Tall structures, during their service lifetime, face many scenarios and are often prone to damages. Generally, static or dynamic measurements from the entire structure are used while formulating the Structural Health Monitoring (SHM) techniques for damage diagnosis. In this paper, an output-only damage diagnostic technique using the decentralized concept (subdomain-based) for high-rise buildings, employing the Vector form of the Autoregressive with exogenous input (VARX) model is developed. Vector version of the ARX model is preferred, as the vector models are more effective in detecting/localising the damage, when compared to the scalar models, due to their capability to predict the signals from a group of sensors per trial. In this work, the dynamic equation of motion associated with a building model is recast into the form of the VARX model, aiding to the decentralised damage diagnostic algorithm. New damage indices have been proposed to handle the inevitable confounding factors like environmental and operational variabilities (EoV), apart from measurement noise, to avoid false-positive alarms. The effectiveness of the proposed subdomain based damage diagnostic technique and its robustness to environmental/operational variabilities and measurement noise, are illustrated using the synthetic time-history responses of a 25-storey framed structure and the responses from a ten-storey experimental steel framed structure.

高大的结构在其使用寿命期间会面临许多情况，并且往往容易受到损坏。通常，在制定用于损伤诊断的结构健康监测 (SHM) 技术时，会使用来自整个结构的静态或动态测量。在本文中，开发了一种使用分散概念（基于子域）的高层建筑仅输出损伤诊断技术，采用自回归与外生输入 (VARX) 模型的向量形式。ARX 模型的矢量版本是首选，因为与标量模型相比，矢量模型在检测/定位损坏方面更有效，因为它们能够预测每次试验来自一组传感器的信号。在这项工作中，与建筑模型相关的动态运动方程被重铸为 VARX 模型的形式，辅助分散式损伤诊断算法。已经提出了新的损伤指数来处理不可避免的混杂因素，如环境和操作变化 (EoV)，除了测量噪声，以避免误报。使用 25 层框架结构的合成时程响应和 10 层实验钢框架结构的响应来说明所提出的基于子域的损伤诊断技术的有效性及其对环境/操作变化和测量噪声的鲁棒性.