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.