Computer Vision for Structural Dynamics and Health Monitoring. Dongming Feng

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Computer Vision for Structural Dynamics and Health Monitoring - Dongming Feng


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authors would like to express their gratitude to the following individuals: Professors Shun’ichi Kaneko and Takayuki Tanaka at Hokkaido University, for inspiring the authors' work on computer vision more than a decade ago and for kindly providing the orientation code matching (OCM) MATLAB code included in Chapter 2; Dr. Yoshio Fukuda, former associate research scientist at Columbia University, for developing the OCM software package with the C++ language; Casey Megan Eckersley, PhD student of Columbia University, for her valuable assistance in editing the book; and last but not least, the authors' families for their strong support.

      The companion website for this book is at

      www.wiley.com/go/feng/structuralhealthmonitoring

      The website includes: MATLAB CODES for chapters 2, 4, 6, 7 and appendix_matlab codes.

      Scan this QR code to visit the companion website.

      1.1 Structural Health Monitoring: A Quick Review

      Structures and civil infrastructure systems, including bridges, buildings, dams, and pipelines, are exposed to various external loads throughout their lifetimes. As they age and deteriorate, effective inspection, monitoring, and maintenance of these systems becomes increasingly important. However, conventional practice based on periodic human visual inspection is time‐consuming, labor‐intensive, subjective, and prone to human error. Nondestructive testing techniques have shown potential for detecting hidden damages, but the large size of the structural systems presents a significant challenge for conducting such localized tests. Over the past few decades, a significant number of studies have been conducted in the area of structural health monitoring (SHM), aiming at timely, objective detection of damage or anomalies and quantitative assessment of structural integrity and safety based on measurements by various on‐structure sensors [1–4]. Most of the SHM techniques are based on structural dynamics, and the basic principle is that any structural damage or degradation would result in changes in structural dynamic responses as well as the corresponding modal characteristics. The SHM process is implemented in four key steps: data acquisition, system identification, condition assessment, and decision‐making.

      Time‐domain SHM techniques, rather than working with modal quantities, directly utilize measured structural response time histories to identify structural parameters. The identification in the time domain is often formulated as an optimization process, wherein the objective function is defined as the discrepancy between the measured and predicted responses. In the majority of existing studies, which are referred to as input–output methods, the known or measured excitation forces are a prerequisite for obtaining the predicted structural responses. However, it is highly difficult to measure excitation forces such as vehicle loads on bridges. Recently, there have been attempts to simultaneously identify both structural parameters and input forces from output‐only identification formulations. For example, Rahneshin and Chierichetti [10] proposed an iterative algorithm – the extended load confluence algorithm – to predict dynamic structural responses in which limited or no information about the applied loads is available. Xu et al. [11] presented a weighted adaptive iterative least‐squares estimation method to identify structural parameters and dynamic input loadings from incomplete measurements. Sun and Betti [12] demonstrated the effectiveness of a hybrid heuristic optimization strategy for simultaneous identification of structural parameters and input loads via three numerical examples. Feng et al. [13] proposed a numerical methodology to simultaneously identify bridge structural parameters and moving vehicle axle load histories from a limited number of acceleration measurements.

      For both frequency‐ and time‐domain methods, vibration‐based SHM strategies have proved effective in evaluating the global health state of structures and performing a rapid risk assessment. However, their wide deployment in realistic engineering structures is limited by the prohibitive requirement of installing dense on‐structure sensor networks (primarily accelerometers) and associated data‐acquisition systems. Contact‐type wired sensors require time‐consuming, labor‐intensive installation and costly maintenance for successful long‐term monitoring, which poses many economic and practical challenges. Although wireless sensor technology has addressed several limitations of wired sensors by eliminating cumbersome wiring, data acquisition remains challenging due to the complexity of data transmission, time synchronization, and power consumption, especially when hundreds of wireless sensors are mounted on a large‐scale structure to measure dynamic responses. Moreover, one main bottleneck is that conventional on‐structure sensors provide sparse, discrete point‐wise measurements and thus low spatial‐sensing resolutions, which limits the effectiveness of SHM on a large‐scale structure. Although such a sensor network with a limited number of sensors may allow for the detection of changes in overall structural dynamics, it is often insufficient for identifying the location or assessing the extent of damage.

      To address these practical limitations, the research and engineering practitioner communities have been actively exploring new sensor technologies that can advance the current state of SHM practice. This book introduces the emerging computer vision–based sensor technology.


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