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

Qi-Ang Wang1,2, Ao-Wen Lu2, Yi-Qing Ni3,4

  • 1State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China.

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Bayesian networks enhance structural health monitoring (SHM) for aging infrastructure by integrating diverse data for better damage prediction and risk assessment. This approach improves safety and reduces maintenance costs for civil engineering structures.

Keywords:
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Area of Science:

  • Civil Engineering
  • Computer Science
  • Data Science

Background:

  • Accelerated urbanization and aging infrastructure pose significant challenges to civil engineering structures.
  • Structural Health Monitoring (SHM) is crucial for ensuring the safety and durability of these structures.
  • Existing SHM methods face challenges in handling uncertainties and integrating multi-source data.

Purpose of the Study:

  • To systematically review the applications of Bayesian networks (BNs) in SHM.
  • To explore how BNs can address uncertainties and fuse multi-source data in SHM.
  • To unify the theoretical framework of BNs with practical SHM applications for infrastructure management.

Main Methods:

  • Systematic literature review of Bayesian network applications in SHM.
  • Analysis of BN's capabilities in damage prediction, data fusion, uncertainty modeling, and decision support.
  • Integration of probabilistic inference with multi-source sensor data.

Main Results:

  • Bayesian networks offer a robust probabilistic reasoning tool for SHM.
  • BNs enhance the accuracy and reliability of monitoring systems through data fusion and uncertainty handling.
  • The study provides a theoretical foundation for damage identification, risk early warning, and maintenance optimization.

Conclusions:

  • Bayesian networks represent a novel technological pathway for advancing SHM.
  • This research bridges the gap between probabilistic reasoning and real-world infrastructure management.
  • The outcomes have significant implications for reducing costs and ensuring public infrastructure safety.