Application of Artificial Intelligence and Machine Learning in Structural Health Monitoring: A Proactive Approach to Infrastructure Safety

Abstract

Structural Health Monitoring (SHM) plays a crucial role in ensuring the safety, reliability, and longevity of civil infrastructures such as bridges, buildings, and tunnels. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), the field of SHM is experiencing a paradigm shift from reactive to proactive maintenance. This paper explores various AI and ML techniques—such as supervised and unsupervised learning, deep learning, and reinforcement learning—for identifying, classifying, and predicting structural damages. It presents the integration of sensor networks, data acquisition systems, and intelligent algorithms to establish automated and continuous monitoring. The paper aims to serve as a guide for researchers and practitioners interested in advancing SHM through intelligent systems.

Keywords: Structural Health Monitoring, Artificial Intelligence, Machine Learning, Predictive Maintenance, Infrastructure Safety, Deep Learning, Sensor Networks