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A robust method for bridge safety risk assessment using improved multi-state fuzzy Bayesian network.

Zhong Cao1, Weicong He1, Kaihong Chen1

  • 1School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China.

Scientific Reports
|August 20, 2025
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Summary
This summary is machine-generated.

This study introduces a Multi-state Fuzzy Bayesian Network (MFBN) for robust bridge safety risk assessment. The method enhances traditional models by incorporating multi-state nodes and expert reliability for improved risk management strategies.

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Area of Science:

  • Civil Engineering
  • Risk Management
  • Artificial Intelligence

Background:

  • Bridge safety is critical, requiring robust risk assessment methods.
  • Traditional risk models often lack the granularity to capture complex failure modes.
  • Evaluating bridge safety necessitates incorporating expert knowledge and uncertainty.

Purpose of the Study:

  • To propose a novel Multi-state Fuzzy Bayesian Network (MFBN) for comprehensive bridge safety risk evaluation.
  • To enhance existing risk assessment frameworks by integrating multi-state node categorization and expert reliability.
  • To provide a foundation for effective bridge risk control and management strategies.

Main Methods:

  • Construction of a bridge collapse fault tree and a directed acyclic graph to map causal relationships.
  • Categorization of bridge components into three states (e.g., normal, warning, failure) instead of binary states.
  • Development of multi-state fuzzy conditional probability tables using expert judgment, reliability levels, and confidence indices.
  • Application of an improved similarity aggregation method, incorporating bridge age, to consolidate expert opinions and determine root node risk probabilities.

Main Results:

  • The MFBN method effectively assesses bridge safety risk levels by integrating prior knowledge and evidence.
  • Identification of critical nodes contributing to elevated safety risks.
  • Demonstrated effectiveness and robustness through application to two urban bridges.

Conclusions:

  • The proposed MFBN method offers a valuable decision-making tool for bridge safety risk management.
  • The approach provides a more nuanced and reliable evaluation of bridge safety compared to traditional methods.
  • This framework supports proactive risk mitigation and enhances overall infrastructure safety.