Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review

Abstract

With accelerated urbanization and aging infrastructure, the safety and durability of civil engineering structures face significant challenges, making structural health monitoring (SHM) a critical approach to ensuring engineering safety. The Bayesian network, as a probabilistic reasoning tool, offers a novel technological pathway for SHM due to its strengths in handling uncertainties and multi-source data fusion. This study systematically reviews the core applications of the Bayesian network in SHM, including damage prediction, data fusion, uncertainty modeling, and decision support. By integrating multi-source sensor data with probabilistic inference, the Bayesian network enhances the accuracy and reliability of monitoring systems, providing a theoretical foundation for damage identification, risk early warning, and optimization of maintenance strategies. The study presents a comprehensive review that systematically unifies the theoretical framework of BN with SHM applications, addressing the gap between probabilistic reasoning and real-world infrastructure management. The research outcomes hold significant theoretical and engineering implications for advancing SHM technology development, reducing operational and maintenance costs, and ensuring the safety of public infrastructure.