Optimization Of Structural Health Monitoring For Steel Bridges Using Wireless Sensor Networks and Machine Learning Algorithms

Abstract

This study presents an advanced structural health monitoring (SHM) system for steel bridges based on wireless sensor networks (WSN) integrated with machine learning algorithms. The proposed system monitors and predicts structural integrity under various load conditions. The research focuses on developing a machine learning model capable of real-time anomaly detection, allowing for early warnings of potential failures. Experimental results from both simulation and field tests demonstrate the system’s effectiveness in prolonging bridge lifespan while reducing maintenance costs.