What question did this study set out to answer?

This research aims to develop a framework for discretizing Bayesian network variables for enhanced structural reliability assessment.

July 3, 2026Open Access

A Limit-State-Surface-Based Discretization of Bayesian Network Variables for Structural System Reliability Assessment

Key Points

This research aims to develop a framework for discretizing Bayesian network variables for enhanced structural reliability assessment.
Proposes a discretization framework for continuous random variables in Bayesian networks.
Identifies important points for reliability estimation on limit state surfaces.
Demonstrates the approach through a benchmark problem and frame structure example.
Achieves accurate failure probability estimation with limited discrete states.
Shows effective sensitivity analysis and posterior inference under multiple collapse modes.

Abstract

This paper proposes a discretization framework for Bayesian network–based structural reliability analysis involving continuous random variables. The method identiﬁes important points for reliability estimation, including design points on limit state surfaces, and discretizes continuous random variables such that the estimation accuracy is maintained in the vicinity of these points. The proposed approach enables accurate and stable estimation of system failure probability under a limited number of discrete states. Its eﬀectiveness is demonstrated through a benchmark problem and a frame structure example with multiple collapse modes, including sensitivity analysis and posterior inference conditioned on collapse.

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