distributed, straight CNTs was developed in MATLAB. Second, the number of CNTs within a unit area was varied to explore its influence on percolation probability. Then, to understand how the film’s electrical properties respond to strain, two different models were implemented. Both models calculated the geometrical response of the film and CNTs due to applied uniaxial strains. The first model considered the fact that the electrical resistance of individual CNTs changed depending solely on its length between junctions. The other model further explored the idea of incorporating strain sensitivity of individual CNTs. The electromechanical responses and the strain sensitivities of the two models were compared by calculating how their bulk resistance varied due to applied tensile and compressive strains. The numerical model results were then qualitatively compared to experimental results reported in the literature.

Scour occurring near bridge piers and abutments jeopardizes the stability and safety of overwater bridges. In fact, bridge scour is responsible for a significant portion of overwater bridge failures in the United States and around the world. As a result, numerous methods have been developed for monitoring bridge scour by measuring scour depth at locations near bridge piers and foundations. Besides visual inspections conducted by trained divers, other technologies include sonar, float-out devices, magnetic sliding collars, tilt sensors, and fiber optics, to name a few. These systems each offer unique advantages, but most of them share fundamental limitations (e.g., high costs, low reliability, limited accuracy, low reliability, etc.) that have limited their implementation in practice. Thus, the goal of this study is to present a low-cost and simple scour depth sensor fabricated using piezoelectric poly(vinylidene fluoride) (PVDF) polymer strips. Unlike current piezoelectric scour sensors that are based on mounting multiple and equidistantly spaced transducers on a rod, the proposed sensor is formed by coating one continuous PVDF film onto a substrate, followed by waterproofing the sensor. The PVDF-based sensor can then be buried in the streambed and at a location where scour depth measurements are desired. When scour occurs and exposes a portion of the PVDF sensor, water flow excites the sensor to cause the generation of a time-varying voltage signal. Since the dynamics of the voltage time history response is related to the exposed length of the sensor, scour depth can be determined. This work presents the design and fabrication of the sensor. Then, the sensor’s performance and accuracy is characterized through extensive laboratory testing.

This paper presents a new method for monitoring and characterizing cracks using Ba_0.64Sr_0.36TiO₃ flexoelectric strain gradient sensors. Firstly, strain gradient field around the mixed mode asymptotic crack tip was analyzed, followed by the derivation of induced flexoelectric polarization in the strain gradient sensors attached in the vicinity of a crack tip. It was found that the flexoelectric polarization of the sensor can be expressed as a function of the stress intensity factors of crack and relative coordinates between the sensor and crack. Given the information of the crack size, further analysis demonstrates that the location of the crack can be traced through the calculation based on flexoelectric outputs of the distributed sensors. A specimen with Mode-I crack was then prepared with two strain gradient sensors (4.7 mm × 0.9 mm × 0.3 mm) attached close to the crack tip to verify the analytical model for detection of cracks. The experimental results yield accurate location of the crack, confirming that flexoelectric strain gradient sensing can be a good avenue for monitoring cracks.

This paper presents a fatigue crack detection technique based on visualization of nonlinear ultrasonic wave modulation produced by a fatigue crack. When distinctive low frequency (LF) and high frequency (HF) inputs are generated and applied to a structure, the presence of a fatigue crack can provide a mechanism for nonlinear ultrasonic modulation and create spectral sidebands around the frequency of the HF signal. In this study, the two input signals are created by two air-coupled transducers (ACT), and the corresponding ultrasonic responses are scanned over a target specimen using a 3D laser Doppler vibrometer (LDV). The crack-induced spectral sidebands are isolated using a combination of linear response subtraction (LRS), and continuous wavelet transform (CWT) filtering. Then, the extracted spectral sideband components are visualized near the fatigue crack. The effectiveness of the proposed non-contact scanning technique is tested using an aluminum plate with a real fatigue crack.

The development of fatigue cracks at fastener holes due to stress concentration is a common problem in aircraft maintenance. This contribution investigates the use of high frequency guided waves for the non-contact monitoring of fatigue crack growth in tensile, aluminium specimens. High frequency guided ultrasonic waves have a good sensitivity for defect detection and can propagate along the structure, thus having the potential for the inspection of difficult to access parts by means of non-contact measurements. Experimentally the required guided wave modes are excited using standard wedge transducers and measured using a laser interferometer. The growth of fatigue cracks during cyclic loading was monitored optically and the resulting changes in the signal caused by crack growth are quantified. Full three-dimensional simulation of the scattering of the high frequency guided ultrasonic waves at the fastener hole and crack has been implemented using the Finite Difference (FD) method. The comparison of the results shows a good agreement of the measured and predicted scattered field of the guided wave at quarter-elliptical and through-thickness fatigue cracks. The measurements show a good sensitivity for the early detection of fatigue damage and for the monitoring of fatigue crack growth at a fastener hole. The sensitivity and repeatability are ascertained, and the robustness of the methodology for practical in-situ ultrasonic monitoring of fatigue crack growth is discussed.

This paper presents a testing method that offers an environment to evaluate the modal vibratory characteristics of structural components in building structures using ambient vibration responses, named substructure resonance vibration testing. In the proposed test configuration, a specimen of structural components is installed to a resonance frame that supports large fictitious mass and the resonance frequency of the entire system is set as the natural frequency of a building structure. The resonance frame is interfaced with a quasi-static loading system and a modal shaker. The specimen is damaged quasi-statically and, resonance vibration tests are conducted with the modal shaker at the presence of notable damage. The proposed method enables the vibratory evaluation of realistic damage in structural components without constructing a large specimen of entire structural systems. A proof-of-concept test was conducted with a quarterscale beam-column connection of a mid-rise steel building. The changes in vibratory characteristics were monitored using accelerometers and dynamic strain sensors. The preliminary test results show the effectiveness of the proposed test method for constructing a damage sensitive feature of structural components.

ABSTRACT The inspection and maintenance of bridges of all types is critical to the public safety and often critical to the economy of a region. Recent advanced sensor technologies provide accurate and easy-to-deploy means for structural health monitoring and, if the critical locations are known a priori, can be monitored by direct measurements. However, for today’s complex civil infrastructure, the critical locations are numerous and often difficult to identify. This paper presents an innovative framework for structural monitoring at arbitrary locations on the structure combining computational models and limited physical sensor information. The use of multi-metric measurements is advocated to improve the accuracy of the approach. A numerical example is provided to illustrate the proposed hybrid monitoring framework, particularly focusing on fatigue life assessment of steel structures.

This study proposes two algorithms for estimating the maximum response of a building when subject to a devastating earthquake only using a sensor agent robot equipped with an accelerometer situated on an arbitrary floor of the building. The first algorithm is for normal buildings. The second one is for base isolated buildings. The algorithms do not require the information on the input motion. The sensor agent robot may be used for other purposes such as security and lighting control under normal circumstances. Thus the proposed algorithms are very useful tools for a robot-based structural health monitoring system.

System identification is a fundamental process for developing a numerical model of a physical structure. The system identification process typically involves in data acquisition; particularly in civil engineering applications accelerometers are preferred due to its cost-effectiveness, low noise, and installation convenience. Because the measured acceleration responses result in translational degrees of freedom (DOF) in the numerical model, moment-resisting structures such as beam and plate are not appropriately represented by the models. This study suggests a system identification process that considers both translational and rotational DOFs by using accelerometers and gyroscopes. The proposed approach suggests a systematic way of obtaining dynamic characteristics as well as flexibility matrix from two different measurements of acceleration and angular velocity. Numerical simulation and laboratory experiment are conducted to validate the efficacy of the proposed system identification process.

System identification of civil engineering structures are often formulated as Multiple-Input, Multiple-Output (MIMO) problems due to the complexity of loading conditions such as differential ground motion, which is also multi-directional in nature. Such MIMO system identification problems are challenging due to strong coupling between the contributions of multiple ground motion inputs to each individual response. Compared with Single-Input, Multiple-Output (SIMO) system identification, MIMO problems are often more computationally complex and error prone. In this paper, a new system identification strategy is proposed in which a more complex MIMO problem is converted into a number of SIMO problems by decoupling the contribution of multiple inputs to the outputs. A QR-factorization based approach is adopted for the decoupling and its accuracy is investigated. The effectiveness of the proposed strategy is demonstrated through applications to a two-span straight bridge and a four-span curved bridge, both are highway bridges.

This paper presents a methodology on prioritizing deployment of scour sensors at high-risk bridges using a holistic approach. Specifically, stream geomorphic data, bridge design information, and bridge scour ratings are used to intelligently identify and prioritize bridge sites for remote scour monitoring. Scour is the leading cause of bridge failure, with 309 bridges in the State of Vermont rated as scour critical. Using Vermont as a case study, this work looks to correlate bridge structural design data with hydraulic and stream geomorphic information to identify bridges at high risk of scour processes, and specifically for bridges more structurally susceptible to scour damage. Including additional stream and hydraulic indicators in the analysis, rather than relying on structural evaluations alone, enables areas of geomorphic instability to be identified; and bridges in these areas can be outfitted with scour monitoring devices. Lowcost monitoring devices are proposed to monitor at-risk bridges and to provide additional information in scour-prone areas. A sensor under development would allow for direct installment into stream beds at existing bridges, and incorporate accelerometer-based monitoring, with wireless data transmission. The device would allow for real-time measurement of streambed degradation and aggradation during high flow events, thereby providing timely information regarding critical scour events. This work aims to aid State transportation engineers in identifying, which bridges in the transportation network are at risk, of scour processes, provides useful insight into scour rating systems, and assesses the value of the geomorphic assessments to improve our existing bridge rating system. The compilation of results from geomorphic assessments, scour ratings, and bridge design information will be presented.

Guided waves can propagate long distances and are sensitive to subtle structural damage. Guided-wave based damage localization often requires extracting the scatter signal(s) produced by damage, which is typically obtained by subtracting an intact baseline record from a record to be tested. However, in practical applications, environmental and operational conditions (EOC) dramatically affect guided wave signals. In this case, the baseline subtraction process can no longer perfectly remove the baseline, thereby defeating localization algorithms. In previous work, we showed that singular value decomposition (SVD) can be used to detect the presence of damage under large EOC variations, because it can differentiate the trends of damage from other EOC variations. This capability of differentiation implies that SVD can also robustly extract a scatter signal, originating from damage in the structure, that is not affected by temperature variation. This process allows us to extract a scatterer signal without the challenges associated with traditional temperature compensation and baseline subtraction routines. . In this work, we use to approach to localize structural damage in large, spatially and temporally varying EOCs. We collect pitch-catch records from randomly placed PZT transducers on an aluminum plate while undergoing temperature variations. Damage is introduced to the plate during the monitoring period. We then use our SVD method to extract the scatter signal from the records, and use the scatter signal to localize damage using the delay-and-sum method. To compare results, we also apply several temperature compensation methods to the records and then perform baseline subtraction. We show that our SVD-based approach successfully localize damage while current temperature-compensated baseline subtraction methods fail.

This paper proposes a series of novel Damage Sensitive Features for earthquake damage estimation. The features take into account input (ground motion) and output acceleration (structure response) measurements. The Continuous Wavelet Transform is applied to both acceleration signals in order to obtain both time domain and frequency domain resolution. An algorithm that has been proposed for Maximum Entropy Deconvolution is applied to the Continuous Wavelet Transforms in order to obtain a matrix that relates the output wavelet coefficients to the input ones. The Damage Sensitive Features are then derived through statistical processing of the resulting matrix. This algorithm has been applied on data acquired from shake table tests where the structures were subjected to progressive damage. The proposed features are compared to response quantities that are indicative of damage (such as the hysteretic energy dissipated) and show high correlation with the extent of damage. The data utilized has not been pre-processed, illustrating the robustness of the algorithm against sensor noise. The proposed algorithm has several advantages: Minimal input and knowledge of the structure is required. More information on the structure's state is extracted through use of both the input and output signals than when only output signal is considered. Only two acceleration measurements are required to obtain a damage forecast utilizing primarily the strong motion recordings, resulting in easier sensor deployment. The use of strong motion recordings allows for information delivery immediately after an earthquake without additional data collection.

One of the main challenges in real-world application of guided-waves based nondestructive evaluation (NDE) of pipelines is their sensitivity to changes in environmental and operational conditions (EOC) that these structures are subject to. In spite of many favorable characteristics of guided-waves for NDE of pipes, their multi-modal, dispersive, and multi-path characteristics result in complex signals whose interpretation is a difficult task. Studies that have considered the effects of EOC variations either fail to reflect realistic EOC scenarios (e.g., limited to particular effects of specific EOCs, like time shifting effects of temperature in plates) or lack the necessary understanding of the effects of EOC variations on different aspects of the developed damage detection approaches. Such gaps limit the extensibility of these approaches to pipeline applications outside of controlled environments. This paper motivates the idea of analytically incorporating the effects of temperature and flow rate variations into damage diagnosis of pipes, through a number of case studies. A review of the existing literature on guided-wave based testing is also provided. For damage detection, a linear supervised classification method, namely linear discriminant analysis (LDA), is applied to experimental guided-wave data recorded from a hot water piping system under regular operation. Principal components, obtained through principal component analysis (PCA), and Fourier transforms of the signals are two sets of damage-sensitive features (DSF) that are examined for LDA-based classification. The effects of temperature and flow rate difference among testing and training datasets on (A) detection performance and (B) goodness of fit of the method to the data are investigated.

In this paper, we present a room-level building occupancy estimation system (BOES) utilizing low-resolution vibration sensors that are sparsely distributed. Many ubiquitous computing and building maintenance systems require fine-grained occupancy knowledge to enable occupant centric services and optimize space and energy utilization. The sensing infrastructure support for current occupancy estimation systems often requires multiple intrusive sensors per room, resulting in systems that are both costly to deploy and difficult to maintain. To address these shortcomings, we developed BOES. BOES utilizes sparse vibration sensors to track occupancy levels and activities. Our system has three major components. 1) It extracts features that distinguish occupant activities from noise prone ambient vibrations and detects human footsteps. 2) Using a sequence of footsteps, the system localizes and tracks individuals by observing changes in the sequences. It uses this tracking information to identify when an occupant leaves or enters a room. 3) The entering and leaving room information are combined with detected individual location information to update the room-level occupancy state of the building. Through validation experiments in two different buildings, our system was able to achieve 99.55% accuracy for event detection, less than three feet average error for localization, and 85% accuracy in occupancy counting.

A measurement method of the stress intensity factors defined by KIRCHHOFF’s theory for a crack in a bending plate is shown. For this purpose, a thin piezoelectric polyvinylidene fluoride film (PVDF) is attached to the surface of the cracked plate. The measured electrical voltages are coupled with the load type and the crack tip position relative to the sensor film. Stress intensity factors and the crack tip position can be determined by solving the non-linear inverse problem based on the measured signals. To guarantee solvability of the problem, more measuring electrodes on the film have to be taken in to account. To the developed sensor concept the KIRCHHOFF’s plate theory has been applied. In order to connect the electrical signals and the stress intensity factors the stresses near the crack tip have to be written in eigenfunctions (see WILLIAMS [1]). The presented method was verified by means of the example of a straight crack of the length 2a in an infinite isotropic plate under all- side bending. It was found that the positioning of the electrodes is delimited by two radii. On one hand, the measurement points should not be too close to the crack tip. In this area, the Kirchhoff’s plate theory cannot be used effectively. On the other hand, the measuring electrodes should be placed at a smaller distance to each other and not too far from the crack tip regarding the convergence radius of the WILLIAMS series expansion. Test calculations on a straight crack in an infinite isotropic plate showed the general applicability of the measurement method.

In crack detection applications large sensor arrays are needed to be able to detect and locate cracks in structures. This paper analyzes different sensor shapes and layouts to determine the layout which provides the optimal performance. A “snaked hexagon” layout is proposed as the optimal sensor layout when both crack detection and crack location parameters are considered. In previous work we have developed a crack detection circuit which reduces the number of channels of the system by placing several sensors onto a common bus line. This helps reduce data and power consumption requirements but reduces the robustness of the system by creating the possibility of losing sensing in several sensors by a single broken wire. In this paper, sensor bus configurations are analyzed to increase the robustness of the bused sensor system. Results show that spacing sensors in the same bus out as much as possible increases the robustness of the system and that at least 3 buses are needed to prevent large segments of a structure from losing sensing in the event of a bus failure.

Recently, the demand of the building spaces to respond to increase of single aged households and the diversification of life style is increasing. Smart house is one of them, but it is difficult for them to be changed and renovated. Therefore, we suggest Biofied builing. In biofied building, we use a mobile robot to get concious and unconcious information about residents and try to make it more secure and comfort builing spaces by realizing the intraction between residents and builing spaces. Walking parameters are one of the most important unconscious information about residents. They are an indicator of autonomy of elderly, and changes of stride length and walking speed may be pridictive of a future fall and a cognitive impairment. By observing their walking and informing residents their walking state, they can forestall such dangers and it helps them to live more securely and autonomously. Many methods to estimate walking parameters have been studied. The famous ones are to use accelerometers and a motion capture camera. Walking parameters estimated by them are high precise but the sensors are attached to a human body in these method and it can make human’s walk different from the original walk. Furthermore, some elderly feel it to invade them. In this work, Kinect which can get information about human untouchably was used on the mobile robot. A stride time, stride length, and walking speed were estimated from the back view of human by following him or her. Evaluation was done for 10m, 5m, 4m, and 3m in whole walking. As a result, the proposal system can estimate walking parameters of the walk more than 3m.

The introduction of wireless telemetry into the design of monitoring and control systems has been shown to reduce system costs while simplifying installations. To date, wireless nodes proposed for sensing and actuation in cyberphysical systems have been designed using microcontrollers with one computational pipeline (i.e., single-core microcontrollers). While concurrent code execution can be implemented on single-core microcontrollers, concurrency is emulated by splitting the pipeline’s resources to support multiple threads of code execution. For many applications, this approach to multi-threading is acceptable in terms of speed and function. However, some applications such as feedback controls demand deterministic timing of code execution and maximum computational throughput. For these applications, the adoption of multi-core processor architectures represents one effective solution. Multi-core microcontrollers have multiple computational pipelines that can execute embedded code in parallel and can be interrupted independent of one another. In this study, a new wireless platform named Martlet is introduced with a dual-core microcontroller adopted in its design. The dual-core microcontroller design allows Martlet to dedicate one core to standard wireless sensor operations while the other core is reserved for embedded data processing and real-time feedback control law execution. Another distinct feature of Martlet is a standardized hardware interface that allows specialized daughter boards (termed wing boards) to be interfaced to the Martlet baseboard. This extensibility opens opportunity to encapsulate specialized sensing and actuation functions in a wing board without altering the design of Martlet. In addition to describing the design of Martlet, a few example wings are detailed, along with experiments showing the Martlet’s ability to monitor and control physical systems such as wind turbines and buildings.

Many signals of interest in the assessment of structural systems lie in the quasi-static range (frequency << 1Hz). This poses a significant challenge for the development of self-powered sensors that are required not only to monitor these events but also to harvest the energy for sensing, computation and storage from the signal being monitored. This paper combines the use of mechanically-equivalent frequency modulators and piezo-powered threshold detection modules capable of computation and data storage with a total current less than 10nA. The system is able to achieve events counting for input deformations at frequencies lower than 0.1Hz. The used mechanically-equivalent frequency modulators allow the transformation of the low-amplitude and low-rate quasi-static deformations into an amplified input to a piezoelectric transducer. The sudden transitions in unstable mode branch switching, during the elastic postbuckling response of slender columns and plates, are used to generate high-rate deformations. Experimental results show that an oscillating semi-crystalline plastic polyvinylidene fluoride (PVDF), attached to the up-converting modules, is able to generate a harvestable energy at levels between 0.8μJ to 2μJ. In this work, we show that a linear injection response of our combined frequency up-converter / piezo-floating-gate sensing system can be used for self-powered measurement and recording of quasi-static deformations levels. The experimental results demonstrate that a sensor fabricated in a 0.5- μm CMOS technology can count and record the number of quasi-static input events, while operating at a power level significantly lower than 1μW.

Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

This paper presents the dynamic interrogation of a wireless antenna sensor for mechanical vibration monitoring. In order to interrogate the antenna resonant frequency at sufficient high speeds, a wireless interrogator that consists of a Frequency Modulated Continuous Wave (FMCW) synthesizer, a signal demodulation unit, and a real-time digital signal processing program was developed. The principle of operation of the dynamic wireless sensing system is first described, followed by the description of the design and implementation of the antenna sensor and the wireless interrogator. After calibrate the antenna sensor response using static tensile tests, dynamic interrogation of the wireless antenna sensor was carried out by subjecting the test specimen to a sinusoidal tensile load. The resonant frequency shifts of the antenna sensor were compared with the strains calculated from the applied loads. A good agreement between the antenna sensor readings and the strain values were achieved. A sampling rate of up to 50 Hz was demonstrated.

In the present work, wireless sensor network and smart real-time controlling and monitoring system are integrated for efficient energy management of standalone photovoltaic system. The proposed system has two main components namely the monitoring and controlling system and the wireless communication system. LabView software has been used in the implementation of the monitoring and controlling system. On the other hand, ZigBee wireless modules have been used to implement the wireless system. The main functions of monitoring and controlling unit is to efficiently control the energy consumption form the photovoltaic system based on accurate determination of the periods of times at which the loads are required to be operated. The wireless communication system send the data from the monitoring and controlling unit to the loads at which desired switching operations are performed. The wireless communication system also continuously feeds the monitoring and controlling unit with updated input data from the sensors and from the photovoltaic module send to calculate and record the generated, the consumed, and the stored energy to apply load switching saving schemes if necessary. It has to be mentioned that our proposed system is a low cost and low power system because and it is flexible to be upgraded to fulfill additional users’ requirements.

Strain measurements are essential in structural health monitoring. Traditional strain gages require physical contact between the sensor and read-out device, perturb the surface being monitored, and allow measurement only at the specific location and orientation axis of the sensor. We demonstrate a novel non-contact, multi- point, multi-directional strain sensing approach that overcomes these limitations. In our method, the surface is coated with a thin film of "smart skin" containing individualized single-walled carbon nanotubes in a polymeric host. After curing, substrate strains are transmitted through the polymer film to embedded nanotubes. This induces axial strains in the nanotubes, systematically shifting the wavelengths of their characteristic near-infrared fluorescence peaks. To measure strain, a visible laser excites nanotubes at points of interest on the surface, and the near-infrared emission is collected and spectrally analyzed. Observed spectral shifts reveal quantitative strain values. Laboratory tests show sensitivity down to ~400µm, limited by mechanical properties of the polymeric host film. We also vary excitation beam polarization to find the axis of substrate strain. Our method provides spatial resolution down to its gage length of ~100µm. Because the entire substrate is coated with nanoscale strain sensors, measurements can be made at arbitrary locations to construct a full strain map. We will describe recent smart skin refinements involving selection of polymer host, nanotube surfactant, nanotube dispersion method, and preparation protocol. Finally, we characterize the orientational distribution of nanotubes using a probabilistic model.

This research addresses the thermal transport in carbon nanofiber (CNF)/epoxy composites via finite element modeling. The effects of nanofiber orientation on thermal transport are investigated through Fourier’s Law of heat conduction and through simulation of a high magnitude, short heat pulse. The effect of interface thermal resistance on the effective composite thermal conductivity is also quantified. In addition, a simplified lightning strike simulation is modeled in order to analyze the effect of interfacial thermal resistance on composite behavior when subjected to multiple short heat pulses.

The technical challenges of managing the health of critical infrastructure systems necessitate greater structural sensing capabilities. Among these needs is the ability for quantitative, spatial damage detection on critical structural components. Advances in material science have now opened the door for novel and cost-effective spatial sensing solutions specially tailored for damage detection in structures. However, challenges remain before spatial damage detection can be realized. Some of the technical challenges include sensor installations and extensive signal processing requirements. This work addresses these challenges by developing a patterned carbon nanotube composite thin film sensor whose pattern has been optimized for measuring the spatial distribution of strain. The carbon nanotube-polymer nanocomposite sensing material is fabricated on a flexible polyimide substrate using a layer-by-layer deposition process. The thin film sensors are then patterned into sensing elements using optical lithography processes common to microelectromechanical systems (MEMS) technologies. The sensor array is designed as a series of sensing elements with varying width to provide insight on the limitations of such patterning and implications of pattern geometry on sensing signals. Once fabrication is complete, the substrate and attached sensor are epoxy bonded to a poly vinyl composite (PVC) bar that is then tested with a uniaxial, cyclic load pattern and mechanical response is characterized. The fabrication processes are then utilized on a larger-scale to develop and instrument a component-specific sensing skin in order to observe the strain distribution on the web of a steel beam. The instrumented beam is part of a larger steel beam-column connection with a concrete slab in composite action. The beam-column subassembly is laterally loaded and strain trends in the web are observed using the carbon nanotube composite sensing skin. The results are discussed in the context of understanding the properties of the thin film sensor and how it may be advanced toward structural sensing applications.

To accurately track the water stage changes and environmental influences on water quality and have sufficient follow-ups to prevent potential flood and environmental hazards, an in-situ continuous monitoring system for water stage and quality is helpful. Traditional in-situ water-stage monitoring system has relative low accuracy and require separate sensors for water quality and temperature which complex the evaluation. Advanced optical fiber sensors, with unique features of real-time and multiparameter sensing capacity, could provide potential simultaneous evaluation of flooding, water-quality deterioration, and science-based decision support. In this study, an in-situ monitoring system for water stage, overall water quality, and temperature is developed using long-period fiber grating (LPFG) sensors. When part of the LPFG sensor is submerged in water, the resonant wavelength of each cladding mode of the LPFG sensor varies linearly with the submerged length (refer to water stage) and temperature, and nonlinearly with refractive index changes (referred to the water quality). Two resonant wavelengths were used to relate the water stage and overall water quality to the change in resonant wavelengths of the LPFG sensor. Sharing the same fiber line, one sequential glass packaged LPFG was used to monitor the temperature changes at the same location. The algorithm for simultaneous determination of the water stage, overall water quality, and temperature was analytically formulated and experimentally validated. Benefit from the multi-parameter sensing using one single fiber line, the developed optical sensing network can potentially provide a low-cost, real-time, and high resolution solution for the in-situ water quality monitoring.

A novel cement composite containing graphene nanoplatelet (GNP) which can sense its own strain and damage is introduced in this paper. Piezoresistive strain sensing was investigated for mortar specimens with GNP under both cyclic and monotonically increasing compressive and tensile strain. Under compression, the electrical resistance decreased with increasing strain and the normalized resistance can be described by a bilinear curve with a kink at about 400 microstrain. At low strain, a high gauge factor exceeding 10³ in magnitude was obtained and it increased almost linearly with the GNP content. This can be attributed primarily to the reducing interfacial distance and forming of better contacts between GNP and cement paste when the composite was initially loaded. At higher compressive strain beyond 400 microstrain, the gauge factor is consistently about 10² for GNP content exceeding the percolation threshold. A different response was observed for specimens under tension due to the formation and propagation of microcracks even at low tensile strain due to the brittleness of the material. The initial gauge factor is of the order 10² for tensile strain up to 100 microstrain and it increases exponentially beyond that. The damage self-sensing capability of this conductive cement composites is explored using electric potential method. Closed form expression for the assessment of damage are derived based on the mathematical analogy between the electrostatic field and the elastostatic field under anti-plane shear loading. The derived expression provide a quick and accurate assessment of the damage of this conductive material which is characterized by its change in compliance.

The purpose of this study is to create and evaluate a smart composite structure that can be used for integrated load sensing and structural health monitoring. In this structure, PVDF films are used as the matrix material instead of epoxy resin or other thermoplastics. The reinforcements are two layers of carbon fiber with one layer of Kevlar separating them. Due to the electrical conductivity properties of carbon fiber and the dielectric effect of Kevlar, the structure acts as a capacitor. Furthermore, the piezoelectric properties of the PVDF matrix can be used to monitor the response of the structure under applied loads. In order to exploit the piezoelectric properties of PVDF, the PVDF material must be polarized to align the dipole moments of its crystalline structure. The optimal condition for poling the structure was found by performing a 2³ factorial design of experiment (DoE). The factors that were studied in DoE were temperature, voltage, and duration of poling. Finally, the response of the poled structure was monitored by exposing the samples to an applied load.

A humanoid robot hand has received significant attention in various fields of study. In terms of dexterous robot hand, slip detecting tactile sensor is essential to grasping objects safely. Moreover, slip sensor is useful in robotics and prosthetics to improve precise control during manipulation tasks. In this paper, sensor based-human biomimetic structure is fabricated. We reported a resistance tactile sensor that enables to detect a slip on the surface of sensor structure. The resistance slip sensor that the novel developed uses acrylonitrile-butadiene rubber (NBR) as a dielectric substrate and carbon particle as an electrode material. The presented sensor device in this paper has fingerprint-like structures that are similar with the role of the human’s finger print. It is possible to measure the slip as the structure of sensor makes a deformation and it changes the resistance through forming a new conductive route. To verify effectiveness of the proposed slip detection, experiment using prototype of resistance slip sensor is conducted with an algorithm to detect slip and slip was successfully detected. In this paper, we will discuss the slip detection properties so four sensor and detection principle.

The possibility of handling uncooperative objects, i.e. objects not equipped with any features that can aid their manipulation, is of particular interest for both terrestrial and space robotic applications. In this framework, this paper deals with the development and testing of a smart material substrate, which can be integrated into an end-effector device, where morphing and electro-adhesive capabilities are combined to allow the manipulation of uncooperative objects of different shapes and materials. Compliance and adhesion properties are obtained by creating a conductive pattern of electrodes embodied on the surface of a polymeric substrate. On one hand, the polymeric material, activated by a change in temperature, can adapt to any shape when it is heated, and maintain the deformed shape after being cooled, even when the load is removed, becoming compliant with the objects surface. On the other hand, the conductive pattern is responsible for the adhesive effect: when a high voltage is applied, the electric field generated induces an opposite charge on the objects surface establishing reversible attraction forces. Furthermore, the conductive pattern could be used to activate the morphing behaviour when the manipulator and the target object come into contact. A resistiveelectroadhesive pad is realized and some tests are performed to verify the heating behavior of the electrodes and the electroadhesion forces achievable. Morphing tests are also performed to verify the ability of the polymeric substrate to maintain the deformed shape after cooling.

The objective of this study was to validate modal analysis, system identification and damage detection of small-scale rotating wind turbine blades in the laboratory and in the field. Here, wind turbine blades were instrumented with accelerometers and strain gages, and data acquisition was achieved using a prototype wireless sensing system. In the first portion of this study conducted in the laboratory, sensors were installed onto metallic structural elements that were fabricated to be representative of an actual wind blade. In order to control the excitation (rotation of the wind blade), a motor was used to spin the blades at controlled angular velocities. The wind turbine was installed on a shaking table for testing under rotation of turbine blades. Data measured by the sensors were recorded while the blade was operated at different speeds. On the other hand, the second part of this study utilized a small-scale wind turbine system mounted on the rooftop of a building. The main difference, as compared to the lab tests, was that the field tests relied on actual wind excitations (as opposed to a controlled motor). The raw data from both tests were analyzed using signal processing and system identification techniques for deriving the model response of the blades. The multivariate singular spectrum analysis (MSSA) and covariance-driven stochastic subspace identification method (SSI-COV) were used to identify the dynamic characteristics of the system. Damage of one turbine blade (loose bolts connection) in the lab test was also conducted. The extracted modal properties for both undamaged and damage cases under different ambient or forced excitations (earthquake loading) were compared. These tests confirmed that dynamic characterization of rotating wind turbines was feasible, and the results will guide future monitoring studies planned for larger-scale systems.

This paper illustrates an application of Bayesian logic to monitoring data analysis and structural condition state inference. The case study is a 260 m long cable-stayed bridge spanning the Adige River 10 km north of the town of Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that long-term load redistribution between cables can be expected. To monitor load redistribution, the owner decided to install a monitoring system which combines built-on-site elasto-magnetic and fiber-optic sensors. In this note, we discuss a rational way to improve the accuracy of the load estimate from the EM sensors taking advantage of the FOS information. More specifically, we use a multi-sensor Bayesian data fusion approach which combines the information from the two sensing systems with the prior knowledge, including design information and the outcomes of laboratory calibration. Using the data acquired to date, we demonstrate that combining the two measurements allows a more accurate estimate of the cable load, to better than 50 kN.

Global assessment of structural conditions is important for structural health monitoring system. In particular, online or almost online structural parametric identification, based on vibration data measured from sensors, has received considerable attention recently. However, the problem becomes more challenging when the structure is complex and the number of degree-of-freedom (DOF) is large. A newly proposed time domain analysis methodology, referred to as the sequential nonlinear LSE (SNLSE) approach, has been studied and shown to be useful for the online tracking of parameters for structures with small DOFs. In this paper, the SNLSE approach will be applied for global assessment of an experimental cable-stay bridge model with large DOFs. A dynamic equivalent model of the bridge will be established and finite element analysis will be carried out to formulate the equation of motion. Numerical analysis will be conducted with different simulated damage scenarios and limited number of response data is considered. The capability of the proposed SNLSE approach in identifying the structural parameters and assessing the structural conditions will be verified.

The detection and localization of damage in a timely manner is critical in order to avoid the failure of structures. When a structure is subjected to an unscheduled impulsive force, the resulting damage can lead to failure in a very short period of time. As such, a monitoring strategy that can adapt to variability in the environment and that anticipates changes in physical processes has the potential of detecting, locating and mitigating damage. These requirements can be met by a cyber-physical system (CPS) equipped with Wireless Smart Sensor Network (WSSN) systems that is capable of measuring and analyzing dynamic responses in real time using on-board in network processing. The Eigenparameter Decomposition of Structural Flexibility Change (ED) Method is validated with real data and considered to be used in the computational core of this CPS. The condition screening is implemented on a damaged structure and compared to an original baseline calculation, hence providing a supervised learning environment. An experimental laboratory study on a 5-story shear building with three damage conditions subjected to an impulsive force has been chosen to validate the effectiveness of the method proposed to locate and quantify the extent of damage. A numerical simulation of the same building subject to band-limited white noise has also been developed with this purpose. The effectiveness of the ED Method to locate damage is compared to that of the Damage Index Method. With some modifications, the ED Method is capable of locating and quantifying damage satisfactorily in a shear building subject to a lower frequency content predominant excitation.

In this paper, a loop substructure identification method is proposed to estimate the parameters of any story in a shear structure with measurements of only limited number of acceleration floors and unknown structural mass. A shear structure is divided into substructures consisting of a series of similar two-story standard substructures; two identification problems are formulated for the standard substructure using the cross power spectral densities (CPSD) of structural responses, each of which identifies the parameters of one story given that the parameters of the other are known. A loop identification scheme is proposed by connecting the two identification problems in a loop manner, forming a sequence of estimation problems to directly identify both story parameters of the standard substructure. If the structural masses are unknown, this loop identification method can still be applied to estimate mass normalized structural parameters as well as the relative mass distribution of the structure. The convergence condition is derived for the loop substructure identification, showing that the loop identification sequence is conditionally converged and some structural responses play a crucial role in determining the convergence. To achieve convergent identification results, a reference selection method is proposed, which uses a synthesized response, formed by a linear combination of the measured structural responses, as the reference response to calculate the CPSD and perform the loop substructure identification. A 20-story shear building is used to verify the convergence condition and to demonstrate that the proposed reference selection method does provide the converged and accurate estimation results.

In this paper, new MEMS strain sensors are introduced. The transduction principle of the sensors is the resistance change due to piezoresistive property of polysilicon. Five different sensors are designed on the same device and tuned to resistance values of 350 Ω and 120 Ω. The sensors are aligned in horizontal, vertical and 45° directions in order to extract the principle strains. The geometry of the sensing element is a rectangular bar anchored at two ends and suspended above silicon substrate. The sensors are numerically modeled using COMSOL Multiphysics software. The model consists of all the micromachining layers, including silicon substrate, 0.7 μm thick polysilicon layer (sensing element) sandwiched between two layers of 0.35 μm thick silicon nitride layers and trenching under polysilicon layer, in order to estimate the strain that piezoresistive element is exposed to. The MEMS strain sensors are manufactured using MetalMUMPs process. The sensors are attached to aluminum and steel plates, and their gauge factors are compared with conventional foil gauges under uniaxial and biaxial loading. It is demonstrated that the MEMS strain sensors can detect both static and dynamic strains with the gauge factor reaching significantly high values. High gauge factor occurs because of unique geometry design and trenching, which amplify the strain that the polysilicon layer senses. The MEMS strain sensor can be fused with other sensing elements on the same device such as accelerometer, acoustic emission in order to have redundant measurement from a single point.

This paper describes a new mechanical implementation of a triaxial sensor, configurable as seismometer and/or as accelerometer, consisting of three one-dimensional monolithic FP sensors, suitably geometrically positioned. The triaxial sensor is, therefore, compact, light, scalable, tunable instrument (frequency < 100mHz), with large band (10⁻⁷ Hz − 10Hz), high quality factor (Q > 2500 in air) with good immunity to environmental noises, guaranteed by an integrated laser optical readout. The measured sensitivity curve is in very good agreement with the theoretical ones (10⁻¹²m/√Hz) in the band (0.1 ÷ 10Hz). Typical applications are in the field of earthquake engineering, geophysics, civil engineering and in all applications requiring large band-low frequency performances coupled with high sensitivities.

A cantilever beam-rigid body microgyroscope under electrostatic force is introduced and analytically modeled. The linear dynamics of the microsystem are studied in detail and the main parameters of the sensor are investigated. The square beam carries an eccentric end rigid body affecting the dynamic and static characteristics of the sensor. A detailed sensitivity analysis is performed and the effects of the base rotation rate, the excitation frequency, and the damping ratio (quality factor) on the dynamics of the system are explored.

Wireless passive temperature sensors are gaining increasing attention due to the ever-growing need of precise monitoring of temperature in high temperature energy conversion systems such as gas turbines and coal-based power plants. Unfortunately, the harsh environment such as high temperature and corrosive atmosphere present in these systems limits current solutions. In order to alleviate these issues, this paper presents the design, simulation, and manufacturing process of a low cost, passive, and wireless temperature sensor that can withstand high temperature and harsh environment. The temperature sensor was designed following the principle of metamaterials by utilizing Closed Ring Resonators (CRR) embedded in a dielectric matrix. The proposed wireless, passive temperature sensor behaves like an LC circuit that has a resonance frequency that depends on temperature. A full wave electromagnetic solver Ansys Ansoft HFSS was used to perform simulations to determine the optimum dimensions and geometry of the sensor unit. The sensor unit was prepared by conventional powder-binder compression method. Commercially available metal washers were used as CRR structures and Barium Titanate (BTO) was used as the dielectric materials. Response of the fabricated sensor at room temperature was analyzed using a pair of horn antenna connected with a network analyzer.

Microcantilever sensors have been widely used in designing force, strain and biochemical sensors. The fast-growing applications in nanoelectromechanical systems (NEMS) lead to strong demands to downsize the sensing elements to nanometer scale. In this paper, the detected environment on the performance of this photonic crystal sensor is investigated. The nanocavity, which can be used to localize the electromagnetic field in a low refractive index region, is a new sensing method to measure nano-scaled deformation. Through numerical simulation, we demonstrate that the range of the force sensor in each component force in X and Y directions are 0-1μN. In X direction, the minimum detectable applied forces are about 0.057μN and 0.070μN for the microcantilever operated in the water and air, respectively. And these in Y direction are 0.043μN and 0.053μN, respectively. Hence, it shows that a better resolution of applied force can be achieved in water than in air.

Damage detection technology needs improvement for aerospace engineering application because detection within complex composite structures is difficult yet critical to avoid catastrophic failure. Damage detection is challenging in aerospace structures because not all the damage detection technology can cover the various defect types (delamination, fiber fracture, matrix crack etc.), or conditions (visibility, crack length size, etc.). These defect states are expected to become even more complex with future introduction of novel composites including nano-/microparticle reinforcement. Currently, non-destructive evaluation (NDE) methods with X-ray, ultrasound, or eddy current have good resolutions (< 0.1 mm), but their detection capabilities is limited by defect locations and orientations and require massive inspection devices. System health monitoring (SHM) methods are often paired with NDE technologies to signal out sensed damage, but their data collection and analysis currently requires excessive wiring and complex signal analysis. Here, we present a capacitance sensor-based, structural defect detection technology with improved sensing capability. Thin dielectric polymer layer is integrated as part of the structure; the defect in the structure directly alters the sensing layer’s capacitance, allowing full-coverage sensing capability independent of defect size, orientation or location. In this work, capacitance-based sensing capability was experimentally demonstrated with a 2D sensing layer consisting of a dielectric layer sandwiched by electrodes. These sensing layers were applied on substrate surfaces. Surface indentation damage (~1mm diameter) and its location were detected through measured capacitance changes: 1 to 250 % depending on the substrates. The damage detection sensors are light weight, and they can be conformably coated and can be part of the composite structure. Therefore it is suitable for aerospace structures such as cryogenic tanks and rocket fairings for example. The sensors can also be operating in space and harsh environment such as high temperature and vacuum.

This paper proposes design optimization of a mechanically decoupled six-axis F/T sensor. In order to indicate the biggest cross coupling error of a Maltese cross type F/T six-axis sensor, principal error is proposed in this paper. Locations of twenty-four strain gages are determined and four design variables are selected to solve optimization problem. The average of principal couplings and output strain levels are chosen as the objective function and the constraints respectively. An effective optimization framework is suggested, which utilizes interaction between FEM software ANSYS and MATLAB by using morphing technique. As a result of optimization, the biggest coupling error is reduced from about 35% to 2.5%, which is satisfactory for use of mechanically decoupled six-axis F/T sensors. Experimental verification is conducted and it is shown that there is maximum 5.1 % difference in strain outputs of numerical and experimental results, which verifies the validity of suggested FE model. The design formulation and framework proposed in this study are expected to promote researches on multi-axis F/T sensors and their commercialization in various industries.

While the field measurement of wind speed at buildings and towers has been made by numerous investigators, the direct measurement of wind pressure at high-rise structures was seldom reported. Up to now, the information regarding wind pressure distribution above the maximum gradient wind level (it is 450 m stipulated in the Chinese code) has never been experimentally obtained. This paper presents a field monitoring investigation on the measurement of wind pressure and its distribution at the Canton Tower of 600 m high above the maximum gradient wind level during the typhoon Kaitak.

As part of a project to predict the full-field dynamic strain in rotating structures (e.g. wind turbines and helicopter blades), an experimental measurement was performed on a wind turbine attached to a 500-lb steel block and excited using a mechanical shaker. In this paper, the dynamic displacement of several optical targets mounted to a turbine placed in a semi-built-in configuration was measured by using three-dimensional point tracking. Using an expansion algorithm in conjunction with a finite element model of the blades, the measured displacements were expanded to all finite element degrees of freedom. The calculated displacements were applied to the finite element model to extract dynamic strain on the surface as well as within the interior points of the structure. To validate the technique for dynamic strain prediction, the physical strain at eight locations on the blades was measured during excitation using strain-gages. The expansion was performed by using both structural modes of an individual cantilevered blade and using modes of the entire structure (three-bladed wind turbine and the fixture) and the predicted strain was compared to the physical strain-gage measurements. The results demonstrate the ability of the technique to predict full-field dynamic strain from limited sets of measurements and can be used as a condition based monitoring tool to help provide damage prognosis of structures during operation.

With development of wireless sensor technology, wireless sensor network has shown a great potential for railway health monitoring. However, how to supply continuous power to the wireless sensor nodes is one of the critical issues in long-term full-scale deployment of the wireless smart sensors. Some energy harvesting methodologies have been available including solar, vibration, wind, etc; among them, vibration-based energy harvester using piezoelectric material showed the potential for converting ambient vibration energy to electric energy in railway health monitoring even for underground subway systems. However, the piezoelectric energy harvester has two major problems including that it could only generate small amount of energy, and that it should match the exact narrow band natural frequency with the excitation frequency. To overcome these problems, a wide band piezoelectric energy harvester, which could generate more power on various frequencies regions, has been designed and validated with experimental test. Then it was applied to a full-scale field test using actual railway train. The power generation of the wide band piezoelectric array has been compared to a narrow-band, resonant-based, piezoelectric energy harvester.

In this research, piezoelectric transducers are incorporated in an impedance-based damage detection approach for railway track health monitoring. The impedance-based damage detection approach utilizes the direct relationship between the mechanical impedance of the track and electrical impedance of the piezoelectric transducer bonded. The effect of damage is shown in the change of a healthy impedance curve to an altered, damaged curve. Using a normalized relative difference outlier analysis, the occurrences of various damages on the track are determined. Furthermore, the integration of inductive circuitry with the piezoelectric transducer is found to be able to considerably increase overall damage detection sensitivity.

Wireless sensor networks (WSN) have great potential to enable personalized intelligent lighting systems while reducing building energy use by 50%-70%. As a result WSN systems are being increasingly integrated in state-ofart intelligent lighting systems. In the future these systems will enable participation of lighting loads as ancillary services. However, such systems can be expensive to install and lack the plug-and-play quality necessary for user-friendly commissioning. In this paper we present an integrated system of wireless sensor platforms and modeling software to enable a↵ordable and user-friendly intelligent lighting. It requires ⇠ 60% fewer sensor deployments compared to current commercial systems. Reduction in sensor deployments has been achieved by optimally replacing the actual photo-sensors with real-time discrete predictive inverse models. Spatially sparse and clustered sub-hourly photo-sensor data captured by the WSN platforms are used to develop and validate a piece-wise linear regression of indoor light distribution. This deterministic data-driven model accounts for sky conditions and solar position. The optimal placement of photo-sensors is performed iteratively to achieve the best predictability of the light field desired for indoor lighting control. Using two weeks of daylight and artificial light training data acquired at the Sustainability Base at NASA Ames, the model was able to predict the light level at seven monitored workstations with 80%-95% accuracy. We estimate that 10% adoption of this intelligent wireless sensor system in commercial buildings could save 0.2-0.25 quads BTU of energy nationwide.

This research looks for appropriate colors of lighting depending on several kinds of feelings, covering not only colors which are on trajectory of a complete radiator but also other various colors, and aims to propose a control algorithm of lighting color of LED considering human's negative feelings. This research consists of three experiments. The first experiment aims to seek an appropriate color of lighting when we feel anger, sadness, happy and joy. The second experiment is to seek for reasons of results of the first experiment and to look for what kind of impression of lighting color leads to comfortable lighting environment. The third experiment aims to look for an applicable control of lighting colors when considering people's feelings. From these experiments, it was found that comfortable lighting color differ depending on feelings and the possibility of an effective use of colors which are not on the trajectory of a complete radiator was shown. Moreover, many elements which lead to comfortable lighting control were found, and as the controlling method which consider those elements was more preferred than the method which does not change colors at all, the usability of controlling lighting colors considering people's negative feelings was proved.

The comfortable space can be changed by season, age, physical condition and the like. However, the current systems are not able to resolve them absolutely. This research proposes the Homeostasis lighting control system based on the mechanism of biotic homeostasis for making the algorithms of apparatus control. Homeostasis are kept by the interaction of the three systems, endocrine system, immune system, and nervous system^[1]. By the gradual reaction in the endocrine system, body's protective response in the immune system, and the electrical reaction in the nerve system, we can keep the environments against variable changes. The new lighting control system utilizes this mechanism. Firstly, we focused on legibility and comfort in the office space to construct the control model learning from the endocrine and immune systems. The mechanism of the endocrine system is used for ambient lights in the space is used considering circadian rhythm for comfort. For the legibility, the immune system is used to control considering devices near the human depending on the distance between the human. Simulations and the demonstration were conducted to show the feasibility. Finally, the nerve system was intruded to enhance the system.

Wireless sensor networks (WSNs) technologies have attracted much attention to collect damage information in a natural disaster. WSNs to monitor temperature or humidity usually collect data once in some seconds or some minutes. Since structural health monitoring (SHM), meanwhile, aims to make a diagnosis for the state of a structure based on detected acceleration, WSNs are a promising technology to collect acceleration data. One concern to employ WSNs in SHM is to detect phenomena at a high sampling rate under energy-aware condition. In this paper, we describe a model for seismic acceleration monitoring, configured with multi-layer networks: WSNs, a wireless distribution system (WDS) and a database server, where the WDS is mainly operating in a wireless local area network (WLAN). Examining the performance in the test bed for the monitoring system, the results showed the system was capable of collecting acceleration at a rate of 100 sampling per second (sps) even in the fashion of intermittent operation, and capable of storing data into a database. We also suggest that the method using intermittent operation with appropriate sampling rate is effective in providing a long time operation for the system by considering in the response motion of a structure.

Structural health monitoring requires collection of large number sample data and sometimes high frequent vibration data for detecting the damage of structures. The expensive cost for collecting the data is a big challenge. The recent proposed Compressive Sensing method enables a potentially large reduction in the sampling, and it is a way to meet the challenge. The Compressed Sensing theory requires sparse signal, meaning that the signals can be well-approximated as a linear combination of just a few elements from a known discrete basis or dictionary. The signal of structure vibration can be decomposed into a few sinusoid linear combinations in the DFT domain. Unfortunately, in most cases, the frequencies of decomposed sinusoid are arbitrary in that domain, which may not lie precisely on the discrete DFT basis or dictionary. In this case, the signal will lost its sparsity, and that makes recovery performance degrades significantly. One way to improve the sparsity of the signal is to increase the size of the dictionary, but there exists a tradeoff: the closely-spaced DFT dictionary will increase the coherence between the elements in the dictionary, which in turn decreases recovery performance. In this work we introduce three approaches for arbitrary frequency signals recovery. The first approach is the continuous basis pursuit (CBP), which reconstructs a continuous basis by introducing interpolation steps. The second approach is a semidefinite programming (SDP), which searches the sparest signal on continuous basis without establish any dictionary, enabling a very high recovery precision. The third approach is spectral iterative hard threshold (SIHT), which is based on redundant DFT dictionary and a restricted union-of-subspaces signal model, inhibiting closely spaced sinusoids. The three approaches are studied by numerical simulation. Structure vibration signal is simulated by a finite element model, and compressed measurements of the signal are taken to perform signal recovery. Comparison of the performance of the three approaches is made, and future work on design of compressive sampling testing system for vibration signal is proposed.

A time-domain nonlinear system identification (SI)-based structural health assessment (SHA) procedure, using Unscented Kalman Filter (UKF) concept, is presented in this paper. It is a two-stage procedure. It integrates an iterative least squares technique and the unscented Kalman filter concept. The authors believe that the integrated procedure significantly improves the basic UKF concept. The procedure can assess the health of a structure using only a limited number of noise-contaminated acceleration time-histories measured only at a small part of a structure and does not need information on input excitation. The structures are represented by finite element models and the location and severity of defect(s) are assessed by tracking the changes in the stiffness properties of individual elements from their expected values. With the help of examples, it is demonstrated that the method is capable of accurately identifying defect-free and defective states of structures. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. It is demonstrated that the accuracy of the method is much better than the other methods currently available for the structural health assessment. It is also superior to the extended Kalman filter. Considering the accuracy and robustness, the procedure can be used as a nondestructive structural health assessment procedure.

In this paper, a fatigue damage detection system used for wind turbine blade is successfully developed by using highspatial- resolution differential pulse-width pair Brillouin optical time-domain analysis (DPP-BOTDA) sensing system. A piece of polarization-maintaining optical fiber is bonded on the blade surface to form the distributed sensing network. A DPP-BOTDA system, with a spatial resolution of 20cm and sampling interval of 1cm, is adopted to measuring distributed strain and detecting fatigue damage of wind turbine blade during fatigue test using the differential pulse pair of 39.5ns/41.5ns. Strain and the Brillouin gain spectra changes from undamaged state to fatigue failure are experimentally presented. The experimental results reveal that fatigue damage changes the strain distribution especially around the high strain area, and the width, amplitude and central frequency of the Brillouin gain spectra are sensitive to fatigue damage as the stiffness degradation and accumulated cracks change local strain gradient. As the damage becomes larger, the width of the Brillouin gain spectra becomes broader. Consequently, location and size of fatigue damage could be estimated. The developed system shows its potentiality for developing highly reliable wind turbine monitoring system as the effectiveness of damage detection and distributed sensing.

To better manage infrastructure assets as they reach the end of their service lives, quantitative data is required to better assess structural behavior and allow for more informed decision making. Distributed fiber optic strain sensors are one sensing technology that could provide comprehensive data for use in structural assessments as these systems potentially allow for strain to be measured with the same accuracy and gage lengths as conventional strain sensors. However, as with many sensor technologies, temperature can play an important role in terms of both the structure’s and sensor’s performance. To investigate this issue a fiber optic distributed strain sensor system was installed on a section of a two span reinforced concrete bridge on the TransCanada Highway. Strain data was acquired several times a day as well as over the course of several months to explore the effects of changing temperature on the data. The results show that the strain measurements are affected by the bridge behavior as a whole. The strain measurements due to temperature are compared to strain measurements that were taken during a load test on the bridge. The results show that even a small change in temperature can produce crack width and strain changes similar to those due to a fully loaded transport truck. Future directions for research in this area are outlined.

Giant Magnetostrictive Actuators (GMA) can be profitably used in application of vibration control on smart structures. In this field, the use of inertial actuators based on magnetostrictive materials has been consolidate. Such devices turn out to be very effective in applications of vibration control, since they can be easily paired with sensors able to ensure the feedback signal necessary to perform the control action. Unlike most widespread applications, this paper studies the use of patch magnetostrictive actuators. They are made of a sheet of magnetostrictive material, rigidly constrained to the structure, and wrapped in a solenoid whose purpose is to change the intensity of the magnetic field within the material itself. The challenge in the use of such devices resides in the impossibility of having co-located sensors. This limit may be exceeded by using strain gauge sensors to measure the deformation of the structure at the actuator. This work analyzes experimentally the opportunity of introducing, inside a composite material structure, both the conventional electric strain gauges and the less conventional optical sensors based on Bragg’s gratings. The performance of both solutions are analyzed with particular reference to the signal to noise ratio, the resolution of the sensors, the sensitivity to variations of the electric and magnetic fields and the temperature change associated with the operation of the actuator.

Increasing economic, political and ecological pressure leads to steadily rising percentage of modern processing and manufacturing processes for fibre reinforced polymers in industrial batch production. Component weights beneath a level achievable by classic construction materials, which lead to a reduced energy and cost balance during product lifetime, justify the higher fabrication costs. However, complex quality control and failure prediction slow down the substitution by composite materials. High-resolution fibre-optic sensors (FOS), due their low diameter, high measuring point density and simple handling, show a high applicability potential for an automated sensor-integration in manufacturing processes, and therefore the online monitoring of composite products manufactured in industrial scale. Integrated sensors can be used to monitor manufacturing processes, part tests as well as the component structure during product life cycle, which simplifies allows quality control during production and the optimization of single manufacturing processes.[1;2] Furthermore, detailed failure analyses lead to a enhanced understanding of failure processes appearing in composite materials. This leads to a lower wastrel number and products of a higher value and longer product life cycle, whereby costs, material and energy are saved. This work shows an automation approach for FOS-integration in the braiding process. For that purpose a braiding wheel has been supplemented with an appliance for automatic sensor application, which has been used to manufacture preforms of high-pressure composite vessels with FOS-networks integrated between the fibre layers. All following manufacturing processes (vacuum infiltration, curing) and component tests (quasi-static pressure test, programmed delamination) were monitored with the help of the integrated sensor networks. Keywords: SHM, high-pressure composite vessel, braiding, automated sensor integration, pressure test, quality control, optic-fibre sensors, Rayleigh, Luna Technologies

The paper reports on the design of surface acoustic wave strain sensing devices based on a Fourier approach. A patterned sensing surface acts as a narrow band filter for surface acoustic waves. The center frequencies vary as functions of the amount of strain present in the sensing area, and can be thus used to estimate the surface strain. The design of the sensing pattern in the Fourier space allows the selection of multiple sensing frequencies and for their shifting characteristics to be related to the three components of strain, i.e. two normal and one shear. The result is a surface acoustic wave strain gauge that acts as a strain rosette. The design procedure formulates the problem in the wavenumber domain, whereby the radiation characteristics of the sensing surface in response to an incident broadband surface wave are selected to ensure sensitivity to all three strain components. The concept is first illustrated for a one-dimensional pattern, whose radiation characteristics are governed by a simple, scalar, grating equation. The design approach is then extended to a two-dimensional pattern to demonstrate the ability to simultaneously measure all three surface strain components. The rosette-like operation of the considered strain sensor is demonstrated through numerical simulations conducted on an elastic surface subjected to a pre-imposed strain state. Eventually, the multi-band filtering properties of the proposed sensor patterning are evaluated experimentally by means of a Scanning Laser Doppler Vibrometer (SLDV).

This paper presents a single Lamb mode phased array beamforming by using a hybrid piezoelectric transducer (PZT)-scanning laser Doppler vibrometer (SLDV) system. The array system consists of a surface mounted PZT to generate Lamb waves and a non-contact SLDV to acquire high spatial resolution time-space wavefield remotely. The time-space wavefield contains Lamb waves which can be generated from the PZT excitation, damage scattering, mode conversion, etc. A frequency-wavenumber (f-k) decomposition technique is used to decompose the miscellaneous Lamb waves into individual wave mode components and wave propagations in different directions. The f-k decomposition allows using a single wave component as the phased array input for beamforming. The single mode array beamforming methodology was verified through PZT-SLDV experimental tests on an aluminum plate with a bonded quartz rod as a simulated damage

Acoustic radiation force is a physical phenomenon caused by propagation of ultrasound in an attenuating medium. When ultrasound propagates in the medium, the momentum of propagating ultrasound is transferred to the medium due to absorption mechanism. As a result, acoustic radiation force is generated in the principal direction of waves. By focusing the ultrasound at a specific location for a certain period, we can exert the acoustic radiation force at the location and generate the source of the shear waves. Characteristics of the shear wave critically depend on the material properties. Therefore, the shear wave propagation in the medium containing an inclusion shows differences compared to the wave in the pure medium. We simulate acoustic radiation force and generate shear waves by using the finite element method. The purpose of this study is to simulate the effect of the radiation force and to estimate the properties of the inclusion through analyzing the change of the shear wave induced by the radiation force in the almost incompressible materials.

The temperature dependent material characteristics of a layered panel are experimentally measured using a Thermal Mechanical Analysis (TMA) configuration. The temperature dependent wave dispersion characteristics of the panel are subsequently computed using a Wave Finite Element Method (WFEM). The WFEM predictions are eventually used within a wave context SEA approach in order to calculate the temperature dependent Sound Transmission Loss (STL) of the layered panel. Results on the STL for temperatures varying between -100 °C to 160 °C are computed for a structure operating at sea level. The importance of the glass transition region on the panel’s vibroacoustic response is exhibited and discussed.

Reinforced concrete (RC) is an economical, versatile and successful construction material as it can be moulded into a variety of shapes and finishes. In most cases, it is durable and strong, performing well throughout its service life. However, in some cases, it does not perform adequately due to various reasons, one of which is the corrosion of the embedded steel bars used as reinforcement. . Although the electro-mechanical impedance (EMI) technique is well established for damage detection and quantification of civil, mechanical and aerospace structures, only limited studies have been reported of its application for rebar corrosion detection in RC structures. This paper presents the recent trends in corrosion assessment based on the model derived from the equivalent structural parameters extracted from the impedance spectrum of concrete-rebar system using the lead zirconate titanate (PZT) sensors via EMI technique.

This study evaluates the damage potential of concrete of different mix designs subjected to cryogenic temperatures, using acoustic emission (AE) and permeability testing. The aim is to investigate design methodologies that might be employed to produce concrete that resists damage when cooled to cryogenic temperatures. Such concrete would be suitable for primary containment of liquefied natural gas (LNG) and could replace currently used 9% Ni steel, thereby leading to huge cost savings. In the experiments described, concrete cubes, 150 mm x 150 mm x 150 mm, were cast using four different mix designs. The four mixes employed siliceous river sand as fine aggregate. Moreover, limestone, sandstone, trap rock and lightweight aggregate were individually used as coarse aggregates in the mixes. The concrete samples were then cooled from room temperature (20°C) to cryogenic temperature (-165°C) in a temperature chamber. AE sensors were placed on the concrete cubes during the cryogenic freezing process. The damage potential was evaluated in terms of the growth of damage as determined from AE, as a function of temperature and concrete mixture design. The damage potential observed was validated with water permeability testing. Initial results demonstrate the effects of the coefficient of thermal expansion (CTE) of the aggregates on damage growth. Concrete damage (cracking) resistance generally decreased with increasing coarse aggregate CTE, and was in the order, limestone ≥ trap rock << lightweight aggregate ≥ sandstone. Work is in progress to fully understand thermal dilation and damage growth in concrete due to differential CTE of its components.

This paper concentrates on conductive composites with conductive fillers and their use for strain sensing based on piezoresistivity. Percolation curves of resistivity were summarized and validated as the basic for the design of such conductive composites-made piezoresistivity-based strain sensors, with percolation zoon and percolation slope as the key. A detailed comparison between cement-based and resin-based conductive composites with fibrous and particulate fillers were made and discussed in this work for further understanding the design of such sensors based on percolation curves. Magnetic field was introduced as an extrinsic factor for the design of the percolation curves and further for the design of such sensors’ piezoresistivity curves, which was proved to be quite an effective way. Concluded speaking, when making more full use of the designability of percolation curves, we can make better strain sensors used for structural health monitoring.

During the service life of structures, fatigue cracks may occur in structural components due to dynamic loadings acting on them, such as wind loads, live loads and ground motion. If undetected timely, these fatigue cracks may lead to a catastrophic failure of the overall structure. Although a number of approaches to detecting fatigue cracks have been proposed, some of them appear rather sophisticated or expensive (requiring complicated equipment), and others suffer from a lack of sensitivity. In this study, a simple approach to detecting fatigue cracks is developed based on the bilinear behavior of fatigue cracks. First, a simple system identification method for bilinear systems is proposed by using the dynamic characteristics of bilinear systems. This method transfers nonlinear system identification into linear system identification by dividing impulse or free-vibration responses into different parts corresponding to each stiffness region according to the stiffness interface. In this way, the natural frequency of each region can be identified using any modal identification approach applicable to linear systems. Second, the procedure for identifying the existence of breathing fatigue cracks and quantifying the cracks qualitatively is proposed by looking for the difference in the identified natural frequency between regions. The proposed system identification method and crack detection procedure have been successfully validated by numerical simulations.

In the authors’ previous work, an inductive substructure identification method was proposed for shear structures, which utilizes the frequency responses (Fourier transforms) of floor accelerations to formulate a series of inductive substructure identification problems, estimating the structural parameters from top to bottom iteratively. However, the simulation results show that the proposed method can only obtain relatively accurate results if measurement noise is not large. In order to improve the identification accuracy, an uncertainty analysis of the parameter identification errors is conducted for this method in this paper, revealing the important factors that influence the identification accuracy. Based on this result, a new substructure identification method is proposed herein, in which the cross power spectral densities (CPSDs) of structural responses, computed via multi-taper method, are utilized to formulate the substructure identification problems. A similar uncertainty analysis of the identification errors is carried out for the new method, illustrating why the new method could significantly improve the identification accuracy. Finally, a numerical example of 8-story shear building structure is utilized to verify the effectiveness of the new multi-taper based substructure method on enhancing the identification accuracy.

Because of the repeated earthquake and the problem such as the aging of buildings, a number of studies of Structural Health Monitoring (SHM) is have been done. Now, the writer is developing structural health assessment system for steel and reinforced concrete structures aiming for completion in 2014. In this system, following three programs for automatically estimating the physical quantity that is important for assessing the integrity of the structure are planned. First program is what automatically estimate the modal parameters (natural frequency and damping ratio) of the structure by the time history by using the subspace method. Second program is what automatically estimate the inter-layer parameters (stiffness and damping coefficient) by the time history by using the adaptive Kalman filter. Third program is what automatically estimate the story drift angle by time history by using the adaptive Kalman filter. The proposed method is expected to be estimated in consideration of the higher order modes than the conventional method by reverse modal analysis.

A process for the design and optimization of a morphing aileron using flexible matrix composite (FMC) actuators is developed. Design requirements were based on the use of a morphing aileron with an existing medium size commercial transport aircraft limiting the planform to the existing conventional aileron. The design uses two sets of contracting FMC actuators operating independently to actuate the deformable aileron in both trailing edge up and down cases while matching coefficient of lift values of a conventional aileron. The design was optimized by developing a series of response models which were then used to understand the sensitivity to material, geometric and loading design variables while minimizing required force from the FMC actuators. Using a combination of response models and sequential quadratic programming algorithm, a design is chosen that matched the conventional ailerons performance at multiple flight conditions.

This paper presents results from a comprehensive experimental program on medium-size and large-size fluid dampers in an effort to extract their force output during cyclic loading by simply measuring the strain on the damper housing and the end-spacer. The paper first discusses the stress path within the damper and subsequently via the use of linear elasticity shows that the experimental data obtained with commercially available strain gauges yield a force output of the damper that is in good agreement with the readings from the load cell. The paper then examines the performance of a portable data acquisition system that can be used to collect and transmit data from a damper installed on a bridge to a nearby location. The data show that the proposed arrangement is promising for monitoring in-situ the force output of fluid dampers and detecting possible loss of their energy dissipation capability.

The Cassini mission has revealed Titan to be one of the most Earthlike worlds in the Solar System complete with many of the same surface features including lakes, river channels, basins, and dunes. But unlike Earth, the materials and fluids on Titan are composed of cryogenic organic compounds with lakes of liquid methane and ethane. One of the potential mission concepts to explore Titan is to land a floating platform on one of the Titan Lakes and determine the local lake chemistry. In order to accomplish this within the expected mass volume and power budgets there is a need to pursue the development for a low power lightweight cryogenic valves which can be used along with vacuum lines to sample lake liquid and to distribute to various instruments aboard the Lander. To meet this need we have initiated the development of low power cryogenic valves and actuators based on a single crystal piezoelectric flextensional stacks produced by TRS ceramics Inc. Since the origin of such high electromechanical properties of Relaxor-PT single crystals is due to the polarization rotation effect, (i.e., intrinsic contributions), the strain per volt decrease at cryogenic temperatures is much lower than in standard Lead Zirconate Titanate (PZT) ceramics. This makes them promising candidates for cryogenic actuators with regards to the stroke for a given voltage. This paper will present our Titan Lake Sampling and Sample Handling system design and the development of small cryogenic piezoelectric valves developed to meet the system specifications.

Most NASA missions require the use of a launch lock for securing moving components during the launch or securing the payload before release. A launch lock is a device used to prevent unwanted motion and secure the controlled components. The current launch locks are based on pyrotechnic, electro mechanically or NiTi driven pin pullers and they are mostly one time use mechanisms that are usually bulky and involve a relatively high mass. Generally, the use of piezoelectric actuation provides high precession nanometer accuracy but it relies on friction to generate displacement. During launch, the generated vibrations can release the normal force between the actuator components allowing free motion of the shaft, which could result in damage to the actuated structures or instruments. This problem is common to other linear actuators that consist of a ball screw mechanism. The authors are exploring the development of a novel launch lock mechanism that is activated by a shape memory alloy (SMA) material ring, a rigid element and an SMA ring holding flexure. The proposed design and analytical model will be described and discussed in this paper.

This paper proposes a new active dynamic absorber control system for high-rise buildings using a neural oscillator and a map, which estimates the amplitude level of the oscillator, and shows some experimental results by using an apparatus, which realizes the proposed control algorithm. The proposed system decides the travel distance and direction of the auxiliary mass of the dynamic absorber using the output of oscillator, which is the filtering result of structure acceleration responses by the property of the oscillator, and Amplitude-Phase map (AP-map) for estimation of the structural response in specific frequency between synchronization region, and then, transfer the auxiliary mass to the predetermined location by using a position controller. In addition, the developed active dynamic absorber system is mounted on the top of the experimental single degree of freedom structure, which represents high-rise buildings, and consists of the auxiliary mass, a DC motor, a ball screw, a microcomputer, a laser displacement sensor, and an acceleration sensor. The proposed AP-map and the algorithm to determine the travel direction of the mass using the oscillator output are embedded in the microcomputer. This paper starts by illuminating the relation among subsystems of the proposed system with reference to a block diagram, and then, shows experimental responses of the whole system excited by earthquakes to confirm the validity of the proposed system.

Returning Martian samples to Earth for extensive analysis is of great interest to planetary science community. Current Mars sample return architecture would require leaving the acquired samples on Mars for several years before being retrieved by subsequent mission. Each sample would be sealed securely to keep its integrity. A reliable seal technique that does not affect the integrity of the samples and uses simple low-mass tool is required. The shape memory alloy (SMA) seal technique is a promising candidate. The performances of several primary designs of SMA seal for sample tubes were analyzed by finite element (FE) modeling. The results of thermal heating characteristics had been reported in a previous presentation this paper focus on the preparation and actuation of SMA plugs, the seal pressure, and the stress and strain induced in the sealing procedure with various designs.