Predictive Maintenance Of Bridge Structures Using Ai-driven Vibration Analysis And Sensor Data

Abstract

Bridge Structures Are Among The Most Crucial Components Of Civil Infrastructure, Often Bearing The Load Of Transportation Systems Across Cities And Regions. Their Failure Can Lead To Significant Economic Disruptions, Public Safety Hazards, And Loss Of Life. Traditionally, Bridge Health Monitoring (BHM) Has Been Carried Out Through Visual Inspections Or Scheduled Maintenance Cycles, Which Are Periodic, Expensive, And Reactive Rather Than Proactive. This Research Proposes An AI-driven Predictive Maintenance System Using Real-time Sensor Data, Particularly Vibration-based Signals, For Early Detection Of Structural Anomalies. By Integrating Accelerometers, Strain Gauges, And Tilt Sensors With Machine Learning Algorithms Such As Long Short-Term Memory (LSTM) Networks And Autoencoders, The System Can Identify Subtle Shifts In Bridge Behavior That Precede Failures. The Model Is Trained On Historical And Simulated Vibration Data To Detect Anomalies And Predict Degradation Patterns. A Decision-support Dashboard Visualizes Critical Parameters And Sends Automated Alerts For Early Intervention. This Approach Moves Beyond Reactive Maintenance To A Predictive Strategy That Ensures Safety, Extends Service Life, And Reduces Costs.

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