A Novel Methodology for Structural Health Monitoring of Buildings Subjected to Earthquakes

Abstract

Recent advancements in sensor technology and data processing algorithms have revolutionized Structural Health Monitoring (SHM), enabling real-time monitoring and analysis of structural responses to dynamic loads. As a result, many buildings are permanently instrumented with sensors, typically accelerometers, to continuously record vibrational responses over time, hence generating huge amounts of monitoring data. However, the analysis and extraction of meaningful insights from the recorded data to assist engineers and building managers in assessing structural conditions would be a challenge. Monitoring systems can be programmed to record ground motion-induced vibrations that surpass specific trigger threshold levels. Nonetheless, there are challenges to long-term damage detection of buildings including automated analysis of previously recorded data, the limited number of available sensors, and nonlinear structural responses under severe earthquakes, to name but a few. In this paper, a new methodology based on adaptive time-series (TS) models for SHM and damage detection in buildings subjected to earthquakes is introduced to overcome these challenges. Using the proposed technique, automated analysis of a large set of previously recorded data and establishing a reliable baseline for the structure using a limited number of sensors, even as few as two accelerometers (one on the building and one on the ground) would be achievable. The efficiency of this method for monitoring large-scale structures instrumented with a limited number of sensors is verified using a 3-D Finite Element (FE) model of a 5-story reinforced concrete (RC) building using SAP2000 platform. Furthermore, experimental validations of the technique were implemented using acceleration datasets of a full-scale two-story post-tensioned concrete wall building tested over a shake table for damage assessments. The simulation results and experimental verifications demonstrated accurate identification of potential damage and provided a clear indication of damage progression as the severity of induced damage increases.