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Bridge cables are the safety lifelines of cable-supported bridges. However, many suspenders in cable-supported bridges were inaccurately evaluated and suffered from premature damage under complex environmental and load actions. A multifactor competition model was proposed and validated to consider the coupling effect of multifactor actions. Furthermore, a framework for corrosion-fretting fatigue analysis was established based on the competition model, stochastic finite-element method, and time-dependent reliability. The degradation process in the wires and suspenders of a bridge was analyzed, and their service lives were accurately predicted. In addition, the suspenders were quantitatively investigated for multifactor coupling and single-factor effects. This paper found that the coupling effect of corrosion, fretting, and fatigue significantly aggravates wire damage and reduces the service lives of short suspenders. [ABSTRACT FROM AUTHOR]

Self-structural health monitoring (SHM) functionalities for fiber-reinforced polymer composites have become highly sought after to ensure the structural safety of newly advancing components in the automotive, civil, mechanical, and aerospace industries. This paper introduces a self-damage detection and memory (SDDM) hybrid composite material, where the structural carbon fiber tow is transformed into a piezoresistive sensor network, and the structural glass fiber operates as electrical insulation. In this study, SDDM specimens were fabricated, and tensile and impact tests were performed. The tensile tests of SDDM specimens find two distinct loading peaks: first where the carbon fiber fails, and second where the glass fiber fails. A linear correlation was observed between the carbon fiber resistance and composite strain up to a threshold, beyond which a sharp nonlinear increase in resistance occurred. The resistance then approached infinity, coinciding with the first loading peak and failure of the carbon fiber elements. This demonstrates the potential for a damage early warning threshold. Additionally, the effect of stitching the sensor tow in a zig-zag pattern over a large area was investigated using tailored fiber placement (TFP) of 1-loop, 3-loop, and 5-loop specimens. Tensile testing found that increasing the number of loops improved the sensor's accuracy for strain sensing. Furthermore, impact tests were conducted, and as the impact energy progressively increased, the sensor resistance permanently increased. This illustrates a capability for self-memory of microdamage throughout the life cycle of the structure, potentially useful for predicting the remaining life of the composite. [ABSTRACT FROM AUTHOR]

In contrast to the traditional uncertainty quantification of the modal parameters identified from the output‐only measurement data, this study develops an uncertainty propagation and quantification method for structural flexibility identified from input–output measurement data and then uses the derived flexibility matrix for structural reliability analysis. First, a novel procedure for variance estimations of the complete system matrix of the state‐space model is derived based on first‐order perturbation theory. Then, the mode scaling factors and integrated modal flexibility are subsequently derived and quantified. Finally, the predicted displacements and their uncertainty intervals are used to implement the reliability analysis in terms of the structural displacement serviceability limit state (SLS). The accuracies of the derived expressions are verified by a numerical example through the comparison of variance values estimated from Monte Carlo simulations. The simulation example also indicates that we can expect to identify structural parameters with higher reliability if the input force to the structure is measured, and the identified modal scaling and corresponding flexibility matrix undergo more uncertainty than those of the modal parameters. Finally, we apply the proposed uncertainty quantification method to a scaled three‐story frame structure to demonstrate its practicability. The proposed method bridges a gap between the identified structural flexibility and structural reliability analysis directly by the probability category. [ABSTRACT FROM AUTHOR]

This paper presents a unique structural reliability estimation method incorporating structural parameter identification results based on the seismic response measurement. In the shaking table test, a three-bent concrete bridge model was shaken to different damage levels by a sequence of earthquake motions with increasing intensities. Structural parameters, stiffness and damping values of the bridge were identified under damaging seismic events based on the seismic response measurement. A methodology was developed to understand the importance of structural parameter identification in the reliability estimation. Along this line, a set of structural parameters were generated based on the Monte Carlo simulation. Each of them was assigned to the base bridge model. Then, every bridge model was analyzed using nonlinear time history analyses to obtain damage level at the specific locations. Last, reliability estimation was performed for bridges modeled with two sets of structural parameters. The first one was obtained by the nonlinear time history analysis with the Monte Carlo simulated parameters which is called nonupdated structural parameters. The second one was obtained by updating the first set in Bayesian sense based on the vibration-based identification results which is called updated structural parameters. In the scope of this paper, it was shown that residual reliability of the system estimated using the updated structural parameters is lower than the one estimated using the nonupdated structural parameters. CE Database subject headings: Shake table tests; Vibration; Identification; Structural reliability; Parameters; Bayesian analysis; Monte Carlo method; Kalman filters. Author keywords: Shaking table test; Vibration-based; Identification; Reliability; Bayesian; Monte Carlo; Extended Kalman filter. DOI: 10.1061/(ASCE)EM.1943-7889.0000066

A stochastic model of traffic excitation on bridges is developed assuming that the arrival of vehicles traversing a bridge (modeled as an elastic beam) follows a Poisson process, and that the contact force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements. The parameters in this model, such as the Poisson arrival rate and the stochastic distribution of vehicle speeds, are obtained by image processing of traffic video data. The model reveals that traffic excitations on bridges are spatially correlated. This important characteristic is usually incorrectly ignored in most output-only methods for the identification of bridge structural properties using traffic-induced vibration measurement data. In this study, the stochastic traffic excitation model with partial traffic information is incorporated in a Bayesian framework, to evaluate the structural properties and update their uncertainty for condition assessment of the bridge superstructure. The vehicle weights are also estimated simultaneously in this procedure. The proposed structural assessment methodology is validated on an instrumented highway bridge. DOI: 10.106l/(ASCE)0733-9399(2009) 135:8(747) CE Database subject headings: Monitoring; Assessment; Bridges, highway; Vibration; Excitation; Vehicles; Stochastic models; Bayesian analysis.

A new vision of structural health monitoring (SHM) is presented, in which the ultimate goal of SHM is not limited to damage identification, but to describe the structure by a probabilistic model, whose parameters and uncertainty are periodically updated using measured data in a recursive Bayesian filtering (RBF) approach. Such a model of a structure is essential in evaluating its current condition and predicting its future performance in a probabilistic context. RBF is conventionally implemented by the extended Kalman filter, which suffers from its intrinsic drawbacks. Recent progress on high-fidelity propagation of a probability distribution through nonlinear functions has revived RBF as a promising tool for SHM. The central difference filter, as an example of the new versions of RBF, is implemented in this study, with the adaptation of a convergence and consistency improvement technique. Two numerical examples are presented to demonstrate the superior capacity of RBF for a SHM purpose. The proposed method is also validated by large-scale shake table tests on a reinforced concrete two-span three-bent bridge specimen. CE Database subject headings: Bayesian analysis; Filters; Monitoring; Assessments; Vibration; Identification; Structural analysis.

Methods that identify structural component stiffness degradation by pre- and postevent low amplitude vibration measurements, based on a linear time-invariant (LTI) system model, are conceptually justified by examining the hysteresis loops the structural components experience in such vibrations. Two large-scale shake table experiments, one on a two-column reinforced concrete (RC) bridge bent specimen, and the other on a two-span three-bent RC bridge specimen were performed, in which specimens were subjected to earthquake ground motions with increasing amplitude and progressively damaged. In each of the damaged stages between two strong motions, low amplitude vibrations of the specimens were aroused, and the postevent component stiffness coefficients were identified by optimizing the parameters in a LTI model. The stiffness degradation identified is consistent with the experimental hysteresis, and could be quantitatively related to the capacity residual of the components. CE Database subject headings: Shake table tests; Verification; Bridges, concrete; Concrete, reinforced; Assessment; Vibration; Stiffness.

This paper presents a comprehensive study involving triaxial earthquake simulator testing on seismic isolation of electric power transformers. In this study, two isolation systems were developed: one using sliding bearings combined with rubber bearings and the other segmented high-damping rubber bearings. Triaxial earthquake simulator testing was performed using a large-scale transformer model equipped with a real bushing. The effectiveness of the base isolation system in reducing the response acceleration of power transformer systems was demonstrated. Important observations were made on the seismic responses of the transformer and bushing. In particular, the vertical component of the ground motion induced a high-frequency response of the bushing when the transformer was isolated with the sliding isolation system. This is because the vertical motion changes the friction forces in the sliding bearings that excite high modes in the transformer-bushing system. Furthermore, the effect of the interaction with the bushing connecting cables on the response of the bushing in the base-isolated system was experimentally evaluated. In conclusion, the base isolation technology, when properly designed, is a highly effective measure for seismic protection of power transformers. Index Terms--Base isolation, bushing, earthquake simulator, electric power transformer, seismic, substations.

Urban water delivery systems can be damaged by earthquakes or severely cold weather. In either case, the damage cannot easily be detected and located, especially immediately after the event. In recent years, real-time damage estimation and diagnosis of buried pipelines attracted much attention of researchers focusing on establishing the relationship between damage ratio (breaks per unit length of pipe) and ground motion, taking the soil condition into consideration. Due to the uncertainty and complexity of the parameters that affect the pipe damage mechanism, it is not easy to estimate the degree of physical damage only with a few numbers of parameters. As an alternative, this paper develops a methodology to detect and locate the damage in a water delivery system by monitoring water pressure on-line at some selected positions in the water delivery systems. For the purpose of on-line monitoring, emerging supervisory control and data acquisition technology can be well used. A neural network-based inverse analysis method is constructed for detecting the extent and location of damage based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water delivery system with one location of damage, and validated by using a set of data that have never been used in the training. It is found that the method provides a quick, effective, and practical way in which the damage sustained by a water delivery system can be detected and located. CE Database subject headings: Damage; Water distribution: Water pipelines: Buried pipes: Urban areas: Data collection.

This paper presents a technology for detecting invisible damage inside concrete, which is based on reconstruction of dielectric profile (image) of the concrete illuminated with microwaves sent from and received by antenna arrays controlled by specialized software. The imaging system developed in this study consists of an 8x8 transmitting and an 8x8 receiving arrays, an innovative numerical bifocusing operator for improving image resolution, and imaging software for reconstructing a two-dimensional image from the scattered field. The effectiveness of the developed technology in detecting steel and voids inside concrete has been demonstrated through numerical simulation and experiments. CE Database subject headings: Microwaves; Imaging techniques; Antennas; Concrete structures; Damage assessment; Nondestructive tests.

The uniaxial compressive strength of concrete is the most widely used criterion in producing concrete. Although testing of the uniaxial compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. At this point, it is too late to make improvements if the test result does not satisfy the required strength. Therefore, the strength estimation before the placement of concrete is highly desirable. This study presents the first effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions. Back-propagation neural networks were developed, trained, and tested using actual data sets of concrete mix proportions provided by two ready-mixed concrete companies. The compressive strengths estimated by the neural networks were verified by laboratory testing results. This study demonstrated that the neural network techniques are effective in estimating the compressive strength of concrete based on the mix proportions. Application of these techniques will contribute significantly to the concrete quality assurance. CE Database subject headings: Neural networks; Compressive strength; Estimation; Concrete; Mixing.

A baseline model is essential for long-term structural performance monitoring and evaluation. This study represents the first effort in applying a neural network-based system identification technique to establish and update a baseline finite element model of an instrumented highway bridge based on the measurement of its traffic-induced vibrations. The neural network approach is particularly effective in dealing with measurement of a large-scale structure by a limited number of sensors. In this study, sensor systems were installed on two highway bridges and extensive vibration data were collected, based on which modal parameters including natural frequencies and mode shapes of the bridges were extracted using the frequency domain decomposition method as well as the conventional peak picking method. Then an innovative neural network is designed with the input being the modal parameters and the output being the structural parameters of a three-dimensional finite element model of the bridge such as the mass and stiffness elements. After extensively training and testing through finite element analysis, the neural network became capable to identify, with a high level of accuracy, the structural parameter values based on the measured modal parameters, and thus the finite element model of the bridge was successfully updated to a baseline. The neural network developed in this study can be used for future baseline updates as the bridge being monitored periodically over its lifetime. CE Database subject headings: Instrumentation; Vibration measurement; Neural networks; Bridges; Performance evaluation; Monitoring; Signal processing.

Microwave tomographic imaging technology using a bifocusing operator has been developed in order to detect the internal voids/objects inside concrete structures. The imaging system consists of several cylindrical or planar array antennas for transmitting and receiving signals, and a numerical focusing operator is applied to the external signals both in transmitting and in receiving fields. An imaging algorithm using numerical focusing operator was developed, which allows the recovery of a two-dimensional object from its scattered field. Numerical simulation demonstrated that a subsurface image can be successfully reconstructed by using the proposed tomographic imaging technology. For the experimental verification, a prototype planar antenna array was fabricated and tested on a concrete specimen. Index Terms--Damage detection, imaging, reflection tomography.

Summary: Measurement of two‐dimensional surface displacement/strain distributions can be crucial in monitoring important structures. Computer vision techniques have the potentials for measuring surface displacements/strains. Conventional digital‐image‐correlation (DIC)‐based computer vision techniques that have been applied in controlled conditions with artificially painted speckle patterns, however, have difficulties in robust measurement of structures' surface displacements against optical noises. Additionally, surface displacements obtained by DIC based on block‐resolution template matching have limited accuracy because of low spatial resolutions and low level of smoothness. Therefore, a new computer vision technique SurfaceVision is proposed for accurate and robust surface displacement/strain measurement to tackle simulated field environmental conditions by incorporating multiple novel algorithms. First, a gradient‐based edge‐enhanced transform (EET) originally developed for one‐point displacement tracking is extended for enabling robust surface displacement measurements against optical noises by manipulating gradient information rather than image intensities. Then, the improvement in the smoothness of surface displacements is enabled by incorporating EET with the iterative displacement optimization and the customized smart branching algorithms. Moreover, a novel pixel‐resolution measurement algorithm is proposed for increasing the spatial resolution of surface displacements. Finally, an original intuitive strain conversion algorithm is developed for converting surface displacements into surface strains based on the principle similar to strain‐gauge transducers. The performance of SurfaceVision is first validated in the numerical simulation and further demonstrated in the experiment of the four‐point bending test. And a new method developed for predicting crack formations before they appear on structure surfaces, based on analyses of surface displacements/strains, is demonstrated. [ABSTRACT FROM AUTHOR]

Heavy vehicle weights need to be closely monitored for preventing fatigue-induced deterioration and critical fractures to highway infrastructure, among many other purposes, but development of a cost-effective weigh-in-motion (WIM) system remains challenging. This paper describes the creation and experimental validations of a computer vision-based non-contact WIM system. The underlining physics is that the force exerted by each tire onto the road is the product of the tire-road contact pressure and contact area. Computer vision is applied (1) to measure the tire deformation parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire inflation pressure can be found. In this research, a computer vision system is developed, which is comprised of a camera and computer vision software for measuring tire deformation parameters and recognizing the tire sidewall markings from images of individual tires of a moving vehicle. Computer vision techniques such as edge detection and optical character recognition are applied to enhance the measurement and recognition accuracy. Field experiments were conducted on fully loaded or empty concrete trucks and the truck weights estimated by this novel computer vision-based non-contact WIM system agreed well with the curb weights verified by static weighing. This research has demonstrated a novel application of the computer vision technology to solve a challenging vehicle WIM problem. Requiring no sensor installation on the roadway or the vehicle, this cost-effective non-contact computer vision system has demonstrated a great potential to be implemented. [ABSTRACT FROM AUTHOR]

Summary: Bridge infrastructure assets possess ultimate value for safe, resilient, and sustainable transportation networks. Monitoring of bridge structural characteristics is an essential process to minimize damage‐associated risk but requires expensive sensor instrumentation, manpower, and expert intervention. Besides, certain bridges' vitality exceeds practical needs due to their landmark identity with symbolic value. In this study, an economical and consumer‐grade‐distributed sensor array is utilized to determine dynamic characteristics of the Golden Gate Bridge, the most prominent landmark suspension bridge in the United States. The bridge is instrumented with multiple smartphones throughout the main and the side spans to collect vibration data without obstructing pedestrian or vehicle traffic. The accelerometer data collected under clock distribution are processed to retrieve modal frequencies and mode shapes of the bridge. Asynchronous and sampling‐deficient sensing approaches are adopted to extract the bridge modal characteristics despite the low vibration frequency and amplitude of the long‐span suspension bridge combined with limited sensing and acquisition quality of the smartphones. The findings show significant correlation with high‐fidelity reference instrumentations and present the largest‐scale civil infrastructure monitoring example utilizing smartphone technology. [ABSTRACT FROM AUTHOR]

The tiles that are used to protect the exterior of a building may separate from the concrete substrate and fall off as they age. To prevent accidents, a tiled wall can be inspected using various methods. In this study, nondestructive inspection (NDI) with microwave electromagnetic waves (EMWs) is used for detecting tile separation, and the chance of detecting the separation by this method is discussed in this paper. Here, the inspection apparatus is used to detect voids in concrete, including voids that simulate tile separation. An antenna attached to the apparatus transmits and receives microwaves to obtain a reflected intensity from the concrete. In addition, the distribution of reflected intensity is obtained using a proposed scanning method, multi-layered scanning. This involves several scans along the concrete surface at different antenna-to-surface distances. Typically, EMWs have to be analyzed with an expensive network analyzer in the time or frequency domain. However, we demonstrate that voids are detected from the obtained distribution of the reflected intensity of EMWs measured by a simple device without a network analyzer. The proposed method of NDI with EMWs is cheaper and simpler than conventional inspection methods. [ABSTRACT FROM AUTHOR]

With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective. [ABSTRACT FROM AUTHOR]

Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy. [ABSTRACT FROM AUTHOR]

Summary: An innovative substructure approach is proposed for estimating the structural parameters of shear structures from the acceleration responses of individual substructures. Two parallel methods are created to form single‐degree‐of‐freedom models of each substructure. The behavioral characteristics of these substructure models chiefly depend on the structural parameters of the edges of the component substructure, which is separate from the shear structure. To obtain structural parameters from the substructure accelerations, discrete substructure models with accelerations are generated using Newmark's method and are found similar to the autoregressive moving average with exogenous (ARMAX) inputs models. Sophisticated techniques for solving ARMAX models are used to process the accelerations and to extract the structural parameters of the substructures. A linear relationship among model coefficients of the discrete substructure models and ARMAX models is discovered that provides an accurate and simple way to identify all the substructure parameters. A numerical simulation of a 10‐story shear structure during earthquake is performed to verify this substructure approach, where the factors of the size of the substructure and the noise disturbance are considered. Finally, this substructure approach is used to identify a structural model and reproduce the structural responses of a five‐story three‐dimensional structure in a shaking‐table experiment. [ABSTRACT FROM AUTHOR]