Damage diagnosis of high-rise buildings under variable ambient conditions using subdomain approach

Abstract

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.