Dynamic loads in disaster events, such as earthquakes, often cause structural damages that severely affect serviceability and even reduce the safety of civil structures or render them unsafe. Most of these damage scenarios can cause the transition from a linear to nonlinear behavior in structural dynamics. Therefore, extracting such nonlinear behaviors is an effective strategy for structural anomaly detection. In recent years, the rapid development of media devices has led to the widespread use of video data, primarily because of the ease of acquisition and lack of contact measurement. This study developed a novel method for detecting anomalies due to structural nonlinearity from video data that could capture target dynamic behaviors via non-contact measurement. The method is based on optical flow, extended repulsive force networks, and morphological operations for extracting singularities due to nonlinear events in the estimated motion vector field in video data. Subsequently, shaking table tests on a three-story shear building model containing a novel controllable hinge bearing verified the effectiveness of the developed method. The results of the feature enhancement process demonstrate the accurate localization of anomalous regions and effective mitigation of unwanted interference using the proposed method. The proposed method facilitates the use of video-based technology to evaluate rapid damage conditions post-earthquake.

中文翻译：

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使用视频数据通过非线性发生分析来检测结构动态系统中的异常


地震等灾害事件中的动态荷载常常会造成结构损坏，严重影响使用性能，甚至降低土木结构的安全性或使其不安全。大多数损坏场景都会导致结构动力学从线性行为转变为非线性行为。因此，提取此类非线性行为是结构异常检测的有效策略。近年来，媒体设备的快速发展导致了视频数据的广泛使用，这主要是因为视频数据易于获取且无需接触测量。这项研究开发了一种新方法，用于检测视频数据中结构非线性引起的异常，该方法可以通过非接触式测量捕获目标动态行为。该方法基于光流、扩展排斥力网络和形态学运算，用于提取由于视频数据中估计运动矢量场中的非线性事件而产生的奇点。随后，对包含新型可控铰链轴承的三层剪力建筑模型进行了振动台试验，验证了所开发方法的有效性。特征增强过程的结果证明了使用所提出的方法可以准确定位异常区域并有效减轻不需要的干扰。所提出的方法有助于使用基于视频的技术来评估震后的快速损坏情况。