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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]

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]

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]

Most of the existing bridge superstructure damage detection methods are based on acceleration or strain response due to ambient excitation. This paper proposes a bridge damage detection procedure that utilizes vehicle-induced displacement response without requiring prior knowledge about the traffic excitation and road surface roughness. This study is partially motivated by the recent advances in convenient measurement of structural displacements enabled by video-based sensors. Vehicle-bridge interaction analysis shows that when subjected to moving vehicles, the bridge displacement response is dominated by the first-order mode component. This justifies the proposed damage detection method that only requires the first-order mode shape curvature. While conventionally the mode shapes are extracted based on operational modal analysis, the first-order mode shape is extracted by directly analyzing the power spectral density functions of measured bridge displacement responses under vehicle excitations. Numerical simulations are carried out to investigate the feasibility and performance of the proposed damage detection method using three damage scenarios including damage at single, double, and multiple locations, each involving several extents of damage defined by the reduction in element stiffness. The results reveal that all the damage cases can be successfully identified. Furthermore, the damage detection performance is evaluated for cases involving different classes of road surface roughness and less measurement points. The study demonstrates the potential of the proposed model-free and time-efficient method for damage detection of bridge structures. Copyright © 2016 John Wiley & Sons, Ltd. [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]

Summary: Computer vision‐based displacement measurement techniques can be affected by the optical turbulence in the field in hot weather. The optical turbulence can be observed naturally when viewing a thick layer of heated air. The nonuniform density distribution of the air due to heat creates spatial variations in refraction indexes, resulting in distortions in video images and measurement errors in displacements obtained from distorted videos. The effect of optical turbulence on vision‐based displacement sensing is first illustrated in the field test conducted in hot weather. Then, the statistical characteristics of the optical‐turbulence‐induced measurement errors are analyzed and confirmed. And a comprehensive optical‐turbulence error alleviation technique, which has two steps, is designed based on the statistical studies. A previously developed high‐performance multitarget vision‐based displacement measurement technique is utilized in the first step to track multiple targets. Distortions of multiple targets are estimated simultaneously continuously. Targets with the least distortions in each frame are identified and their displacements are extracted as the primary displacement. To further alleviate errors in the primary displacement, an adaptive optical‐turbulence error filter is formulated in the second step based on the statistical characteristics of the optical‐turbulence errors. Validations of the optical‐turbulence error alleviation technique are performed in both laboratory and field tests. Field tests are conducted on the Williamsburg Bridge in the natural environment. After alleviating optical‐turbulence and camera vibration errors, frequency domain analyses are conducted on vibrational displacements of the Williamsburg Bridge in response to train passing. [ABSTRACT FROM AUTHOR]