Search

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]

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]

To address the limitations of current sensor systems for field applications, the research community has been actively exploring new technologies that can advance the state-of-the-practice in structural health monitoring (SHM). Thanks to the rapid advances in computer vision, the camera-based noncontact vision sensor has emerged as a promising alternative to conventional contact sensors for structural dynamic response measurement and health monitoring. Significant advantages of the vision sensor include its low cost, ease of setup and operation, and flexibility to extract displacements of any points on the structure from a single video measurement. This review paper is intended to summarize the collective experience that the research community has gained from the recent development and validation of the vision-based sensors for structural dynamic response measurement and SHM. General principles of the vision sensor systems are firstly presented by reviewing different template matching techniques for tracking targets, coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements, measurements by tracking artificial targets vs. natural targets, measurements in real time vs. by post-processing, etc. Then the paper reviews laboratory and filed experimentations carried out to evaluate the performance of the vision sensors, followed by a discussion on measurement error sources and mitigation methods. Finally, applications of the measured displacement data for SHM are reviewed, including examples of structural modal property identification, structural model updating, damage detection, and cable force estimation. [ABSTRACT FROM AUTHOR]

Uncertainty of modal parameters estimation appear in structural health monitoring (SHM) practice of civil engineering to quite some significant extent due to environmental influences and modeling errors. Reasonable methodologies are needed for processing the uncertainty. Bayesian inference can provide a promising and feasible identification solution for the purpose of SHM. However, there are relatively few researches on the application of Bayesian spectral method in the modal identification using SHM data sets. To extract modal parameters from large data sets collected by SHM system, the Bayesian spectral density algorithm was applied to address the uncertainty of mode extraction from output-only response of a long-span suspension bridge. The posterior most possible values of modal parameters and their uncertainties were estimated through Bayesian inference. A long-term variation and statistical analysis was performed using the sensor data sets collected from the SHM system of the suspension bridge over a one-year period. The t location-scale distribution was shown to be a better candidate function for frequencies of lower modes. On the other hand, the burr distribution provided the best fitting to the higher modes which are sensitive to the temperature. In addition, wind-induced variation of modal parameters was also investigated. It was observed that both the damping ratios and modal forces increased during the period of typhoon excitations. Meanwhile, the modal damping ratios exhibit significant correlation with the spectral intensities of the corresponding modal forces. [ABSTRACT FROM AUTHOR]

The emerging noncontact vision-based displacement sensor system offers a promising alternative to the conventional sensors for quantitative structural integrity assessment. Significant advantages of the noncontact vision-based sensor include its low cost, ease of operation, and flexibility to extract structural displacement responses at multiple points. This study aims to link the measured displacement data to the quantification of the structural health condition, by validating the feasibility of simultaneous identification of structural stiffness and unknown excitation forces in time domain using output-only vision-based displacement measurement. Numerical analysis are first carried out to investigate the accuracy, convergence and robustness of identified results to different noise levels, sensor numbers, and initial estimates of structural parameters. Then, experiment on a laboratory scaled beam structure is conducted. Results show that the global stiffness of the beam specimen as well as external hammer excitation forces can be successfully and accurately identified from displacement measurement at two points using one camera. The proposed output-only time-domain identification procedure utilizing vision-based displacement measurement represents a low-cost method for either periodic or long-term bridge performance assessment. [ABSTRACT FROM AUTHOR]

Monitoring structural displacement responses can provide quantitative information for both structural safety evaluations and maintenance purposes. To overcome the limitations of conventional displacement sensors, advanced noncontact vision-based systems offer a promising alternative. This study validates the potentials of the vision displacement sensor for cost-effective structural health monitoring. The results of laboratory experiments on simply-supported beam structures demonstrate the high accuracy of the vision sensor for dense full-field displacement measurements. The identified natural frequencies and mode shapes from measurements by using one camera match well with those from an array of accelerometers. Moreover, the smoother mode shapes make possible the noncontact damage detection based on the conventional mode shape curvature index. This study also discusses the issues concerning the practical applications of the vision displacement sensors, such as the scaling factor determination, measurement with small camera tilt angles, tradeoffs between the measurement resolution and measurement points or field of view, etc. Furthermore, the remote, real-time and multi-point measurement capacities of the vision sensor are confirmed through field tests of Manhattan Bridge during train passing. [ABSTRACT FROM AUTHOR]

Future structural health monitoring systems are evolving toward crowdsourced, autonomous, sustainable forms based on which damage-indicative structural features can be identified. Unlike conventional sensor systems, they serve as non-stationary, mobile, and distributed sensor network components. For example, smartphone sensors carried by pedestrians decouple from the structure of interest, making it difficult to measure structural vibration. Taking bridges as instances, smartphone sensor data contain not only the bridge vibration but also the pedestrians' biomechanical features. In this article, pedestrians' smartphone data are used to conduct force estimation and modal identification for structural health monitoring purposes. Two major pedestrian activities, walking and standing, are adopted to estimate walk-induced forces on structures and identify modal parameters, respectively. First, vibration time history of a walking pedestrian combined with pedestrian weight is a measure of dynamic forces imposed on the structure. Second, standing pedestrian's smartphone sensors provide spectral peaks which are mixtures of structural and biomechanical vibrations. Eliminating biomechanical content reveals structural modal properties which are sensitive to structural integrity. This study presents the first structural health monitoring application recruiting pedestrians in a testbed bridge monitoring example. Orchestrating pervasive and participatory pedestrian data might bring new frontiers to structural health monitoring through a smart, mobile, and urban sensing framework. [ABSTRACT FROM AUTHOR]

A novel noncontact vision sensor for simultaneous measurement of structural displacements at multiple points using one camera is developed based on two advanced template matching techniques: the upsampled cross correlation (UCC) and the orientation code matching (OCM). While existing studies on vision sensors are mostly focused on the time-domain performance evaluation, this study investigates the performance in both time and frequency domains through a shaking table test of a three-story frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Excellent agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. The results of structural modal analysis based on the measurements by the vision sensor and reference accelerometers also agree well. Moreover, the identified modal parameters are used to update the finite element model of the structure, demonstrating the potential of the vision sensor for structural health monitoring applications. This study further examines the robustness of the proposed vision sensor against ill environmental conditions such as dim light, background image disturbance, and partial template occlusion, which is important for future implementation in the field. Significant advantages of the proposed vision sensor include its low cost (a single camera to remotely measure structural displacements at multiple points without installing artificial targets) and flexibility to extract structural displacements at any point from a single measurement. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. The ubiquity of smartphones has provided an opportunity to create low-cost sensor networks for SHM. Crowdsourcing has given rise to citizen initiatives becoming a vast source of inexpensive, valuable but heterogeneous data. Previously, the authors have investigated the reliability of smartphone accelerometers for vibration-based SHM. This paper takes a step further to integrate mobile sensing and web-based computing for a prospective crowdsourcing-based SHM platform. An iOS application was developed to enable citizens to measure structural vibration and upload the data to a server with smartphones. A web-based platform was developed to collect and process the data automatically and store the processed data, such as modal properties of the structure, for long-term SHM purposes. Finally, the integrated mobile and web-based platforms were tested to collect the low-amplitude ambient vibration data of a bridge structure. Possible sources of uncertainties related to citizens were investigated, including the phone location, coupling conditions, and sampling duration. The field test results showed that the vibration data acquired by smartphones operated by citizens without expertise are useful for identifying structural modal properties with high accuracy. This platform can be further developed into an automated, smart, sustainable, cost-free system for long-term monitoring of structural integrity of spatially distributed urban infrastructure. Citizen Sensors for SHM will be a novel participatory sensing platform in the way that it offers hybrid solutions to transitional crowdsourcing parameters. [ABSTRACT FROM AUTHOR]

Determination of response characteristics using probabilistic approaches is essential to deal with high level of load and resistance uncertainties in civil engineering structures. Multi-span bridges present a significant problem regarding the varying nature of seismic loads on different bridge piers. With an emphasis on comparison of multi-support excitation with the conventional uniform excitation, this paper aims at providing a framework to evaluate probabilistic seismic response and estimate reliability of bridges under multi-support excitations simulated by multivariate stochastic processes. Moreover, the framework integrates the experimental data of a multi-support seismic shaking table test of a multi-span bridge structure as well as structural health monitoring (SHM) findings based on vibration measurements. The results demonstrate the importance of the multivariate stochastic processes, therefore, multi-support excitation, on estimating seismic behavior of multi-span bridges. Furthermore, this study proves the significance of structural parameter updating with respect to the evaluation of structural reliability. It is observed that the difference between the SHM-integrated and the conventional reliability estimation results vary according to the change in bedrock conditions. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recent advances in the Micro Electro-Mechanical System (MEMS) technology have made wireless MEMS accelerometers an attractive tool for Structural Health Monitoring (SHM) of civil engineering structures. To date, sensors' low sensitivity and accuracy--especially at very low frequencies--have imposed serious limitations for their application in monitoring large-sized structures. Conventionally, the MEMS sensor's analog signals are converted to digital signals before radio-frequency (RF) wireless transmission. The conversion can cause a low sensitivity to the important low-frequency and low-amplitude signals. To overcome this difficulty, the authors have developed a MEMS accelerometer system, which converts the sensor output voltage to a frequency-modulated signal before RF transmission. This is achieved by using a Voltage to Frequency Conversion (V/F) instead of the conventional Analog to Digital Conversion (ADC). In this paper, a prototype MEMS accelerometer system is presented, which consists of a transmitter and receiver circuit boards. The former is equipped with a MEMS accelerometer, a V/F converter and a wireless RF transmitter, while the latter contains an RF receiver and a F/V converter for demodulating the signal. The efficacy of the MEMS accelerometer system in measuring low-frequency and low-amplitude dynamic responses is demonstrated through extensive laboratory tests and experiments on a flow-loop pipeline. [ABSTRACT FROM AUTHOR]

In this study, the useful application of an instrumented structural health monitoring (SHM) system is proposed for the reliable seismic performance evaluation based on measured response data. A seismic fragility is chosen as a key index for probabilistic seismic performance assessment on an infrastructure. The seismic performance evaluation procedure consists of the following five main steps; (1) measuring ambient vibration of a bridge under general traveling vehicles; (2) identifying modal parameters including natural frequencies and mode shapes from the measured acceleration data by output-only modal identification method; (3) updating linear structural parameters in a preliminary finite element (FE) model using the identified modal parameters; (4) analyzing nonlinear response time histories of the structure using nonlinear seismic analysis program; and finally (5) evaluating the probabilistic seismic performance in terms of seismic fragility. In the present study, the seismic fragility curves are represented by a log-normal distribution function. An instrumented highway bridge is utilized to demonstrate the proposed evaluation procedure and it is found that the seismic fragility of a highway bridge can be reliably evaluated by combining the modal information obtained from the instrumented SHM system and FE model updating by using the information. [ABSTRACT FROM AUTHOR]

• Validated necessity of tackling heat haze in vision-based displacement sensor. • Proposed a image distortion estimation method for heat haze-affected frames. • Developed an improved inclusive and explicit heat haze error model. • Proposed and validated a heat haze detection method. • Conducted experiments with simulated heat haze and field tests on a bridge. Computer vision has become widely applied for structural displacement monitoring. However, heat haze is one of the major challenges. Image distortions caused by heat haze in hot weather can result in displacement errors. Therefore, a comprehensive study of properties of heat haze-induced distortions and displacement errors is conducted. Firstly, an image distortion estimation method is proposed for estimating heat haze-induced image distortions. Secondly, displacement errors due to heat haze are analyzed. A heat haze error model is formulated to describe the properties of heat haze errors, and the explicit effect of the environmental factor of temperature on the heat haze error model. Thirdly, a heat haze detection method is proposed to enable detection of heat haze's influence on vision-based displacement sensors by extracting features from distortion measurements and applying a classification algorithm. Field tests in hot weather and experiments with dark heaters for introducing heat haze are conducted for validations. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue including the structure health monitoring, computational algorithms, and the innovative applications of computers.

This paper presents a new computer vision-based method that simultaneously provides the moving vehicle's tire loads, the location of the loads on a bridge, and the bridge's response displacements, based on which the bridge's influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system. The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the co-authors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads. The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements. The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system's modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge's displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring. • Non-contact computer vision-based methodology for extracting displacement influence lines. • Simultaneous tracking of the bridge displacement, location and magnitude of the moving vehicle loads. • Experimental validation with a laboratory bridge system and pressurized vehicle model. • Excellent match between the simulated and experimental influence lines after model updating. [ABSTRACT FROM AUTHOR]

Abstract: Capital investment in national infrastructure is significant. The need to maintain and protect critical infrastructure links has led in recent years to significant developments in the area of structural health monitoring. The objective is to track a structure’s long-term performance, typically using sensors, and to successively compare the most recently measured responses with prior response history. During construction of the West Street On-Ramp, a curved concrete box girder bridge, located in the city of Anaheim (California), eleven accelerometers were permanently installed on its bridge deck. The associated data acquisition system was configured to record once a specified threshold acceleration response was exceeded; during the period 2002–2010 a total of 1350 datasets including six earthquakes, for each of the eleven sensors, were acquired. This automatically acquired data was supplemented, during the summer of 2009, with responses measured during controlled vehicle tests. Six accelerometers were additionally installed on the frame of the weighed test vehicle. This paper presents the findings of the analyses of these measured data sets and serves to inform owners and managers as to the potential feedback from their instrumentation investment. All response histories were analyzed using frequency domain techniques for system identification. Extraction of the modal characteristics revealed a continuous reduction, of approximately 5%, in the first three natural frequencies over the period of the study. The measured responses from the vehicle sensors are discussed in the context of identifying the potential for bridge frequency measurement using instrumented vehicles. [Copyright &y& Elsevier]

• Energy distribution characteristics of displacement and acceleration are revealed. • The estimated covariances on modal flexibility are used for comparison. • Lower uncertainty is obtained for modal parameters when using displacement. • Displacement response is more suitable for structural flexibility identification. For civil engineering structures, the dynamics-based structural health monitoring is commonly based on inverse analysis of structural acceleration response data to identify the structural parameters. As the measurement of structural displacement response becomes more feasible and cost-effective thanks to advances in computer vision techniques, a comparison of the displacement and acceleration response data for structural identification has become of considerable interest among researchers. This paper presents a comprehensive analysis and comparison of structural flexibility identification based on displacement versus acceleration response data through uncertainty quantification technique. Firstly, theoretical derivation reveals that the energy spectral density of acceleration response is equal to the fourth power of the frequency of the energy spectral density of displacement response, which affects the distribution of measurement noise across different frequency bands. Secondly, the uncertainty quantification method for modal flexibility from subspace-based system identification is utilized to compare the derivation process of covariance estimation on modal flexibility. Finally, numerical analysis and experimental example are carried out to compare the structural flexibility identification results when using the displacement versus acceleration. It shows that more precise identification results of lower-order modal parameters are obtained when using displacement, which further improves the precision of modal flexibility identification. The displacement is more suitable for identifying the modal flexibility when the measurement noises are at the same level. [ABSTRACT FROM AUTHOR]

Neural network-based strategies require balanced training datasets to avoid creating unreliable classification and prediction models. While these strategies are commonly used to model the dynamics of structural and mechanical systems, the imbalanced composition of monitoring data is a fundamental challenge for damage assessment in structural systems. The monitoring data often contain abundant observations from structures in their normal operating conditions (undamaged state) and small and partial information from systems in the damaged state. Therefore, the model, trained by adopting deep learning approaches, tends to show an ill-conditioned nature, limited to specific structures in a narrow range of damage conditions. The current study presents a damage assessment strategy that overcomes the limitations of unbalanced datasets. To improve the model's ability to distinguish between different health conditions, informative features are utilized to facilitate the differentiation of multiple classes according to the frequency content of vibration signals. The model acquires this ability by learning from a rich dataset of human voices (source domain), where low-level features that denote the vibration traits of human waveforms are extracted. Subsequently, this knowledge is transferred to the features of a target domain that has limited data for damage detection. The proposed methodology relies on creating an informative feature extractor training a Time-Delay Neural Network (TDNN) using a collection of human voice recordings. Cepstral and pitch features derived from the speech data are used as input features for the TDNN. This network is used to derive low-level features at intermediate layers of the network, called " x -vectors". These features store non-case-dependent information about the frequency content of the signals and depict the ability to distinguish between different classes according to a change in the frequency content of the investigated system. This is not a unique attribute of the original audio source domain, and it can be employed to help differentiate categories for any vibrating system where a modification in the frequency content is representative of a transition between classes, including the structural and mechanical systems. Because of the generalization trait of the x -vector, they can be employed to construct a Probabilistic Linear Discriminant Analysis model able to classify various damage classes considering vibration measurements obtained from a different system, i.e. , a structural system (target domain). Initially, the simulated acceleration response from the 12-degree of freedom structure are analyzed to affirm the effectiveness of the framework. Then, the method is further validated by using the field data of the Z24 bridge, to evaluate its reliability in real-world applications. [ABSTRACT FROM AUTHOR]