Failure Evaluation of Bridge Deck Based on Parallel Connection Bayesian Network: Analytical Model.

Area of Science:

• Structural Engineering
• Reliability Engineering
• Computational Mechanics

Background:

• Bridge deterioration significantly impacts serviceability, with fatigue failure being a primary concern.
• Bayesian networks are increasingly utilized for probabilistic fatigue failure analysis in structures.
• Existing methods may not fully capture system-level and component-level fatigue interactions.

Purpose of the Study:

• To propose a system-level fatigue reliability evaluation model for bridge decks using Bayesian networks.
• To develop a fatigue probability reliability model for bridge decks based on the master S-N curve.
• To demonstrate the model's applicability through numerical case studies.

Main Methods:

• Development of a Bayesian network model for bridge deck fatigue reliability.
• Derivation of a fatigue probability reliability model using the master S-N curve.
• Application of the Monte Carlo method to solve complex analytical expressions within the Bayesian network.

Main Results:

• A novel system-level fatigue reliability model for bridge decks was successfully developed.
• The model effectively integrates probabilistic approaches for fatigue assessment.
• Numerical case studies confirmed the model's practical applicability and effectiveness.

Conclusions:

• The proposed Bayesian network model offers a robust framework for evaluating bridge deck fatigue reliability.
• This approach enhances the understanding of fatigue failure mechanisms at both system and component levels.
• The study provides a valuable tool for ensuring the long-term serviceability of long span bridges.