Health Monitoring of Outriggers of Tall Structures Under Lateral Force Using Machine Learning

Abstract

High-rise buildings face significant challenges in withstanding lateral forces, such as wind and seismic activity, which can cause excessive drift and vibrations. Outriggers, integral to stabilizing these structures, are particularly susceptible to stress under such forces. This paper presents a novel approach utilizing Machine Learning (ML) for the Structural Health Monitoring (SHM) of outrigger systems in tall buildings, with the aim of enhancing damage detection and long-term structural integrity predictions. The proposed ML framework uses data from sensor networks and focuses on key stress indicators, including average maximum stress (sig-max) and maximum shear stress (tau-max), to identify early signs of failure. Through the integration of advanced ML algorithms, such as Long Short-Term Memory (LSTM) networks, the study demonstrates the model's high predictive accuracy in forecasting displacements and stress levels under typical lateral loads. The framework offers a proactive approach for monitoring and maintaining structural health in high-rise buildings, reducing risks and optimizing maintenance schedules. By leveraging real-time sensor data, the proposed system supports a continuous evaluation process, contributing to the development of resilient urban infrastructure. This research underscores the potential of ML to revolutionize SHM practices, ensuring the safety and longevity of critical structural systems in tall buildings.