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

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 12; p. 3577
• Main Authors: Wang, Qi-Ang, Lu, Ao-Wen, Ni, Yi-Qing, Wang, Jun-Fang, Ma, Zhan-Guo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.06.2025; MDPI
• Subjects: Bayesian network; Civil engineering; damage prediction; data fusion; Decision making; Graph representations; Infrastructure; Random variables; Review; Risk assessment; Sensors; structural health monitoring; uncertainty; United States; China; damage prediction; data fusion; structural health monitoring; uncertainty; Bayesian network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25123577

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.