This PDF file contains the front matter associated with SPIE Proceedings Volume 8692, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

The emerging concept of structural health management relies on extensive onboard diagnostic sensors that can provide near real-time information about the state of a structure so that informed prognostic assessment can be made of the continuing reliability of the structure. In this paper, we will discuss two types of sensing platforms that can provide valuable information about the state of a structure: 1D fiber-optic sensors and 2D thin-film sensors. Both fiber-optic and thin film sensors are easily integrated with structures, and can offer local and/or distributed sensing capabilities. Parameters that can be sensed include: static and dynamic strain, acoustic emission, vibration, corrosion products, moisture ingression etc. We will first describe some recent developments in dynamic strain sensing using optical fiber Bragg grating (FBG) sensors. Applications to detection of acoustic emission and impact will be described. In the area of chemical sensing, we will describe a nanofilm-coated photonic crystal fiber (PCF) long-period grating (LPG) sensing platform. PCF-LPG sensors can be designed to provide greater interaction between the analyte of interest and the light propagating in the fiber, thereby increasing the sensitivity of detection. Applications to humidity sensing will be described. Finally, 2D thin-film sensors on polymer substrates will be discussed. One type of sensor we have been fabricating is based on reduced graphene oxide for large-area chemical sensing applications. It is expected that these 1D and 2D sensing platforms will form part of a suite of sensors that can provide diagnostic structural health information.

The properties of highly magnetostictive materials, such as Terfenol-D, have opened the door to a wide variety of application possibilities. One such developing application is embedding magnetostictive particles (MSP) as sensors for determining the structural integrity of composite materials over the course of the operating life. The process of embedding these particles during the fabrication of the composite structure presents many challenges. This paper will briefly discuss and show the relationship between particle density and the output of a uni-axial induction based sensor. This relationship is critical for defining the goal of embedding process in this paper, to create a uniform uni-axial distribution of particles within the composite structure. Multiple methods of embedding magnetostrictive particles into a composite structure are detailed and then compared to determine their relative effectiveness. Methods included are: a simple by-hand spread of particles onto uncured prepreg composite, using the controlled adhesiveness of the prepreg to separate particles, applying the particles using a unidirectional application tool, introducing the particles into the epoxy mix to create a slurry during a VARTM layup, and spraying the particles onto a tacky composite surface during layup. Each method is used to embed particles into a composite beam or analog beam. That beam is then scanned with the uniaxial induction sensor to determine the effectiveness of the method. Results show promise for the adhesive method while the remaining processes show critical flaws.

Carbon fiber reinforced composites are used in a wide range of applications in aerospace, mechanical, and civil structures. Due to the nature of material, most damage in composites, such as delaminations, are always barely visible to the naked eye, which makes it difficult to detect and repair. The investigation of biological systems has inspired the development and characterization of self-healing composites. This paper presents the development of a new type of self-healing material in order to impede damage progression and conduct in-situ damage repair in composite structures. Carbon nanotubes, which are highly conductive materials, are mixed with shape memory polymer to develop self-healing capability. The developed polymeric material is applied to carbon fiber reinforced composites to automatically heal the delamination between different layers. The carbon fiber reinforced composite laminates are manufactured using high pressure molding techniques. Tensile loading is applied to double cantilever beam specimens using an MTS hydraulic test frame. A direct current power source is used to generate heat within the damaged area. The application of thermal energy leads to re-crosslinking in shape memory polymers. Experimental results showed that the developed composite materials are capable of healing the matrix cracks and delaminations in the bonded areas of the test specimens. The developed self-healing material has the potential to be used as a novel structural material in mechanical, civil, aerospace applications.

An electrically conductive polyaniline (PANi) membrane has been coated on the end of an optical fiber to form a fiber optoelectrode for simultaneously monitoring electrical and optical responses of the polymer membrane exposed to gas samples. Two electrodes were glued on the side of the optical fiber for measuring the membrane’s electrical resistance. In the same time a light beam was delivered to the membrane coated on the end of the optical fiber by using an 1x2 optical fiber coupler. The light reflected back from the PANi membrane to the optical fiber coupler was delivered with the second arm of the optical fiber coupler to an optical fiber compatible spectrometer. The responses of the fiber optoelectrode to trace ammonia and water vapor in air samples have been investigated. It was found that the electrical resistance of the fiber optoelectrode changes when exposed to ammonia or water vapor. However, the fiber optoelectrode showed optical spectrometric response to trace ammonia in air, but did not show optical spectrometric response to water vapor in air in the tested concentration range (relative humidity from <1% to 83%). The nature of electrical and optical spectrometric responses of this fiber optoelectrode gives an opportunity to design a sensor to simultaneously monitor both trace ammonia and relative humidity in air.

Cables are always a critical and vulnerable type of structural components in a long-span cable-stayed bridge in normal operation conditions. This paper presents the surface characteristics and mechanical performance of high-strength steel wires in simulated corrosive conditions. Four stress level (0MPa, 300MPa, 400MPa and 500MPa) steel wires were placed under nine different corrosive exposure periods based on the Salt Spray Test Standards ISO 9227:1990. The geometric feathers of the corroded steel wire surface were illustrated by using fractal dimension analysis. The mechanical performance index including yielding strength, ultimate strength and elastic modulus at different periods and stress levels were tested. The uniform and pitting corrosion depth prediction model, strength degradation prediction model as well as the relationship between strength degradation probability distribution and corrosion crack depth would be established in this study.

Detect and locate the structural damage from direct measurements can be done only when the sensors are very closely located to the damage initiating point, which is generally impossible to predict, particularly for the reinforced concrete structures. With the availability of high resolution distributed sensing, using optical tracker on light targets, the damage location as well as the level of damage can be identified. The objective of this paper is to conduct structural system identification of reinforced concrete frame by using the proposed structural integrity index (identify element curvature and null-space damage index) and the estimation of finite element strain. Finally, discussion on the identified time-varying system natural frequency and stiffness/strength degradation of the reinforced concrete structure from global measurement in relating to the calculated structural integrity index using optical sensing array data and element strain on the identification of damage location and damage severity are presented.

Reinforced Concrete (RC) has been widely used in construction of infrastructures for many decades. The cracking behavior in concrete is crucial due to the harmful effects on structural performance such as serviceability and durability requirements. In general, in loading such structures until failure, tensile cracks develop at the initial stages of loading, while shear cracks dominate later. Therefore, monitoring the cracking modes is of paramount importance as it can lead to the prediction of the structural performance. In the past two decades, significant efforts have been made toward the development of automated structural health monitoring (SHM) systems. Among them, a technique that shows promises for monitoring RC structures is the acoustic emission (AE). This paper introduces a novel probabilistic approach based on Gaussian Mixture Modeling (GMM) to classify AE signals related to each crack mode. The system provides an early warning by recognizing nucleation of numerous critical shear cracks. The algorithm is validated through an experimental study on a full-scale reinforced concrete shear wall subjected to a reversed cyclic loading. A modified conventional classification scheme and a new criterion for crack classification are also proposed.

The purpose of this study is the remote structural health monitoring to identify the torsional natural frequencies and mode shapes of a concrete cable-stayed bridge using a hybrid networking sensing system. The system consists of one data aggregation unit, which is daisy-chained to one or more sensing nodes. A wireless interface is used between the data aggregation units, whereas a wired interface is used between a data aggregation unit and the sensing nodes. Each sensing node is equipped with high-precision MEMS accelerometers with adjustable sampling frequency from 0.2 Hz to 1.2 kHz. The entire system was installed inside the reinforced concrete box-girder deck of Hwamyung Bridge, which is a cable stayed bridge in Busan, South Korea, to protect the system from the harsh environmental conditions. This deployment makes wireless communication a challenge due to the signal losses and the high levels of attenuation. To address these issues, the concept of hybrid networking system is introduced with the efficient local power distribution technique. The theoretical communication range of Wi-Fi is 100m. However, inside the concrete girder, the peer to peer wireless communication cannot exceed about 20m. The distance is further reduced by the line of sight between the antennas. However, the wired daisy-chained connection between sensing nodes is useful because the data aggregation unit can be placed in the optimal location for transmission. To overcome the limitation of the wireless communication range, we adopt a high-gain antenna that extends the wireless communication distance to 50m. Additional help is given by the multi-hopping data communication protocol. The 4G modem, which allows remote access to the system, is the only component exposed to the external environment.

Due to the heterogeneous nature of the cement-based materials, the ultrasonic waves in concrete exhibit highly scattering and attenuation, leading to the difficulty of concrete damaged detection. This paper presents a dual mode ultrasonic array imaging methodology that can map damage using Rayleigh surface waves and permanently installed piezoelectric sensors. The dual mode sensing integrates passive acoustic emission and active ultrasonic wave inspection. When a crack is developing, acoustic emission (AE) occurs and the disturbance can propagate outwards along the structure surface. A novel AE source imaging algorithm has been developed to detect and locate the AE source. Once the AE source is located, the sensor array switches to its active mode. For active sensing, one sensor in the array is used to generate Rayleigh wave for interrogation, while all the others are used as the wave receivers. All the sensory data are processed by the active ultrasonic array imaging algorithm. The proof-of-concept testing was performed on a grout specimen with representative dimensions. The passive array imaging algorithm was able to locate the AE source simulated by pencil lead break while active sensing imaging was able to detect the damage simulated by a hole. The duel mode imaging method is promising and economically beneficial for solving a key source localization problem in damage detection on large concrete structures.

A structural health monitoring system that aims to extract local damage information (i.e., existence, location and severity) in buildings may require a dense array of transducers due to the high complexity and high degree of statical indeterminacy of their structural system. While monitoring systems for building applications are mostly consisted of seismographs or tremor sensors, a technique to pragmatically and accurately capture strain information of structural members is efficacious for detecting damage in individual members. This paper presents the use of polyvinylidene fluoride piezoelectric films as dynamic strain sensors for detecting local damage in steel moment-resisting frames. First, a damage detection methodology that monitors the changes in the relative distribution of the bending moments in structural systems is presented. Next, an array of dynamic strain sensors networked by wireless sensing units is developed in consideration of its installation cost and efforts when it is applied to real buildings. Finally, the performances of the developed methodology and its sensing system are evaluated through a series of vibration testing using a 5-story steel testbed frame that can simulate seismic damage at beam-to-column connections.

Earthquakes have the potential to cause large-scale destruction of civil infrastructure often leading to significant economic losses or even the loss of human life. Therefore, it is vital to protect civil infrastructure during these events. Structural vibration control provides a method for mitigating the damage to civil infrastructure during earthquakes by absorbing seismic energy from the structure. Semi-active control has emerged as an attractive form of structural control due to its effectiveness, inherent stability, and reliability. One semi-active control device particularly effective in reducing the response of civil structures subject to near-field earthquakes is the resetting semi-active stiffness damper (RSASD). Substantial research has been conducted to develop the RSASD and demonstrate its control performance. However, like other semi-active control technologies, the RSASD relies on a multi-component feedback control system that is subject to reliability issues. The purpose of the proposed research is to develop a novel resettable stiffness system that is capable of achieving a similar control performance to the RSASD, but with fewer feedback components. The resulting device, the resetting semi-passive stiffness damper (RSPSD), will offer increased reliability without compromising effectiveness. The objective of the present work is to present the concept for the RSPSD, develop a mathematical model describing its resetting, identify critical design parameters, and then evaluate its control performance for single-degree-of-freedom structures subject to an earthquake ground motion. Numerical results indicate that the RSPSD is capable of comparable control performance to the RSASD for the structures and earthquake ground motion considered.

Base isolation is the most popular seismic protection technique for civil engineering structures. However, research has revealed that the traditional base isolation system due to its passive nature is vulnerable to two kinds of earthquakes, i.e. the near-fault and far-fault earthquakes. A great deal of effort has been dedicated to improve the performance of the traditional base isolation system for these two types of earthquakes. This paper presents a recent research breakthrough on the development of a novel adaptive seismic isolation system as the quest for ultimate protection for civil structures, utilizing the field-dependent property of the magnetorheological elastomer (MRE). A novel adaptive seismic isolator was developed as the key element to form smart seismic isolation system. The novel isolator contains unique laminated structure of steel and MR elastomer layers, which enable its large-scale civil engineering applications, and a solenoid to provide sufficient and uniform magnetic field for energizing the field-dependent property of MR elastomers. With the controllable shear modulus/damping of the MR elastomer, the developed adaptive seismic isolator possesses a controllable lateral stiffness while maintaining adequate vertical loading capacity. In this paper, a comprehensive review on the development of the adaptive seismic isolator is present including designs, analysis and testing of two prototypical adaptive seismic isolators utilizing two different MRE materials. Experimental results show that the first prototypical MRE seismic isolator can provide stiffness increase up to 37.49%, while the second prototypical MRE seismic isolator provides amazing increase of lateral stiffness up to1630%. Such range of increase of the controllable stiffness of the seismic isolator makes it highly practical for developing new adaptive base isolation system utilizing either semi-active or smart passive controls.

This paper describes recent collaborative efforts made by the United States Geological Survey and Department of Veterans Affairs (VA) in real-time seismic monitoring of VA hospital buildings located in seismically active regions. The instrumentation in each building encompasses accelerometers deployed on all floors, a multi-channel recorder, and a server to analyze and archive the building’s dynamic response in real-time. The server runs advanced structural health monitoring software, which consists of several data processing and analysis modules. Four different algorithms are implemented in four separate modules to compute shear-wave travel time, modal parameters, base shear force, and inter-story drift ratio from the measured vibration data from the instrumented building. The performance level and damage state of the building are estimated from the inter-story drift ratio and base-shear; the change in modal parameters and wave travel time is also used to detect and locate any possible damage zone(s) in the building. These algorithms are validated and verified using data from full-scale shake table tests. The information obtained from the real-time seismic monitoring system can be used to support timely decisions regarding the structural integrity of the VA hospital buildings immediately after an earthquake, and to help with inspections and necessary repairs and replacements.

Quick and reliable assessment of the condition of bridges in a transportation network after an earthquake can greatly assist immediate post-disaster response and long-term recovery. However, experience shows that available resources, such as qualified inspectors and engineers, will typically be stretched for such tasks. Structural health monitoring (SHM) systems can therefore make a real difference in this context. SHM, however, needs to be deployed in a strategic manner and integrated into the overall disaster response plans and actions to maximize its benefits. This study presents, in its first part, a framework of how this can be achieved. Since it will not be feasible, or indeed necessary, to use SHM on every bridge, it is necessary to prioritize bridges within individual networks for SHM deployment. A methodology for such prioritization based on structural and geotechnical seismic risks affecting bridges and their importance within a network is proposed in the second part. An example using the methodology application to selected bridges in the medium-sized transportation network of Wellington, New Zealand is provided. The third part of the paper is concerned with using monitoring data for quick assessment of bridge condition and damage after an earthquake. Depending on the bridge risk profile, it is envisaged that data will be obtained from either local or national seismic monitoring arrays or SHM systems installed on bridges. A method using artificial neural networks is proposed for using data from a seismic array to infer key ground motion parameters at an arbitrary bridges site. The methodology is applied to seismic data collected in Christchurch, New Zealand. Finally, how such ground motion parameters can be used in bridge damage and condition assessment is outlined.

Formation of ettringite and gypsum from sulfate attack together with carbonation and chloride ingress have been considered as the most serious deterioration mechanisms of concrete structures. Although Electrical Resistance Sensors and Fibre Optic Chemical Sensors could be used to monitoring the latter two mechanisms in situ, currently there is no system for monitoring the deterioration mechanisms of sulfate attack and hence still needs to be developed. In this paper, a preliminary study was carried out to investigate the feasibility of monitoring the sulfate attack with optical fibre Raman spectroscopy through characterizing the ettringite and gypsum formed in deteriorated cementitious materials under an ‘optical fibre excitation + spectroscopy objective collection’ configuration. Bench-mounted Raman spectroscopy analysis was also used to validate the spectrum obtained from the fibre-objective configuration. The results showed that the expected Raman bands of ettringite and gypsum in the sulfate attacked cement paste have been clearly identified by the optical fibre Raman spectroscopy and are in good agreement with those identified from bench-mounted Raman spectroscopy. Therefore, based on these preliminary results, there is a good potential of developing an optical fibre Raman spectroscopy-based system for monitoring the deterioration mechanisms of concrete subjected to the sulfate attack in the future.

This paper presents an AE source localization technique using near-field acoustic emission (AE) signals induced by crack growth and propagation. The proposed AE source localization technique is based on the phase difference in the AE signals measured by two identical AE sensing elements spaced apart at a pre-specified distance. This phase difference results in canceling-out of certain frequency contents of signals, which can be related to AE source direction. Experimental data from simulated AE source such as pencil breaks was used along with analytical results from moment tensor analysis. It is observed that the theoretical predictions, numerical simulations and the experimental test results are in good agreement. Real data from field monitoring of an existing fatigue crack on a bridge was also used to test this system. Results show that the proposed method is fairly effective in determining the AE source direction in thick plates commonly encountered in civil engineering structures.

Magnetic transducers have been applied in a number of diverse fields including non-contact measurement, active damping and non-destructive evaluation. Recently, due to the magneto-mechanical coupling characteristics between a magnetic transducer and the underneath metallic structure, such type of transducers is employed in impedance-based damage detection schemes, which can facilitate damage detection in a non-contact manner, and have potential advantages in monitoring structures with complicated geometries and boundaries. In this research, we formulate detailed first-principle-based modeling of the magnetic impedance transducer. In particular, we focus on accurately modeling the coupling between the impedance of the magnetic transducer and the electrical and structural impedance of the host metallic structure. The modeling and analysis are validated by experimental studies.

A curvature sensor based on flexoelectricity using Ba0.64Sr0.36TiO₃ (BST) material is proposed and developed in this paper. The working principle of the sensor is based on the flexoelectricity, exhibiting coupling between mechanical strain gradient and electric polarization. A BST curvature sensor is lab prepared using a conventional solid state processing method. The curvature sensing is demonstrated in four point bending tests of the beam under harmonic loads. BST sensors are attached on both side surfaces of an aluminum beam, located symmetrically with respect to its neutral axis. Analyses have shown that the epoxy bonding layer plays a critical role for curvature transfer. Consequently a shear lag effect is taken into account for extracting actual curvature from the sensor measurement. Experimental results demonstrated good linearity from the charge outputs under the frequencies tests and showed a sensor sensitivity of 30.78pC•m in comparison with 32.48pC•m from theoretical prediction. The BST sensor provides a direct curvature measure instead of using traditional strain gage through interpolation and may offer an optional avenue for on-line and in-situ structural health monitoring.

Electromagnetic motors can have problems when operating in extreme environments. In addition, if one needs to do mechanical work outside a structure, electrical feedthroughs are required to transport the electric power to drive the motor. In this paper, we present designs for driving rotary and linear motors by pumping stress waves across a structure or barrier. We accomplish this by designing a piezoelectric actuator on one side of the structure and a resonance structure that is matched to the piezoelectric resonance of the actuator on the other side. Typically, piezoelectric motors can be designed with high torques and lower speeds without the need for gears. One can also use other actuation materials such as electrostrictive, or magnetostrictive materials in a benign environment and transmit the power in acoustic form as a stress wave and actuate mechanisms that are external to the benign environment. This technology removes the need to perforate a structure and allows work to be done directly on the other side of a structure without the use of electrical feedthroughs, which can weaken the structure, pipe, or vessel. Acoustic energy is pumped as a stress wave at a set frequency or range of frequencies to produce rotary or linear motion in a structure. This method of transferring useful mechanical work across solid barriers by pumping acoustic energy through a resonant structure features the ability to transfer work (rotary or linear motion) across pressure or thermal barriers, or in a sterile environment, without generating contaminants. Reflectors in the wall of barriers can be designed to enhance the efficiency of the energy/power transmission. The method features the ability to produce a bi-directional driving mechanism using higher-mode resonances. There are a variety of applications where the presence of a motor is complicated by thermal or chemical environments that would be hostile to the motor components and reduce life and, in some instances, not be feasible. A variety of designs that have been designed, fabricated and tested will be presented.

In a previous paper an algorithm was developed for estimating the slope at locations of ambient vibration measurements along the deformed shape of a single column subjected to strong earthquake motion. These slope estimates were used in obtaining permanent drift values for the single column and those were correlated to various levels of damage. The algorithm was illustrated with applications to single column tests performed at the University of Nevada, Reno and the University of California, Berkeley. In this paper, the same columns are first used to simulate slope values along the deformed shape of the columns in order to determine the best estimate of the displacement distribution. This information can then be used to determine the optimal number and location of sensors needed to provide a reliable permanent drift in a single column. Sensitivity studies are also performed to evaluate the effect of plastic hinge length over or underestimate as this value is usually inferred from empirical equations. The rotation algorithm is then extended to estimate residual drift from rotation measurements taken from multiple sensors in order to correlate them to structural damage. The resulting drift values can then be related to various damage states and can be used for rapid damage assessment immediately following a major earthquake. The main advantage of the proposed approach is that low-cost accelerometers can be used to obtain the information needed for rapid damage assessment.

This paper introduces two forecasting methods for building energy consumption data that are recorded from smart meters in high resolution. For utility companies, it is important to reliably forecast the aggregate consumption profile to determine energy supply for the next day and prevent any crisis. The proposed methods involve forecasting individual load on the basis of their measurement history and weather data without using complicated models of building system. The first method is most efficient for a very short-term prediction, such as the prediction period of one hour, and uses a simple adaptive time-series model. For a longer-term prediction, a nonparametric Gaussian process has been applied to forecast the load profiles and their uncertainty bounds to predict a day-ahead. These methods are computationally simple and adaptive and thus suitable for analyzing a large set of data whose pattern changes over the time. These forecasting methods are applied to several sets of building energy consumption data for lighting and heating-ventilation-air-conditioning (HVAC) systems collected from a campus building at Stanford University. The measurements are collected every minute, and corresponding weather data are provided hourly. The results show that the proposed algorithms can predict those energy consumption data with high accuracy.

The multi-state progressive damage of a structure is introduced into the change point detection problem: a generalized Hidden Markov Model (HMM) with multiple states is developed. Applications to Structural Health Monitoring (SHM) are explored. Demonstrate development of suitable likelihood ratio for simple progressive damage case, as well as generalized to arbitrary HMM. Computational costs are limited by exploiting structure of this model, and further limited by “windowing” technique. Demonstrations of algorithm are shown, with use of multi-damage state response from a structural frame in a test site.

Damage detection on engineered systems is a challenging task that has been explored by numerous researchers. In recent years wireless sensors systems have arisen as a vehicle for low-power, low-cost, and localized damage detection that can be applied to various structural systems. Such sensors, however, are limited in their computational capacity and as a result, careful consideration must be taken as to which algorithms can be effectively embedded so as to balance energy constraints with computational efficiency. In this study, two classifier algorithms (least squares classifier and Fisher's linear discriminant analysis) are explored for detecting damage on a cooling system test bed. In particular, the algorithms are used to determine the valve configuration of the system and to verify if damage exists within the valves. To validate the efficiency of the algorithms in the embedded domain, the algorithms are implemented on a wireless sensing network and used to classify the system state of the test bed.

System identification algorithms currently require a full data set, i.e., no missing observations, to estimate the natural vibration properties of a structural system. These algorithms are often based on parameters estimated from a state-space model. There are circumstances in which a Missing Data Problem can arise during data collection; therefore, it is important to adjust these algorithms to facilitate Structural Modal Identification. Despite having missing observations, state-space parameters can be estimated for a time series; subsequently, structural modal properties can be identified. This paper will use the EM algorithm to identify structural modal properties from a data set with missing observations. The end of the paper will focus on the search for a missingness threshold which can be used to assess the probability of extracting useful structural modal properties from a given data set with missing observations. This assessment will be based on the accuracy of modal estimates for data sets with varying magnitudes and patterns of missingness. It is clear that missingness can only reduce the accuracy of modal estimates; however, it is important to establish the associated scale and behavior of the reduction. An example is presented to illustrate the main concepts of this approach.

In recent years, the concept of Nonlinear Structural Intensity (NSI) has been applied to detect fatigue cracks and loose joints in isotropic structures. This paper extends the NSI concept to orthotropic and anisotropic materials and investigates the possibility to use NSI for the localization of a closing delamination in thin laminated plates. When the delamination is excited by a high frequency interrogation signal, the periodic contact occurring between the delaminated plies produces Contact Acoustic Nonlinear (CAN) effects that are associated with the generation of both higher order and fractional harmonics. The closing delamination acts as a mechanism of redistribution of energy from the driving frequency to the nonlinear harmonics. The structural intensity associated with the nonlinear harmonics is an effective metric to identify size and location of the damage. NSI is computed using a combined approach based on a Finite Element (FE) model and a 13 point finite differencing scheme. Using this approach, we performed a numerical investigation on a thin laminated plate to analyze the effect that the material orthotropy has on the propagation of vibration energy and to understand the impact that preferential directions of energy propagation have on the ability to interrogate the damage. Then, the approach is extended for application to an anisotropic symmetric laminated plate.

Composites manufactured with networks of carbon nanotubes (CNTs) exhibit piezoresistivity. These composites have tremendous potential for integrated and high sensitivity strain sensing. However, modeling CNT composite piezoresistivity remains a challenge. Prevailing approaches rely on resistor network and fiber reorientation models but are computationally burdensome due to the extreme number of CNTs required to form a percolated network. This research circumvents that limitation by developing an analytical model for CNT composite piezoresistivity in tension. The accuracy of this model is verified by favorable comparison to experimental results in existing literature.

In this paper, new MEMS Acoustic Emission (AE) sensors are introduced. The transduction principle of the sensors is capacitance due to gap change. The sensors are numerically modeled using COMSOL Multiphysics software in order to estimate the resonant frequencies and capacitance values, and manufactured using MetalMUMPS process. The process includes thick metal layer (20 μm) made of nickel for freely vibration layer and polysilicon layer as the stationary layer. The metal layer provides a relatively heavy mass so that the spring constant can be designed high for low frequency sensor designs in order to increase the collapse voltage level (proportional to the stiffness), which increases the sensor sensitivity. An insulator layer is deposited between stationary layer and freely vibration layer, which significantly reduces the potential of stiction as a failure mode. As conventional AE sensors made of piezoelectric materials cannot be designed for low frequencies (<300 kHz) with miniature size, the MEMS sensor frequencies are tuned to 50 kHz and 200 kHz. The each sensor contained several parallel-connected cells with an overall size of approximately 250μm × 500 μm. The electromechanical characterizations are performed using high precision impedance analyzer and compared with the numerical results, which indicate a good fit. The initial mechanical characterization tests in atmospheric pressure are conducted using pencil lead break simulations. The proper sensor design reduces the squeeze film damping so that it does not require any vacuum packaging. The MEMS sensor responses are compared with similar frequency piezoelectric AE sensors.

Studies on the role of body flexibility in propulsion suggest that fish have the ability to control or modulate the stiffness of the fin for optimized propulsive performance. Fins with certain stiffness might be efficient for a particular set of operating parameters but may be inefficient for other parameters. Therefore active stiffness modulation of a fin can improve the propulsive performance for a range of operating conditions. This paper discusses the preliminary experimental work on the open loop active deformation control of heaving flexible fins using Macro Fiber Composites (MFCs). The effect of important parameters such as oscillation frequency, flexibility of the fin, applied voltage and the phase difference between applied voltage and heaving on propulsive performance are studied and reported. The results indicate that propulsive performance can be improved by active control of the fins. The mean thrust improved by 30- 38% for the fins used in the experiments. The phase difference of ~90° is found to be optimal for maximized propulsive performance for the parameters considered in the study. Furthermore, there exists an optimal voltage magnitude at which the propulsive performance is a maximum for the range of operating conditions.

Fiber-optic gas sensing techniques are commonly based on the recognition of a wide range of chemical species from characteristic absorption, fluorescence or Raman-scattering spectra signatures. By tuning over the vibrational lines of species in the path of laser beam, tunable diode laser gas sensors measure signal spectroscopic intensity, gas concentration, and other properties. However, they have limitations of bulk architecture, small change of signal on top of large background, and low sensitivity of direct absorption. Here we report the fabrication and optical measurements of tunable Er-doped fiber ring laser absorption spectroscopic sensor featuring a gas cell that is a segment of photonic crystal fiber (PCF) with a long-period grating (LPG) inscribed. The tunable laser beam is coupled into the cladding of the PCF by the LPG where the gas in air holes absorbs light. The light travels along the PCF cladding and reflects at the end of the fiber where a silver film is coated as a mirror at one end facet. The light propagates back within cladding and passes through the gas one more time thus increasing the interaction length. This light is finally recoupled into the fiber core for intensity measurement. The proposed fiber gas sensors have been experimentally used for ammonia (NH₃) concentration detection. They show excellent sensitivity and selectivity, and are minimally affected by temperature and/or humidity changes. The sensors using PCF-LPG gas cell are simple to fabricate, cost-effective, and are deployed for a variety of applications not possible using conventional optical fibers.

In this study, combined effects of magnetic fields and applied mechanical compression load on the electrical resistivity behavior of magnetorheological elastomers (MREs) is studied. MREs with different particle volume percentages are prepared and tested under constant temperature. The theoretical study was performed using a finite element analysis to understand MRE’s deformation subjected to a magnetic field. The coupled magnetic and elastic fields’ equations are employed to determine the magnetic attraction force between the particles. Magnetostriction and magnetoresistance are evaluated to determine the piezoresistivity effect of MREs under the combined loading condition.

This paper presents a multiple input multiple output (MIMO) wireless radar sensor network capable of measuring lower-frequency vibration and static deflection in bridges. An integrated simulation model that combines a multi degree-of-freedom structural model with a realistic model of the radar sensor network is introduced and used to characterize and predict the network’s functionality in different measurement conditions. In addition, a series of laboratory experiments have been performed for comparison with the simulation model. Finally, challenges associated with achieving accurate measurements from the radar network in a range of testing environments are discussed.

Advancements in sensing technology have improved the practice of structural health monitoring in different aspects. One of the distinguished developments introduced to the monitoring systems is deployment of wireless technology for data communication in a sensing network. While researchers have shown the effective role of wireless sensor networks in improving the affordability of structural monitoring systems, their possible impact on the reliability and accuracy of the results is still a research question. Some challenges in the design of wireless sensor units, such as the trade-off between the functionality and the power consumption, and also attempts for minimizing the cost, have caused limitations in their architecture which do not necessarily exist in the design of wired systems. On the other hand, depending on the subsequent application of the results of sensing and monitoring, the accuracy of measurements and the level of uncertainty in results can be very important. Therefore, it is necessary to carefully investigate the impact of sensor quality on monitoring results. As an effort towards understanding the effects of sensor quality on the results of structural monitoring, this paper presents and validates a metric, called Physical Contribution Ratio (PCR), which can be used to investigate the influence of measurement noise on modal parameter identification. This parameter in applied for quantification of measurement noise effects on the quality of modal identification of a steel bridge structure. Bridge’s vibration is measured through use of wired and wireless sensors with different sensing qualities and the obtained results are compared through the use of the developed metric.

With the rapid development of electrical circuits, Micro electromechanical system (MEMS) and network technology, wireless smart sensor networks (WSSN) have shown significant potential for replacing existing wired SHM systems due to their cost effectiveness and versatility. A few structural systems have been monitored using WSSN measuring acceleration, temperature, wind speed, humidity; however, a multi-scale sensing device which has the capability to measure the displacement has not been yet developed. In the previous paper, a new high-accuracy displacement sensing system was developed combining a high resolution analog displacement sensor and MEMS-based wireless microprocessor platform. Also, the wireless sensor was calibrated in the laboratory to get the high precision displacement data from analog sensor, and its performance was validated to measure simulated thermal expansion of a laboratory bridge structure. This paper expands the validation of the developed system on full-scale experiments to measure both static and dynamic displacement of expansion joints, temperature, and vibration of an in-service highway bridge. A brief visual investigation of bridges, comparison between theoretical and measured thermal expansion are also provided. The developed system showed the capability to measure the displacement with accuracy of 0.00027 in.

At present, cantilever structure used widely in civil structures will generate continuous vibration by external force due to their low damping characteristic, which leads to a serious impact on the working performance and service time. Therefore, it is very important to control the vibration of these structures. The active vibration control is the primary means of controlling the vibration with high precision and strong adaptive ability. Nowadays, there are many researches using piezoelectric materials in the structural vibration control. Piezoelectric materials are cheap, reliable and they can provide braking and sensing method harmless to the structure, therefore they have broad usage. They are used for structural vibration control in a lot of civil engineering research currently. In traditional sensor applications, information exchanges with the monitoring center or a computer system through wires. If wireless sensor networks(WSN) technology is used, cabling links is not needed, thus the cost of the whole system is greatly reduced. Based on the above advantages, a wireless control system is designed and validated through preliminary tests. The system consists of a cantilever, PVDF as sensor, signal conditioning circuit(SCM), A/D acquisition board, control arithmetic unit, D/A output board, power amplifier, piezoelectric bimorph as actuator. DSP chip is used as the control arithmetic unit and PD control algorithm is embedded in it. PVDF collects the parameters of vibration, sends them to the SCM after A/D conversion. SCM passes the data to the DSP through wireless technology, and DSP calculates and outputs the control values according to the control algorithm. The output signal is amplified by the power amplifier to drive the piezoelectric bimorph for vibration control. The structural vibration duration reduces to 1/4 of the uncontrolled case, which verifies the feasibility of the system.

A key challenge in structural health monitoring (SHM) sensors embedded inside civil structures is that elec- tronics need to operate continuously such that mechanical events of interest can be detected and appropriately analyzed. Continuous operation however requires a continuous source of energy which cannot be guaranteed using conventional energy scavenging techniques. The paper describes a hybrid energy scavenging SHM sensor which experiences zero down-time in monitoring mechanical events of interest. At the core of the proposed sensor is an analog floating-gate storage technology that can be precisely programmed at nano-watt and pico- watt power levels. This facilitates self-powered, non-volatile data logging of the mechanical events of interest by scavenging energy directly from the mechanical events itself. Remote retrieval of the stored data is achieved using a commercial off-the-shelf Gen-2 radio-frequency identification (RFID) reader which periodically reads an electronic product code (EPC) that encapsulates the sensor data. The Gen-2 interface also facilitates in simultaneous remote access to multiple sensors and also facilitates in determining the range and orientation of the sensor. The architecture of the sensor is based on a token-ring topology which enables sensor channels to be dynamically added or deleted through software control.

Currently, the maintenance or inspection of large structures is labor-intensive, so it has a problem of the large cost due to the staffing professionals and the risk for hard to reach areas. To solve the problem, the needs of wall-climbing robot are emerged. Infra-based wall-climbing robots to maintain an outer wall of building have high payload and safety. However, the infrastructure for the robot must be equipped on the target structure and the infrastructure isn’t preferred by the architects since it can injure the exterior of the structure. These are the reasons of why the infra-based wall-climbing robot is avoided. In case of the non-infra-based wall-climbing robot, it is researched to overcome the aforementioned problems. However, most of the technologies are in the laboratory level since the payload, safety and maneuverability are not satisfactory. For this reason, aerial vehicle type wall-climbing robot is researched. It is a flying possible wallclimbing robot based on a quadrotor. It is a famous aerial vehicle robot using four rotors to make a thrust for flying. This wall-climbing robot can stick to a vertical wall using the thrust. After sticking to the wall, it can move with four wheels installed on the robot. As a result, it has high maneuverability and safety since it can restore the position to the wall even if it is detached from the wall by unexpected disturbance while climbing the wall. The feasibility of the main concept was verified through simulations and experiments using a prototype.

In the present study, we propose a method to fabricate large-area graphene-based thin films using rapid low temperature reduction of graphene-oxide. Large area (~17.5 sq.cm.) graphene oxide (GO) thin films are fabricated by vacuum filtration of GO solution synthesized by the modified Hummers' method. The graphene-oxide thin films are reduced in a MTS testing machine equipped with a controlled atmosphere furnace. Reduction is carried out at temperatures from 200 °C to 400 °C, for different time durations. The fabricated reduced GO thin films are characterized using powder x-ray diffraction and energy-dispersive x-ray spectroscopy. The reduced graphene oxide thin films show decreased interlayer spacing and higher carbon-to-oxygen ratio. Conductivity measurements show an increase in conductivity by over _ve orders of magnitude compared to GO. This method offers a scalable new way of fabricating conductive large-area graphene-based thin films.

We describe lithium niobate SAW devices and PDMS microfluidic channels with which we study microparticle movement. We generate standing surface acoustic waves (with wavelengths of 200 micrometers) and show that microparticles (between 5 and 35 micrometers in diameter) move to nodes or antinodes. We report measurements of device response in the presence and absence of the microfluidic channel, which we combine with finite element simulation modeling to extract estimates of the PDMS damping.

While sensing technologies for structural monitoring applications have made significant advances over the last several decades, there is still room for improvement in terms of computational efficiency, as well as overall energy consumption. The biological nervous system can offer a potential solution to address these current deficiencies. The nervous system is capable of sensing and aggregating information about the external environment through very crude processing units known as neurons. Neurons effectively communicate in an extremely condensed format by encoding information into binary electrical spike trains, thereby reducing the amount of raw information sent throughout a neural network. Due to its unique signal processing capabilities, the mammalian cochlea and its interaction with the biological nervous system is of particular interest for devising compressive sensing strategies for dynamic engineered systems. The cochlea uses a novel method of place theory and frequency decomposition, thereby allowing for rapid signal processing within the nervous system. In this study, a low-power sensing node is proposed that draws inspiration from the mechanisms employed by the cochlea and the biological nervous system. As such, the sensor is able to perceive and transmit a compressed representation of the external stimulus with minimal distortion. Each sensor represents a basic building block, with function similar to the neuron, and can form a network with other sensors, thus enabling a system that can convey input stimulus in an extremely condensed format. The proposed sensor is validated through a structural monitoring application of a single degree of freedom structure excited by seismic ground motion.

A vibration-based energy harvester is built upon the idea of transforming mechanical vibration of an inertial frame into electrical power. When the excitation frequency matches the natural frequency of the harvester, the energy generated by mechanical vibration is maximized. However, the reliance on resonance inevitably poses a robustness issue, in that power production drops significantly when the excitation frequency is slightly off from the tuned natural frequency of the harvester. To reduce the sensitivity of power output on the resonance, this paper proposes a novel concept of vibration based energy harvester in which both the magnets and the coils are attached to the vibrating cantilevers whose natural frequencies are separated with an optimally chosen frequency band. Due to the relative motions between the coil and the magnet cantilevers, the proposed energy harvester generates higher power over a wider range of excitation frequency compared to a conventional inertial frame based energy harvester. The improvements in the power output and the robustness are validated by experiments in the laboratory and on a bridge.

New developments in novel energy harvesting schemes for structural health monitoring sensor networks have progressed in parallel with advancements in low-power electronic devices and components. Energy harvesting from galvanic corrosion is one such scheme that has shown to be a viable solution for powering sensing platforms for marine infrastructure. However, with this particular energy harvesting scheme, the power output is current limited as a result of a high terminal resistance that increases with time. In addition, the output voltage is non-stationary, and is a function of several environmental parameters and the applied resistive load. Variability in the power source requires a robust conditioning circuit design to produce a regulated power supply to the sensing and computing electronics. This paper experimentally investigates the non-stationary power characteristics of a galvanic corrosion energy harvester; and uncertainty quantification (UQ) is performed on the measured power characteristics for two experimental specimens subject to resistive load sweeps. The effects on designing a low-power sensor node are considered, and the uncertainty characteristics are applied to a low-power boost converter by means of a Monte Carlo simulation. Lastly, the total energy harvester capacity (measured in mA-Hr) is approximated from the data and is compared to a conventional battery.

Vibration control on a CX-100 wind turbine blade using piezoelectric transducers and periodic structures is investigated through experimental tests. Local phase velocity dispersion curves are extracted using measurements from impulse tests on the blade skin. The Macro Fiber Composite (MFC) transducer is used in the experimental tests because of its advantages for integrating with composite structures. The transducers are connected to a passive resonant shunt circuit to attenuate vibrations at a tuned frequency. The theory of periodic structures and ultrasonic wave theory is considered with regard to the placement of the transducers on the structure. The resonant shunt circuit combined with the periodic placement shows a reduction in response at the targeted frequency.

Wind energy is becoming increasingly important worldwide as an alternative renewable energy source. Economical, maintenance and operation are critical issues for large slender dynamic structures, especially for remote offshore wind farms. Health monitoring systems are very promising instruments to assure reliability and good performance of the structure. These sensing and control technologies are typically informed by models based on mechanics or data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order and having overlapping frequency content. Instead, time-domain techniques have shown powerful advantages from a practical point of view (e.g. embedded algorithms in wireless-sensor networks), being more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify the analysis, but such is not the case for wind loaded structures with spinning multibodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system, and the wind tower substructure interaction. Transformations of the cyclic effects on the vibration data can be applied to isolate inertia quantities different from rotating-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated Eigensystem realizations. In this paper an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here by means of a modified Eigensystem Realization Algorithm (ERA) via Stochastic Subspace Identification (SSI) and Linear Parameter Time-Varying (LPTV) techniques. Structural response is assumed under stationary ambient excitation produced by a Gaussian (white) noise assembled in the operative range bandwidth of horizontal-axis wind turbines. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to get frequencies and mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment the wind turbines are subjected to. A numerical example is presented based on data acquisition carried out in a BWC XL.1 low power wind turbine device installed in University of California at Davis. Finally, comments and observations are provided on how this subspace realization technique can be extended for modal-parameter identification using exclusively ambient vibration data.

In the previous study, a visually servoed paired structured light system (ViSP) which is composed of two sides facing each other, each with one or two lasers, a 2-DOF manipulator, a camera, and a screen has been proposed. The lasers project their parallel beams to the screen on the opposite side and 6-DOF relative displacement between two sides is estimated by calculating positions of the projected laser beams and rotation angles of the manipulators. To apply the system to massive civil structures such as long-span bridges or high-rise buildings, the whole area should be divided into multiple partitions and each ViSP module is placed in each partition in a cascaded manner. In other words, the movement of the entire structure can be monitored by multiplying the estimated displacements from multiple ViSP modules. In the multiplication, however, there is a major problem that the displacement estimation error is propagated throughout the multiple modules. To solve the problem, propagation error minimization method (PEMM) which uses Newton-Raphson formulation inspired by the error back-propagation algorithm is proposed. In this method, a propagation error at the last module is calculated and then the estimated displacement from ViSP at each partition is updated in reverse order by using the proposed PEMM that minimizes the propagation error. To verify the performance of the proposed method, various simulations and experimental tests have been performed. The results show that the propagation error is significantly reduced after applying PEMM.

This paper presents a noncontact laser lock-in thermography (LLT) technique for surface-breaking fatigue crack detection. LLT utilizes a modulated continuous wave (CW) laser as a heat source for lock-in thermography instead of commonly used flash and halogen lamps. LLT has following merits compared to conventional active thermography techniques: (1) the laser heat source can be precisely positioned at a long distance from a target structure thank to its directionality and low energy loss, (2) a large target structure can be inspected using a scanning laser heat source, (3) no special surface treatment is necessary to measure thermal wavefields and (4) background noises reflected from arbitrary surrounding heat sources can be eliminated. The LLT system is developed by integrating and synchronizing a modulated CW laser, galvanometer and infrared camera. Then, a holder exponent filter is proposed for crack identification, localization and quantification. Test results confirm that a surface-breaking fatigue crack on a steel plate is successfully evaluated using the proposed technique without any special surface treatment.

The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail integrity evaluation. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection in pair with a real-time statistical analysis algorithm has been realized. This solution presents an improvement over the previously considered laser/air-coupled hybrid system because it replaces the costly and hard-to-maintain laser with a much cheaper, faster, and easier-to-maintain air-coupled transmitter. This system requires a specialized filtering approach due to the inherently poor signal-to-noise ratio of the air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a LISA algorithm. Many of the system operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. Experimental tests have been carried out at the UCSD Rail Defect Farm. The laboratory results indicate that the prototype is able to detect internal rail defects with a high reliability. A field test will be planned later in the year to further validate these results. Extensions of the system are planned to add rail surface characterization to the internal rail defect detection.

Fatigue cracks can develop in aerospace structures at locations of stress concentration such as fasteners. For the safe operation of the aircraft fatigue cracks need to be detected before reaching a critical length. Guided ultrasonic waves offer an efficient method for the detection and characterization of fatigue cracks in large aerospace structures. Noncontact excitation of guided waves was achieved using electromagnetic acoustic transducers (EMAT). The transducers were developed for the specific excitation of the A₀ Lamb mode. Based on the induced eddy currents in the plate a simple theoretical model was developed and reasonably good agreement with the measurements was achieved. However, the detection sensitivity for fatigue cracks depends on the location and orientation of the crack relative to the measurement locations. Crack-like defects have a directionality pattern of the scattered field depending on the angle of the incident wave relative to the defect orientation and on the ratio of the characteristic defect size to wavelength. The detailed angular dependency of the guided wave field scattered at crack-like defects in plate structures has been measured using a noncontact laser interferometer. Good agreement with 3D Finite Element simulation predictions was achieved for machined part-through and through-thickness notches. The amplitude of the scattered wave was quantified for a variation of angle of the incident wave relative to the defect orientation and the defect depth. These results provide the basis for the defect characterization in aerospace structures using guided wave sensors.

The aim of this paper is to present a method for visualization thermally induced delamination in composite material based on guided wave propagation phenomenon. Tested specimen was submitted to short time period high temperature source, which generated thermal degradation. In particular, delamination in material occurred. This procedure simulates some real case scenarios damage like one cased by atmospheric discharge striking wind turbine blade. Proposed method utilizes processing of full wavefield data acquired by the Scanning Doppler Laser Vibrometer. Registered wavefield images are transformed to wavenumber domain where the wave propagation pattern is removed. In this way after transformation signal back to space domain it contains only information about changes in wave propagation and may be used for damage visualization. However, attenuation of waves cause that visualized anomalies has lower amplitudes with increased distance from the actuator. The proposed enhancement of signal processing algorithm enables quantification of the size of the damage. The enhancement is a technique for compensation of the wave attenuation so that the effects of structural damages have the same influence regardless of the location.

Increasingly, NASA exploration mission objectives include sample acquisition tasks for in-situ analysis or for potential sample return to Earth. To address the requirements for samplers that could be operated at the conditions of the various bodies in the solar system, a piezoelectric actuated percussive sampling device was developed that requires low preload (as low as 10N) which is important for operation at low gravity. This device can be made as light as 400g, can be operated using low average power, and can drill rocks as hard as basalt. Significant improvement of the penetration rate was achieved by augmenting the hammering action by rotation and use of a fluted bit to provide effective cuttings removal. Generally, hammering is effective in fracturing drilled media while rotation of fluted bits is effective in cuttings removal. To benefit from these two actions, a novel configuration of a percussive mechanism was developed to produce an augmenter of rotary drills. The device was called Percussive Augmenter of Rotary Drills (PARoD). A breadboard PARoD was developed with a 6.4 mm (0.25 in) diameter bit and was demonstrated to increase the drilling rate of rotation alone by 1.5 to over 10 times. The test results of this configuration were published in a previous publication. Further, a larger PARoD breadboard with a 50.8 mm (2.0 in) diameter bit was developed and tested. This paper presents the design, analysis and test results of the large diameter bit percussive augmenter.

Guided wave ultrasonics is an attractive technique for structural health monitoring, especially on pressurized pipes. However, civil infrastructure components, including pipes, are often subject to large environmental and operational variations that prevent traditional baseline subtraction-based approaches from detecting damage. We collect ultrasonic data on a large-scale pipe segment in its normal operating conditions and observe large environmental variations. We developed a damage detection method based on singular value decomposition (SVD) that is robust to those benign variations. We further develop an online novelty detection framework based on our SVD method to detect the presence of a mass scatterer on the pipe at the same time that we collect the data. We examine the framework with both synthetic simulations and field experimental data. The results show that the framework can effectively detect the presence of a scatterer and is robust to large environmental and operational variations.

Ultrasonic guided waves have been widely utilized for the structural health monitoring (SHM) of structural components such as plates and pipes. In particular, the noncontact excitation of the pipe surfaces using laser pulses has shown several advantages in experiments by eliminating the bonding process of the dielectric patches on the curved surfaces and the complicated interpretation of the temperature effect on the bonding layers. However, the numerical simulation of the methodology requires thermo-mechanical coupling and large-scale computation. Therefore, the numerical efficiency of the spatial partitioning by deploying thermo-mechanical elements and mechanical elements is investigated. Then, the laser excitation on the surface is modeled in the form of heat flux, and the generated wave forms are observed. The formation and propagation of the guided waves are also represented numerically.

In order to reduce the CO2-emissions and to increase the energy efficiency, the operating temperatures of power plants will be increased up to 720°C. This demands for novel high-performance steels in the piping systems. Higher temperatures lead to a higher risk of damage and have a direct impact on the structure stability and the deposition structure. Adequately trusted results for the prediction of the residual service life of those high strength steels are not available so far. To overcome these problems the implementation of an online monitoring system in addition to periodic testing is needed. RWE operates the lignite power plant Neurath. All test and research activities have to be checked regarding their safety and have to be coordinated with the business operation of the plant. An extra bypass was established for this research and made the investigations independent from the power plant operating. In order to protect the actuators and sensors from the heat radiated from the pipe, waveguides were welded to the bypass. The data was evaluated regarding their dependencies on the environmental influences like temperature and correction algorithms were developed. Furthermore, damages were introduced into the pipe with diameters of 8 mm to 10 mm and successfully detected by the acoustic method.

The use of electromagnetic sensors such as Time Domain Reflectometry (TDR) probes has gained increasing importance for water content measurements since several years, for long term monitoring of structures, among which radioactive waste repositories. TDR probes are basically sensitive to electromagnetic properties of the host material, clayrock in our case Prior to perform in-situ experiments with TDR probes, it is mandatory to have an accurate knowledge of the electromagnetic properties of clayrock as a function of their water content. We developed a new laboratory dielectric measurement device, consisting of a coaxial transmission line, enabling characterization of intact clayrock permittivity over the 50 MHz - 1 GHz frequency range. The study has shown a large variation of complex permittivity with (i) water content, the parameter of interest and (ii) frequency. The frequency dependence is induced by different relaxation processes. In a second step, these data are introduced in an original semi analytical model (Distributed Points Sources Method) in order to obtain a reliable modeling of the TDR probe. Taking into account some experimental aspects of the TDR probe, we propose to introduce a in this paper the effect of an air gap between the TDR antennas and the surrounding media. The effect of this influent parameter is evaluated owing to our DPSM modeling, and some solutions are proposed to overcome the problem.

An extrinsic Fabry-Perot interferometric (EFPI) fiber optic sensor is presented for measurement of strain at high ambient temperatures. The sensor is fabricated using a femto-second (fs) laser. The EFPI sensor is fabricated by micromachining a cavity on the tip of a standard single-mode fiber and is then self-enclosed by fusion splicing another piece of singlemode fiber. The fs-laser based fabrication makes the sensor thermally stable to sustain temperatures as high as 800 °C. The sensor is relatively insensitive towards the temperature as compared to its response towards the applied strain. The sensor can be embedded in Carbon fiber/Bismaleimide (BMI) composite laminates for strain monitoring at high ambient temperatures.

Fiber optic sensors (FOS) have significantly evolved and have reached their market maturity during the last decade. Their widely recognized advantages are high precision, long-term stability, and durability. But in addition to these advantageous performances, FOS technologies allow for affordable instrumentation of large areas of structure enabling global large-scale monitoring based on long-gauge sensors and integrity monitoring based on distributed sensors. These two approaches are particularly suitable for damage detection and characterization, i.e., damage localization and to certain extent quantification and propagation, as illustrated by two applications presented in detail in this paper: post-tensioned concrete bridge and segmented concrete pipeline. Early age cracking was detected, localized and quantified in the concrete deck of a pedestrian bridge using embedded long-gauge FOS. Post-tensioning of deck closed the cracks; however, permanent weakening in a bridge joint occurred due to cracking and it was identified and quantified. The damage was confirmed using embedded distributed FOS and a separate load test of the bridge. Real-size concrete pipeline specimens and surrounding soil were equipped with distributed FOS and exposed to permanent ground displacement in a large-scale testing facility. Two tests were performed on different pipeline specimens. The sensors bonded on the pipeline specimens successfully detected and localized rupture of pipeline joints, while the sensors embedded in the soil were able to detect and localize the failure plane. Comparison with strain-gauges installed on the pipeline and visual inspection after the test confirmed accurate damage detection and characterization.

A dynamic fibre Bragg grating interrogation scheme is investigated using two-wave mixing in erbium-doped fibre, capable of adapting to quasistatic strain and temperature drifts. An interference pattern set up in the erbium-doped fibre creates, due to the photorefractive effect, a dynamic grating capable of wavelength demodulating the FBG signal. The presence of a dynamic grating was verified and then dynamic strain signals from a fibre stretcher were measured. The adaptive nature of the technique was successfully demonstrated by heating the FBG while it underwent dynamic straining leading to detection unlike an alternative arrayed waveguide grating system which simultaneously failed detection. Two gratings were then wavelength division multiplexed with the signal grating receiving approximately 30dB greater signal showing that there was little cross talk in the system.

It is the aim of this paper to present the design of a sensor system based on fiber Bragg gratings (FBG) for the strain monitoring of an adaptive trailing edge (ATE) device. Some of the activities herein showed comes from developments inside the project SARISTU (EU-FP7), funded by the European Union inside the VII Framework Programme and focused on smart aircraft structures. Because the TE is immerged into 3D structural and aerodynamic fields, the sensor system network should have chord- and span-wise features. The ATE device will be equipped with a shape monitoring system using a widely distributed sensors based on fiber optic (FO) elements herein referred to, mainly with the aim of reducing the number of channels (then expense, complexity, etc.). In what follows, the mathematical modelling of a sensor system concept based on FBG is applied to evaluate the chord-wise strain of a trailing edge device. A hinge rotation detection capabilities based on strain measurements is presented. The detection and process of data concerning the in-flight ATE local deformation are necessary to reconstruct the shape produced by the action of a dedicated actuation system.

A solar powered aircraft is operated by converting solar energy into electrical energy. The wing of the solar powered aircraft requires a wide area to attach a number of solar cells in order to collect a large amount of solar energy. But the structural deformation and damage of the aircraft wing may occur because of bending and torsional loads induced by aerodynamic force during the operation. Therefore, the structural health monitoring of the wing is needed for increasing the operating time of the aircraft. In this study, the strain and damage of a composite wing of a solar powered aircraft were monitored by using fiber optic sensors until failure occurrence. In detail, a static loading experiment was performed on the composite wing with a length of 3.465m under a solar simulation environment, and the strain and acoustic emission (AE) of fracture signal were monitored by using fiber Bragg grating (FBG) sensors. In the results of the structural experiment, the damage occurred at a stringer when 4.5G load was applied to the composite wing, and the strain variations and AE signals were successfully measured by using FBG sensors. As a result, it is verified that the damage occurrence and location could be estimated by analyzing the strain variations and AE signals, and the fiber optic sensor would be a good transducer to monitor the structural status of a solar powered aircraft.

This paper presents a wireless ultrasound pitch-catch system that demonstrates the wireless generation and sensing of ultrasounds based on the principle of frequency conversion. The wireless ultrasound pitch-catch system consists of a wireless interrogator and two wireless ultrasound transducers. The wireless interrogator generates an ultrasound-modulated signal and a carrier signal, both at the microwave frequency, and transmits these two signals to the wireless ultrasound actuator using a pair of antennas. Upon receiving these two signals, the wireless ultrasound actuator recovers the ultrasound excitation signal using a passive mixer and then supplies it to a piezoelectric wafer sensor for ultrasound generation in the structure. For wireless ultrasound sensing, the frequency conversion process is reversed. The ultrasound sensing signal is up-converted to a microwave signal by the wireless ultrasound sensor and is recovered at the wireless interrogator using a homodyne receiver. To differentiate the wireless actuator from the wireless sensor, each wireless transducer is equipped with a narrowband microwave filter so that it only responds to the carrier frequency that matches the filter’s operation bandwidth. The principle of operation of the wireless pitch-catch system, the hardware implementation, and the associated data processing algorithm to recover the ultrasound signal from the wirelessly received signal are described. The wirelessly acquired ultrasound signal is compared with those acquired using wired connection in both time and frequency domain.

Current technologies for smart sheet metal part production base upon adhesive bonding of piezo-patches to the surface. A novel concept and process chain is the assembly of piezoceramic micro parts into local microstructures of metal sheets and subsequent joining by forming. This results in a full functional integration of the piezoceramic in the metal for sensor and actuator purposes. Mechanical coupling is non-positive without elastic interlayers and the electrical coupling is characterized by the metal being the ground electrode of the sensor. The paper describes the design, methods and tolerance management to overcome the challenges for reliable parallel microassembly and joining of prefabricated batches of 10 piezoceramic fibers with dimensions of 0.267 × 0.250 × 10 mm³ and nominal assembly clearances of ±0.018 mm. The prefabrication of the batches is achieved by stacking and dicing of piezoceramic plates. Both the principles of precision machining and elastic averaging are applied for reliable production and joining of the batches. In experiments, equally spaced piezoceramic fibers within the batches were achieved. Prototypes were assembled and joined by forming achieving functional piezo-metal composites. With the given tolerances of the parts and the microstructure a statistical tolerance analysis has been performed in order to determine the maximum allowable position uncertainty of the microassembly system. An assembly yield of > 95% is expected for future scaled up high volume assembly of piezo-metal composites.

This work presents guided wave generation, sensing, and damage detection in metallic plates using in-plane shear (d₃₆ type) piezoelectric wafers as actuators and sensors. The conventional Lead zirconate titanate (PZT) based on induced in-plane normal strain (d₃₁ type) has been widely used to excite and receive guided wave in plates, pipes or thin-walled structures. The d₃₆ type of piezoelectric wafers however induces in-plane (or called face) shear deformation in the plane normal to its polarization direction. This form of electromechanical coupling generates more significant shear horizontal waves in certain wave propagation directions, whose amplitudes are much greater than those of Lamb waves. In this paper, an analysis of shear horizontal (SH) waves generated using in-plane shear electromechanical coupling is firstly presented, followed by a multiphysics finite element analysis for comparison purpose. Voltage responses of both conventional d₃₁ and new d₃₆ sensors are obtained for comparison purpose. Results indicate this type of wafers has potential for simply providing quantitative estimation of damage in structural health monitoring.

In this paper, we describe the designs and the relative experiments of novel transducers utilized for the generation and detection of highly nonlinear solitary waves (HNSWs). In recent years these waves have been proposed for the NDE/SHM of structural materials and structural elements such as concrete and aluminum lap-joints bonded by high-strength epoxy. Conventionally these transducers contain a chain of spherical particles and the waves are measured by means of thin piezoelectric material embedded in between two half-particles. The final product is usually identified as a bead sensor. Although bead sensors can measure the propagation of HNSWs effectively, their manufacturing may require high level of precision. In this paper we propose an alternative design and investigate the use of magnetostrictive phenomenon to detect HNSWs. For both transducers’ designs a series of experiments are conducted and the results are compared. As the results in good agreement, this study may pave the road to broader applications of HNSWs for NDE and SHM.

This paper discusses theoretical analysis of electro-mechanical impedance spectroscopy (EMIS) of piezoelectric wafer active sensor (PWAS). Both free and constrained PWAS EMIS models are developed for in-plane (lengthwise) and outof plane (thickness wise) mode. The paper starts with the general piezoelectric constitutive equations that express the linear relation between stress, strain, electric field and electric displacement. This is followed by the PWAS EMIS models with two assumptions: 1) constant electric displacement in thickness direction (D3) for out-of-plane mode; 2) constant electric field in thickness direction (E3) for in-plane mode. The effects of these assumptions on the free PWAS in-plane and out-of-plane EMIS models are studied and compared. The effects of internal damping of PWAS are considered in the analytical EMIS models. The analytical EMIS models are verified by Coupled Field Finite Element Method (CF-FEM) simulations and by experimental measurements. The extent of the agreement between the analytical and experimental EMIS results is discussed. The paper ends with summary, conclusions, and suggestions for future work.

After a bridge was completed, a faulting at supporting point may occur because of the unexpected loads to bridge bearing. Serviceability of bridges could be impaired by the faulting which had caused structural damage. Therefore, it is needed for a smart bridge bearing which can observe the supporting points continuously. Some of bridge bearings have been developed for measuring vertical load and vertical displacement by installing sensors in the bearing. However in those systems, it is not easy to be replaced with new sensors when repairs are needed. In this study, the smart bridge bearing of which sensors can be replaced has been developed to overcome such a problem. In this study, strain signals were used for measuring both vertical displacements and loads. Smart bridge bearings based on FBG sensors consist of EQS(Eradi Quake System) which has been commercially used for seismic bridge bearings. Experiments were carried out to prove applicability of the smart bridge bearing based on FBG sensors that can measure vertical displacements and loads.

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.

Scour was responsible for most of the U.S. bridges that collapsed during the past 40 years. The maximum scour depth is the most critical parameter in bridge design and maintenance. Due to scouring and refilling of river-bed deposits, existing technologies face a challenge in measuring the maximum scour depth during a strong flood. In this study, a new methodology is proposed for real time scour monitoring of bridges. Smart Rocks with embedded electronics are deployed around the foundation of a bridge as field agents. With wireless communications, these sensors can send their position change information to a nearby mobile station. This paper is focused on the design, characterization, and performance validation of active sensors. The active sensors use 3-axis accelerometers/ magnetometers with a magneto-inductive communication system. In addition, each sensor includes an ID, a timer, and a battery level indicator. A Smart Rock system enables the monitoring of the most critical scour condition and time by logging and analyzing sliding, rolling, tilting, and heading of the spatially distributed sensors.

Cables are critical load carrying members of cable-stayed bridges; monitoring tension forces of the cables provides valuable information for SHM of the cable-stayed bridges. Monitoring systems for the cable tension can be efficiently realized using wireless smart sensors in conjunction with vibration-based cable tension estimation approaches. This study develops an automated cable tension monitoring system using MEMSIC’s Imote2 smart sensors. An embedded data processing strategy is implemented on the Imote2-based wireless sensor network to calculate cable tensions using a vibration-based method, significantly reducing the wireless data transmission and associated power consumption. The autonomous operation of the monitoring system is achieved by AutoMonitor, a high-level coordinator application provided by the Illinois SHM Project Services Toolsuite. The monitoring system also features power harvesting enabled by solar panels attached to each sensor node and AutoMonitor for charging control. The proposed wireless system has been deployed on the Jindo Bridge, a cable-stayed bridge located in South Korea. Tension forces are autonomously monitored for 12 cables in the east, land side of the bridge, proving the validity and potential of the presented tension monitoring system for real-world applications.

In this research, a noncontact nondestructive testing (NDT) method is proposed to detect the fatigue crack and to identify the location of the damage. To achieve this goal, Lamb wave propagation of a plate-like structure is analyzed, which is induced by scanning laser source actuation system. A ND: YAG pulsed laser system is used to generate Lamb wave exerted at the multiple points of the plate and a piezoelectric sensor is installed to measure the structural responses. Multiple time signals measured by the piezoelectric sensor are aligned along the vertical and horizontal axes corresponding to laser impinging points so that 3 dimensional data can be constructed. Then, the 3 dimensional data is sliced along the time axis to visualize the wave propagation. The scattering of Lamb wave due to the damage can be described in the wave propagation image and hence the damage can be localized and quantified. Damage-sensitive features, which are reflected wave from the damage, are clearly extracted by wave-number filtering based on the 3 dimensional Fourier transform of the visualized data. Additional features are extracted by observing different scales of wavelet coefficients so that the time of flight (TOF) of Lamb wave modes can be clearly separated. Steel plates with fatigue cracks are investigated to verify the effectiveness and the robustness of the proposed NDT approach.

In this paper, a preliminary study on the development of a wireless image-based sensor for two-dimensional (2D) crack propagation monitoring is reported. This sensor contains an optical navigation sensor board (ADNS-9500) which is incorporated into the Imote2 IPR2400 platform. To monitor crack propagation, the Imote2 sends a signal to the ADNS-9500 to switch on the built-in laser and camera collecting images reflected from the concrete surface. The captured images are processed by motion estimation methods to obtain the relative displacement between images. A series of tests have been conducted to calibrate the accuracy of the proposed crack sensor. Results show that the crack sensor can track linear motion and sinusoidal motion quiet well under some a range of speed.

Imaging based nondestructive monitoring systems are critical for evaluation of large-scale infrastructures. In this study, an accurate and fast in-plane displacement measurement method based imaging technique is developed for the purpose of health monitoring of large-scale infrastructures such as high building, long bridge, etc. The build-in repeated patterns on infrastructure facade, such as tile, checker, and brick wall pattern is used to measure the in-plane displacement distribution accurately. By performing down-sampling and intensity interpolation image processing to the images captured before and after deformations, multiple phase-shifted moiré fringe can be obtained simultaneously. The phase distribution of the moiré fringe is calculated using the phase shifting method and discrete Fourier transform technique. In the present study, both the fundamental and high frequency components are considered to analyze the repeated patterns. The in-plane displacement distribution can be obtained from the phase differences of the moiré fringe before and after deformations. Compared with conventional displacement methods and sensors, the main advantages of the method developed herein are high-resolution, accurate, fast, low-cost, and easy to implement. The principle of the proposed inplane displacement measurement is presented. The effectiveness of our method is confirmed by a simple displacement measurement experiment. Experimental result showed that a sub-millimeter displacement could be successfully detected for the field of view with meter-scale.

This research demonstrates the use of Digital Image Correlation (DIC) as a non-contact, non-destructive testing and evaluation (NDT and E) technique by presenting experimental results pertinent to damage monitoring and quantification in several material systems at different length scales of interest. At the microstructural level compact tension aluminum alloy specimens were tested under Mode I loading conditions using an appropriate field of view to track grain scale crack initiation and growth. The results permitted the quantification of the strain accumulation near the tip of the fatigue pre-crack, as well as the computation of the relevant crack opening displacement as a function of crack length. At the mesoscale level, damage quantification in fiber reinforced composites subject to both tensile and fatigue loading conditions was achieved by using the DIC as part of a novel integrated NDT approach combining both acoustic and thermal methods. DIC in these experiments provided spatially resolved and high accuracy strain measurements capable to track the formation of damage "hot spots" that corresponded to the sites of the ultimately visible fracture pattern, while it further allowed the correlation of mechanical parameters to thermal and acoustic features. Finally, at the macrostructural level DIC measurements were also performed and compared to traditional displacement gauges mounted on a steel deck model subject to both static and dynamic loads, as well as on masonry structures including hollow and grouted concrete walls.

Estimation of defect size and depth in composite structures is a relevant problem as the aerospace and wind energy industries are increasingly using composites. The determination of defect depth and size is important in order to perform repairs and assess the integrity of the structure. The problem has been previously studied using simple 1D heat conduction models. Unfortunately, 1D heat conduction based models are generally inadequate in predicting heat flow around defects, especially in composites. In this study, a novel heat conduction model is proposed to model heat flow around defects accounting for 3D heat conduction in quasi-isotropic anisotropic materials. The proposed approach is used to quantitatively determine the defect depth and size. The validity of the model is established using experiments performed on a quasi-isotropic CFRP specimen with rectangular flat-bottom defects present at different depths.

In Japan, large number of road structures which were built in the period of high economic growth, has been deteriorated due to heavy traffic and severe conditions, especially in the metropolitan area. In particular, the poor condition of expansion joints of the bridge caused by the frequent impact from the passing vehicles has significantly influence the vehicle safety. In recent year, stereo vision is a widely researched and implemented monitoring approach in object recognition field. This paper introduces the development of a stereo camera system for road surface assessment. In this study, first the static photos taken by a calibrated stereo camera system are utilized to reconstruct the three-dimensional coordinates of targets in the pavement. Subsequently to align the various coordinates obtained from different view meshes, one modified Iterative Closet Point method is proposed by affording the appropriate initial conditions and image correlation method. Several field tests have been carried out to evaluate the capabilities of this system. After succeeding to align all the measured coordinates, this system can offer not only the accurate information of local deficiency such as the patching, crack or pothole, but also global fluctuation in a long distance range of the road surface.

In this paper, a new approach to identify the source location is proposed by exploiting the compressive sensing theory, which indicates that sparse or compressible signals can be recovered using just a few measurement. A square grid configuration plate with some piezoelectric actuator and sensor is used to verify the proposed approach. The grid is used to sweep across the plate to identify the location of source. Piezoelectric actuator placed on the plate is used to excite waves, and the signals of waves received at some sensors. The sensor locations are known, however, the source location need not be known. The candidate source locations are suitably chosen grid on the surface of plate. Sensing matrix which is related to the locations of source and sensor can be calculated at each sensor. Then, the proposed approach used the received signal strengths to locate the source by minimizing the ℓ1-norm of the sparse matrix in the discrete spatial domain based on the concept of compressive sensing (CS). The simulation results show the proposed method achieves a high level of localization accuracy.

Recently, health and usage monitoring systems (HUMS) are being studied to monitor the real-time condition of aircrafts during flight. HUMSs can prevent aircraft accidents and reduce inspection time and cost. Fiber Bragg grating (FBG) sensors are widely used for aircraft HUMSs with many advantages such as light weight, small size, easy-multiplexing, and EMI immunity. However, commercial FBG interrogators are too expensive to apply for small aircrafts. Generally the cost of conventional FBG interrogators is over $20,000. Therefore, cost-effective FBG interrogation systems need to be developed for small aircraft HUMSs. In this study, cost-effective low speed FBG interrogator was applied to full-scale small aircraft wing structure to examine the operational applicability of the low speed FBG interrogator to the monitoring of small aircrafts. The cost of the developed low speed FBG interrogator was about $10,000, which is an affordable price for a small aircraft. 10 FBG strain sensors and 1 FBG temperature sensor were installed on the surface of the full-scale wing structure. Load was applied to the tip of the wing structure, and the low speed interrogator detected the change in the center wavelength of the FBG sensors at the sampling rate of 10Hz. To assess the applicability of the low-cost FBG interrogator to full-scale small aircraft wing structure, a temperature-compensated strain measurement algorithm was verified experimentally under various loading conditions of the wing structure with temperature variations.

Within the EC Clean Sky - Smart Fixed Wing Aircraft initiative concepts for actuating morphing wing structures are under development. In order for developing a complete integrated system including the actuation, the structure to be actuated and the closed loop control unit a hybrid deflection and damage monitoring system is required. The aim of the project "FOS3D" is to develop and validate a fiber optic sensing system based on low-coherence interferometry for simultaneous deflection and damage monitoring. The proposed system uses several distributed and multiplexed fiber optic Michelson interferometers to monitor the strain distribution over the actuated part. In addition the same sensor principle will be used to acquire and locate the acoustic emission signals originated from the onset and growth of defects like impact damages, cracks and delamination’s. Within this paper the authors present the concept, analyses and first experimental results of the mentioned system.

Magnetorheological (MR) dampers are used for semi-active vibration control of various dynamic systems. Existing MR dampers are usually cylinder-piston based design, which may limit the shapes and have constraints to the design of MR devices. In this paper, we propose a new MR-fluid working operation, in which MR fluids work between gear transmissions. This operation could provide more design flexibility. A prototype of the regenerative damper with MR fluids working between gear transmissions was designed, fabricated, and tested. This MR damper has the capability of power generation and velocity sensing. The feasibilities of the controllable MR damping force, power generation and velocity sensing are experimentally verified. The results of this research would be beneficial to advance the design and multiple functions of MR dampers while not limited to traditional piston-type design.

The ability to penetrate subsurfaces and perform sample acquisition at depth of meters may be critical for future NASA in-situ exploration missions to bodies in the solar system, including Mars and Europa. A corer/sampler was developed with the goal of enabling acquisition of samples from depths of several meters where if used on Mars would be beyond the oxidized and sterilized zone. For this purpose, we developed a rotary-hammering coring drill, called Auto-Gopher, which employs a piezoelectric actuated percussive mechanism for breaking formations and an electric motor that rotates the bit to remove the powdered cuttings. This sampler is a wireline mechanism that can be fed into and retrieved from the drilled hole using a winch and a cable. It includes an inchworm anchoring mechanism allowing the drill advancement and weight on bit control without twisting the reeling and power cables. The penetration rate is being optimized by simultaneously activating the percussive and rotary motions of the Auto-Gopher. The percussive mechanism is based on the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that is driven by piezoelectric stack and that was demonstrated to require low axial preload. The design and fabrication of this device were presented in previous publications. This paper presents the results of laboratory and field tests and lessons learned from this development.

Several types of flaw can occur during the layup process of prepreg composite laminates. Quality control after the production process checks the end product by testing the specimens for flaws which are included during the layup process or curing process, however by then these flaws are already irreversibly embedded in the laminate. This paper demonstrates the use of a laser displacement sensor technique applied during the layup process of prepreg laminates for in-situ flaw detection, for typical flaws that can occur during the composite production process. An incorrect number of layers and fibre wrinkling are dominant flaws during the process of layup. These and other dominant flaws have been modeled to determine the requirements for an in-situ monitoring during the layup process of prepreg laminates.

In this paper, we demonstrate an attachable energy-harvester-powered wireless vibration-sensing module for milling-process monitoring. The system consists of an electromagnetic energy harvester, MEMS accelerometer, and wireless module. The harvester consisting of an inductance and magnets utilizes the electromagnetic-induction approach to harvest the mechanical energy from the milling process and subsequently convert the mechanical energy to an electrical energy. Furthermore, through an energy-storage/rectification circuit, the harvested energy is capable of steadily powering both the accelerometer and wireless module. Through integrating the harvester, accelerometer, and wireless module, a self-powered wireless vibration-sensing system is achieved. The test result of the system monitoring the milling process shows the system successfully senses the vibration produced from the milling and subsequently transmits the vibration signals to the terminal computer. Through analyzing the vibration data received by the terminal computer, we establish a criterion for reconstructing the status, condition, and operating-sequence of the milling process. The reconstructed status precisely matches the real status of the milling process. That is, the system is capable of demonstrating a real-time monitoring of the milling process.

The development of data processing algorithms that enhance pattern detectability for civil infrastructure systems exposed to the environment is critical in various monitoring applications for construction, operation, maintenance, and hazard detection. For example, the precise detection of snow/ice forming on road pavement surface is essential for transportation safety. Another example is monitoring precipitation effects for the structural safety of retaining walls. Ad hoc analysis of streamed data involves processing complicated, non-stationary and nonlinear multi-physics behaviors of coupled interactions between civil systems and various surrounding factors. However, it is sometimes impossible to measure all the significant factors that influence the system’s behavior. In addition, monitoring costs can be exorbitant, limiting the amount of resources used. Therefore, the modeling of these coupled interactions is usually very difficult. The Auto Modulating Pattern Detection Algorithm (AMP) is a novel data processing algorithm that extends the original EMD-HHT method to detect a “small” but important intermittent event of interest that is usually masked by “dominant” environmental disturbances in various monitoring applications. With AMP, higher detectability can be achieved by: (1) amplifying the amplitude of the pattern-changing event’s frequency characteristics in the time-frequency domain, (2) reducing the baseline frequency fluctuation in the time-frequency domain, and (3) increasing the temporal resolution of the energy-time-frequency domain signal. This study demonstrates AMP’s applicability to various monitoring applications in operation and maintenance: monitoring structural safety for retaining walls and monitoring meteorological hazards on road pavement surface under field conditions for traffic safety.

A reliable prognostics framework is essential to prevent catastrophic failure of bridges due to scour. In the U.S., scour accounts for almost 60% of bridge failures. Currently available techniques in the literature for predicting scour are mostly based on empirical equations and deterministic regression models, like Neural Networks and Support Vector Machines, and do not predict the evolution of scour over time. In this paper, we will discuss a Gaussian process model, which includes Bayesian uncertainty for prediction of time-dependent scour evolution. We will validate the model on the experimental data conducted in four different flumes in different conditions. The robustness of the algorithm will also be demonstrated under different scenarios, like lack of training data and equilibrium scour conditions. The results indicate that the algorithm is able to predict the scour evolution with an error of less than 20% for most of the time, and 5% or less given enough training data.

This paper proposes an evaluation method for a CPG controller designed for active mass dampers. Neural oscillators composing the CPG have nonlinear and entrainment properties. Therefore, the proposed controller has possibility to have flexibility, when the structural parameters, i.e. stiffness or damping, are changed by the effect of earthquakes and the like. However, there has been no study to evaluate the controller’s above-mentioned properties. For tuning into practical application, the reliability and flexibility along with the controller’s performance must be analyzed. In our previous study, the phase reduction theory was tried to appraise the synchronization between a structure and a single neural oscillator and the synchronization region of the neural oscillator was obtained as basic research. However, the information from the synchronization region was insufficient to evaluate the system, because the neural oscillator has a phase difference called a phase locking point between the structure and the neural oscillator during the synchronization. Then, in this paper, the phase locking point within the synchronization region between a structure and a single neural oscillator is focused on, and the phase locking point and the vibration mitigation effect are considered with the simple object model.

Fiber Bragg Gratings (FBG) sensors have a great potential in active vibration control of smart structures thanks to their small transversal size and the possibility to make an array of many sensors. The paper deals with the opportunity to reduce vibration in structures by using distributed sensors embedded in carbon fiber structures through the so called sensors-averaging technique. This method provides a properly weighted average of the outputs of a distributed array of sensors generating spatial filters on a broad range of undesired resonance modes without adversely affecting phase and amplitude. This approach combines the positive sides of decentralized control techniques as the control forces applied to the system are independent of one another, while, as for the centralized controls it has the possibility to exploit the information from all the sensors. The ability to easily manage this information allows to synthesize an efficient modal controller. Furthermore it enables to evaluate the stability of the control, the effects of spillover and the consequent effectiveness in reducing vibration. Theoretical aspects are supported by experimental applications on a large flexible system composed of a thin cantilever beam with 30 longitudinal FBG sensors and 6 piezoelectric actuators (PZT).

This paper describes a new application of the monolithic folded pendulum configured as seismometer (no force feed-back) and used as sensor in the control of inertial platforms and suspensions, like, for example, those used in interferometric detectors of gravitational waves, where a residual horizontal motion better than 10⁻¹⁵m/√Hz in the band 0.01 ÷ 100Hz is a requirement. The experimental results, obtained in the band 0.01 ÷ 10Hz, demonstrate that this sensor has enough dynamics and sensitivity to introduce no limitations to the state-of-the-art control systems. Moreover, its full scalability allows an easy integration and positioning also on the different stages of multistage mechanical suspensions (seismic attenuators) and inertial platforms. This new application demonstrates not only the feasibility of the proposed new control strategy in the low frequency region, but, and it is very relevant, that it is now possible the implementation of very effective control systems with a large reduction of control electronics, replaced by less noisy optical and mechanical devices, with the further advantage of rendering the whole system surely less sensitive to environmental noises. The results of this study, although preliminary and obtained with sensors not optimized for the specific application, are presented and discussed in this paper, in connection with some of the possible applications (platforms and mechanical structure control and stabilization, building controls, etc.) and the planned further developments and improvements.

The mathematical model of a vibrating structure includes mass, damping and stiffness; out of which mass and stiffness could be defined as a function of the system geometry, whereas damping is more of an observed phenomenon. Despite having a large literature on the subject, the underlying physics is only known in a phenomenological ad-hoc manner, making damping an overall mystery in the general dynamic analysis of structures. A major reason of this could be the fact that there is no single universally accepted model for damping. Common practice is to use the classical viscous damping model originated by Rayleigh, through his famous ‘Rayleigh dissipation function’, with a preconceived damping ratio, irrespective of the purpose or type of analysis involved. This paper investigates the effect of this modelling uncertainty on the analytical prediction of the required control force in a semi-active control application for civil structures. Global classical Rayleigh damping models and global non-viscous damping models are used in the present study. Responses of a laboratory slab strip are simulated and are compared with experimental responses. The comparisons emphasises the fact that the choice of in-structure damping models has a significant effect in the computation of the required control force. The comparison also clearly indicates that mathematically sophisticated models have better prediction capability as compared to the classical Rayleigh model.

Up to date, various studies have been conducted using electro-mechanical impedance (EMI) method on concrete, including monitoring the strength development or to find damage in the structure. Since EMI method utilizes a single piezoelectric material to be used as an actuator and a sensor simultaneously, the method has major advantages compared to other non-destructive testing methods. However the method requires a piezoelectric material to be permanently attached or embedded into a structure. Thus when monitoring multiple structures, the method may become quite expensive. In this study, two re-usable EMI methods conducted by researchers Na et al and Tawie et al are overviewed. The idea of re-usable EMI method is still relatively new, resulting in the reduction of monitoring costs since the same piezoelectric material is used as many times as possible, while ensuring better repeatability and reliability in measurements.

The piezoresistivity-based strain sensing ability of cementitious composites containing graphite nanoplatelet (GNP) is investigated in this paper. GNP offers the advantages of ease of processing, excellent mechanical and electrical properties at a very low cost compared to carbon nanotubes and carbon nano-fibers. Cement mortar with 0%, 1.2%, 2.4%, 3.6% and 4.8% of GNP (by volume of composite) were cast. The electrical resistance of the specimens was measured by both the two- and four-probe methods using direct current (DC). The effect of polarization was characterized and the percolation threshold was experimentally found to be between 2.4% and 3.6% of GNP based on both accelerated and normal drying specimens. The assumption of Ohmic material was tested with varying current and found to be valid for current < 0.01mA and 0.5mA for four- and two-probe methods respectively. The piezoresistive effect was demonstrated by comparing the gage factors of mortars with GNP vs plain mortar under cyclic loading in compression at 3 strain levels. At low strains, the high gage factor is believed to stem from both the effect of the imperfect interfaces around the GNP and the piezoresistivity of the GNP; at higher strains, the gage factor is likely to be attributed to the piezoresistivity of the GNP and it is still 1-2 orders of magnitude larger than the gage factor arising from geometric changes.

This paper investigates a scouring sensor using electrical properties of carbon nanotubes(CNTs)-filled cement-based composite. First, for specimens filled with different amount of CNTs, the electrical behavior and the principle which it followed were studied. The effect of the different magnetic field intensity on the arrangement of CNTs in the base was presented. Furthermore, the environment effects (temperature and humidity) on sensors and its causes were revealed. Also, the design of the temperature and humidity self-compensation sensor based on separated electrode was proposed. Finally, by comparison of the sensitivity of the scouring electrode and the stability of the reference electrode, the optimal scheme of the electrode was determined.

Frequent and quantitative assessment of road condition is important as the maintenance of the road infrastructure needs to be performed with a limited budget. Vehicle Intelligent Monitoring System (VIMS) has been developed to estimate an index of road ride comfort (International Roughness Index; IRI) by obtaining the acceleration responses of ordinary vehicles together with GPS position data. VIMS converts the vertical acceleration of the measurement vehicle to acceleration RMS of the sprung mass of the standard Quarter Car model, and then to IRI using an approximate expression. By driving over a hump of a known profile and comparing the responses with Quarter Car simulation responses, a variety of vehicles can be calibrated; a non-linear quarter car model equivalent to the vehicle is identified. By performing numerical simulation using the nonlinear vehicle model, the difference in driving speed can also be calibrated. The measurement results can be exported to maps to comprehend road condition in a geographical view and to other data base systems. In addition, smartphones which can record motions, GPS data, and movies synchronously are utilized to improve VIMS. Because practical installation locations of smartphones are limited and because angular velocity responses are less subjective to difference in installation locations, VIMS is extended to utilize the pitching angular velocity. Furthermore, high frequency components of acceleration responses are analyzed to distinguish local pavement damages or joints from rough road sections. The examination of synchronously recorded movies confirmed the capability to distinguish the local conditions.

A moving loads distribution identification method for cable-stayed bridges based on compressive sampling (CS) technique is proposed. CS is a technique for obtaining sparse signal representations to underdetermined linear measurement equations. In this paper, CS is employed to localize moving loads of cable-stayed bridges by limit cable force measurements. First, a vehicle-bridge model for cable-stayed bridges is presented. Then the relationship between the cable force and moving loads is constructed based on the influence lines. With the hypothesis of sparsity distribution of vehicles on bridge deck (which is practical for long-span bridges), the moving loads are identified by minimizing the ‘l2-norm of the difference between the observed and simulated cable forces caused by moving vehicles penalized by the ‘l1-norm’ of the moving load vector. The resultant minimization problem is convex and can be solved efficiently. A numerical example of a real cable-stayed bridge is carried out to verify the proposed method. The robustness and accuracy of the identification approach with limit cable force measurement for multi-vehicle spatial localization are validated.

This work presents a non-destructive and non-contact acoustic sensing approach for measuring road profile of road and bridge deck with vehicles running at normal speed without stopping traffic. This approach uses an instantaneous and real-time dynamic tire pressure sensor (DTPS) that can measure dynamic response of the tire-road interaction and increases the efficiency of currently used road profile measuring systems with vehicle body-mounted profilers and axle-mounted accelerometers. In this work, a prototype of real-time DTPS system has been developed and demonstrated on a testing van at speeds from 5 to 80 miles per hour (mph). A data analysis algorithm has been developed to remove axle dynamic motions from the measured DTPS data and to find the transfer function between dynamic tire pressure change and the road profile. Field test has been performed to estimate road profiles. The road profile resolution is approximately 5 to 10 cm in width and sensitivity is 0. 3 cm for the height road surface features at driving speeds of 5 to 80 mph.

In this paper, an approach based on a new damage index-Distributed Force Change(WDFC), for monitoring the structural health of risers used for production in deep-water floating platforms, is presented. Experiments of a scaled pipe are carried out to validate the vibration based damage identification method. The influences of multiple cracks in the WDFC damage index are studied. Futhermore, this paper demonstrates the effectiveness of wave propagation based structural health monitoring (SHM) strategies within the pipe model. This is realized based on the results of numerical investigation obtained by the use of Finite Element Method(FEM) together with application of Time-of-Flight(FoT) damage identification method in which the damage severity is indicated by Root Mean Square(RMS) of the damage-reflected wave. The influence of crack(s) in the riser/pipe on the wave propagation are studied. The results from the experiments and numerical analysis indicate that both the two damage identification methods can provide information about the estimated crack location(s) and the possible extent of crack. Hence the two methods are suitable for globally and locally monitoring the structural health of deepwater risers respectively.

A novel symbolization strategy, "dynamic" strategy, of symbolization state series analysis (STSA) is employed in symbolization-based differential evolution strategy (SDES) to alleviate the effects of harmful noise. Procedure of "dynamic" strategy is described, effect of parameters in "dynamic" is verified, cases of partial output are considered. Performance of the proposed methodology was numerically compared with other symbolization strategies. Particle swarm optimization (PSO) and differential evolution (DE) on raw acceleration data are used as comparison to show the good noise immunity of our proposed methodology. These simulations revealed that our proposed methodology is a powerful tool for identifying the unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

The last decade has witnessed rapid developments in structural system identification methodologies based on intelligent algorithms, which are formulated as multi-modal optimization problems. However, these deterministic methods more or less ignore uncertainties, such as modeling errors and measurement errors, that are inevitably involved in the system identification problem of civil-engineering structures. A new stochastic structural identification method is proposed that takes into account parametric uncertainties in the parameters of building structures. The proposed method merges the advantages of the multi-objective differential evolution optimization algorithm for the non-domination selection strategy and the probability density evolution method for incorporating parametric uncertainties. The results of simulations on identifying the unknown parameters of a structural system demonstrate the feasibility and effectiveness of the proposed method.

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 < 100 mHz with large band (10⁻⁷ Hz − 10 Hz), high quality factor (Q < 1500 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.

In this paper we present the scientific data recorded by tunable mechanical monolithic horizontal seismometers located in the Gran Sasso National Laboratory of the INFN, within thermally insulating enclosures onto concrete slabs connected to the bedrock. The main goals of this long-term large-band measurements are for the seismic characterization of the site in the frequency band 10⁻⁶÷10Hz and the acquisition of all the relevant information for the optimization of the sensors.

The paper describes a tilt meter sensor for geophysical applications, based on Folded Pendulum (FP) mechanical sensor. Both the theoretical model and the experimental results of a tunable mechanical monolithic FP tilt meter prototype are presented and discussed. Some of the most important characteristics, like the measured resolution of ≈ 0.1 nrad at 100mHz, are detailed. Among the scientific results, earth tilt tides have been already observed with this monolithic FP tilt meter prototype.

Stay cables of long span cable-stayed bridges are easy to vibrate under wind or wind/rain loads owning to their very low inherent damping. To install cable dampers near to the anchorages of cable has become a common practice for cable vibration control of cable-stayed bridge structures. The performance of passive linear viscous dampers has been widely studied. However, even the optimal passive device can only add a small amount of damping to the cable when attached a reasonable distance from the cable anchorage. This paper investigates the potential for improved damping using semiactive devices based on nonlinear frictional type of dampers. The equations of motion of a cable with a friction damper were derived using an assumed modes approach and the analytical solution for the motion equations was obtained. The results show that the friction damper evokes linearly decaying of free vibrations of the cable as long as the damper does not lock the cable. The equivalent modal damping ratio of cable with the friction damper is strongly amplitude dependent. Based on the characteristics of friction damper, the authors proposed a semi-active control strategy for cable control with dampers. According to the semi-active control law, the damper force has to be adjusted in proportion to the cable amplitude at damper position. The effectiveness of passive linear viscous dampers is reviewed. The response of a cable with passive and semi-active dampers is studied. The response with a semi-active damper is found to be dramatically reduced compared to the optimal passive linear viscous damper, thus demonstrating the potential benefits using a semi-active damper for absorbing cable vibratory energy.

Structural health monitoring (SHM) for evaluating and maintaining structural integrity of a building is a very important research field. This paper proposes the use of squared modal frequencies, to detect damage to individual parts of the structures as well their extent by using a limited number of sensors, as proposed by Mita and Hagiwara¹. This damage assessment method evaluated in numerical simulations of a five-story shear structure and in shake-table tests of a five-story steel model. The damage to the structure was simulated by reducing the stiffness of each floor. The study showed that it is possible to identify, localize, and evaluate the magnitude of the real damage in a multi-story structure from shifts in its natural frequencies.

Sparsity constraints are now very popular to regularize inverse problems in the field of applied mathematics. Structural damage identification is a typical inverse problem of structural dynamics and Structural damage is a spatial sparse phenomenon, i.e., structural damage occurs, only part of elements or substructures are damaged. In this paper, a structural damage identification method based on the substructure-based sensitivity analysis and the sparse constraints regularization is proposed. Substructure sensitivity analysis, the establishment of structural damage stiffness parameter variation and change of modal parameters of linear equations between the measured degrees of freedom is limited, the equations for a morbid equation. The introduction of structural damage sparsity conditions, to minimize the l1 norm optimization solution. The numerical example of the 20 bay-truss structure with considering measurement noise, incomplete of measurements and multi-damage cases are carried out. The effects of number sensor and layout to the identification results are also investigated. The results indicated that the damage locations and extents can be effectively identified by the proposed method. Additionally, the sensor location can be random arrangement, which has great significance to the sensor placement of the actual structural health monitoring because robust structural damage identification also can be obtained even a few of sensor are failure.

In this paper, we reports on a tactile sensor with Hall effect elements, which are generally used as magnetic sensors, for multimodal sensing devices to detect the contact force and the temperature. This tactile sensor consists of Hall elements and a magnet that are embedded in an elastic silicone rubber as the artificial skin. Here, the normal contact force is detected by distance change between a Hall element and a magnet, and the temperature is also detected using the temperature dependence of the Hall element. The temperature dependence of Hall elements depends on the Hall material and the drive circuit to generate the Hall voltage. In this study, two Indium antimonide (InSb) Hall elements and two drive circuits, that is, a constant voltage drive and a constant current drive were used to demonstrate the tactile sensor. Two output Hall voltages were measured in the normal contact force range from 0 to 50N, the temperature range from -10 to 50°C. The inverse response surface to identify the normal contact force and the temperature was formulated using the experimental results. It was possible to detect the contact force and the temperature by obtaining two kinds of Hall voltages.

Statistical analysis of vehicle loads on expressway in Dongying City in China was made to investigate vehicle loads on expressway in recent years. Results show that all the vehicle loads follow multimodal distribution, and cannot be described by a typical probability model. In this paper, the probability density function is simulated by a weighted sum of three lognormal distributions. The three peaks represent light, normal and heavy vehicles respectively. The multivariate extreme value distribution for heavy vehicles is simulated by the Extreme Value I Distribution. Finally the 0.95-fractals of maximum distribution of the three peaks are calculated for different time periods.

Tin oxide (SnO₂) thin film gas sensors that function at room temperature have been fabricated on nanostructured substrates. After femtosecond laser irradiation of the surface of the SnO₂, the sensitivity to gases, for example, carbon monoxide, increased noticeably. The dependence of the sensitivity on the number of laser pulses has been investigated. It is believed that the femtosecond laser pulses generate defects in a thin layer on the SnO₂ sensor surface. These defects may result in a potential energy well creating surface bound states for electrons to move on the surface, which increases the sensitivity to gases.