Use of Advanced Sensors and Data Analysis Techniques for Structural Health Monitoring

Abstract

Structural health monitoring (SHM) is crucial for ensuring the safety, longevity, and performance of infrastructures such as bridges, buildings, and dams. Traditional inspection methods often fall short in detecting earlystage structural damage, leading to costly repairs and safety risks. With advancements in sensor technology and data analysis techniques, SHM has transformed into a proactive system capable of real-time monitoring, early damage detection, and predictive maintenance. This paper explores the use of advanced sensors, such as fiber optics, accelerometers, and strain gauges, alongside sophisticated data analysis methods, including machine learning and signal processing, to enhance SHM systems. Case studies are presented to highlight the effectiveness of these technologies in improving structural integrity assessments. The paper also discusses challenges, future trends, and the potential impact of these technologies on infrastructure management.