Time series models have been popularly used in structural damage identification because the model coefficients and residual errors are sensitive to structural damages. As the direct relationship between the time series model coefficients (or residual errors) and damage severity is hard to be established, these methods provide limited information about the location or severity of damage. This study theoretically derives the sensitivity of the autoregressive coefficients of autoregressive moving average model to structural stiffness reduction factors. The autoregressive coefficients of both the damaged structure and the undamaged structure are extracted for damage identification. Structural damage identification is an inverse problem, and an underdetermined set of equations is a common obstacle encountered in solving such problem. Afterwards, sparse regularization is used to solve the underdetermined set of equations. The location and severity of damage is identified using the solution. The effectiveness of the proposed method is verified through a laboratory test of a cantilever beam and the Experimental Phase II IASC‐ASCE benchmark structure. In addition, the proposed time series analysis‐based method is promising to be used in online structural health monitoring systems.

中文翻译：

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使用时间序列分析和稀疏正则化进行损伤识别

时间序列模型已广泛用于结构损伤识别，因为模型系数和残差对结构损伤敏感。由于很难建立时间序列模型系数（或残差）与破坏严重性之间的直接关系，因此这些方法提供的有关破坏位置或严重性的信息有限。该研究从理论上推导了自回归移动平均模型的自回归系数对结构刚度减小因子的敏感性。提取受损结构和未受损结构的自回归系数以进行损伤识别。结构损伤识别是一个反问题，而方程组的欠定是解决该问题的常见障碍。之后，稀疏正则化用于求解欠定的方程组。使用解决方案可以确定损坏的位置和严重程度。通过对悬臂梁的实验室测试和实验第二阶段IASC-ASCE基准结构，验证了所提出方法的有效性。此外，提出的基于时间序列分析的方法有望用于在线结构健康监测系统。