AI-Enabled Multisensor Data Fusion Framework for Real-Time Structural Health Monitoring and Predictive Maintenance of Civil Infrastructure

Abstract

Aging civil infrastructure systems face increasing risks due to material degradation, excessive loading, and environmental exposure, necessitating reliable real-time monitoring and proactive maintenance strategies. Conventional inspection-based structural health monitoring approaches are limited by subjectivity, low inspection frequency, and delayed damage identification. This study proposes an artificial intelligence– enabled multisensor data fusion framework for real-time structural health monitoring and predictive maintenance of civil infrastructure. The framework integrates heterogeneous data acquired from accelerometers, strain gauges, acoustic emission sensors, and environmental sensors to enhance damage detection accuracy and robustness. Advanced machine learning models are employed for feature extraction, damage classification, and remaining useful life prediction under varying operational conditions. The proposed methodology is validated through numerical simulations and experimental investigations on reinforced concrete structural components subjected to progressive damage. Results demonstrate that multisensor data fusion significantly improves damage localization accuracy and early-stage crack detection compared to single-sensor systems. The predictive maintenance module successfully forecasts structural degradation trends, enabling condition-based maintenance planning. The findings indicate that AI-enabled multisensor monitoring systems provide a scalable and reliable solution for intelligent infrastructure management, supporting safety, resilience, and sustainability objectives in modern urban environments.