Physics-Informed Machine Learning and Multi-Sensor Fusion for Structural Health Monitoring: A Bridge Case Study
TL;DR
Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements, and a supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support.
Abstract
Ensuring the integrity of critical infrastructure, such as bridges, dams, and large-scale structures, is essential to safety, reliability, and operational continuity. These assets are exposed to mechanical, thermal, environmental, and operational loads that can accelerate fatigue, corrosion, wear, and other deterioration mechanisms. Traditional inspections are periodic and costly, while sensor-based monitoring systems generate large volumes of imperfect field data that are difficult to interpret without engineering context. This paper presents a physics-informed machine learning and multi-sensor fusion framework for fatigue-oriented Structural Health Monitoring (SHM) and predictive maintenance. The method is demonstrated on an instrumented steel-concrete composite bridge using resistive strain, fiber Bragg grating (FBG) strain and temperature, and displacement measurements. In decision-critical SHM applications, uncertainty arises from noisy sensor signals, thermal effects, drift, missing data, and ambiguous operating events. The proposed framework reduces this uncertainty by applying physics and engineering principles to structure the correction, validation, and interpretation of sensor data. Machine learning supports thermal compensation, anomaly screening, cross-sensor substitution, and confidence tagging, while deterministic engineering methods remain responsible for strain interpretation, event extraction, rainflow-style cycle counting, and fatigue damage indicators. Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements. A supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support. SHM value does not come only from better sensors or better ML models. It comes from traceable workflows that turn imperfect field data into reliable, reviewable, and actionable engineering evidence.