Nowadays, there is a considerable effort to develop technologies for smart cities. Smart buildings are a critical component of such smart cities, and their automated structural health monitoring is essential. This paper presents a new, efficient, and robust methodology for automated structural damage detection of shear‐type buildings. The proposed method uses output‐only acceleration response to separately detect changes in stiffness and mass using adaptive zero‐phase component analysis (AZCA) in conjunction with time series analysis, that is, autoregressive moving average models with exogenous inputs (ARMAX). In our efforts to tackle the effects of operational factors on structural damage detection processes, herein, mass changes are differentiated from structural damage. Assuming the mass at one DOF at any location is constant (a priori knowledge about the location is not needed), changes in the ARMAX model coefficients are then employed to build stiffness change features (SCFs) and mass change features (MCFs) from which changes in mass and stiffness can be detected separately. A four‐story shear structure was constructed in the laboratory to experimentally validate the proposed methodology. The experiment results demonstrate that the approach is successful in eliminating mass effect to determine the existence, location, and severity of the structural damage accurately.

中文翻译：

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使用时间序列模型和自适应零相位分量分析在多个刚度和质量变化下的结构损伤检测

如今，人们为开发智慧城市的技术付出了巨大的努力。智能建筑是此类智能城市的重要组成部分，其自动化结构健康监控至关重要。本文介绍了一种用于剪切型建筑物的自动结构损伤检测的新的，有效的和健壮的方法。所提出的方法使用仅输出的加速度响应，通过自适应零相位分量分析（AZCA）结合时间序列分析，即具有外生输入的自回归移动平均模型（ARMAX），分别检测刚度和质量的变化。为了解决操作因素对结构损伤检测过程的影响，本文将质量变化与结构损伤区分开来。假设一个自由度在任何位置的质量都是恒定的（不需要有关该位置的先验知识），然后采用ARMAX模型系数的变化来构建刚度变化特征（SCF）和质量变化特征（MCF），从中进行变化质量和刚度可以分别检测。在实验室中构造了一个四层的剪切结构，以通过实验验证了所提出的方法。实验结果表明，该方法成功消除了质量效应，可以准确地确定结构损伤的存在，位置和严重程度。在实验室中构造了一个四层的剪切结构，以通过实验验证了所提出的方法。实验结果表明，该方法成功消除了质量效应，可以准确地确定结构损伤的存在，位置和严重程度。在实验室中构造了一个四层的剪切结构，以通过实验验证了所提出的方法。实验结果表明，该方法成功消除了质量效应，可以准确地确定结构损伤的存在，位置和严重程度。