A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

Integrating structural health monitoring (SHM) data into reliability assessment has increasingly been practiced in the condition evaluation of in-service bridges over the past decade. The selection of probability distribution models for load- and resistance-related random variables is a prerequisite for monitoring-based reliability assessment. However, the underlying probabilistic assumptions of the used models could be restrictive and unverifiable especially when dealing with real-world heterogeneous monitoring data, weakening the confidence on the estimated reliability index. This study aims to develop a nonparametric Bayesian model with the Dirichlet process prior for bridge reliability assessment, where the model order constraint can be released such that the complexity of the model adapts to the observed data. Reliability analysis via the nonparametric Bayesian model allows the aleatory uncertainty and the epistemic uncertainty arising from monitoring data to be concurrently accounted for in the formulated reliability index. A numerical example is presented to verify the effectiveness of the nonparametric Bayesian model for dealing with multimodal data. The feasibility of the proposed approach for reliability assessment is then demonstrated with one-year strain monitoring data acquired from a large-scale bridge instrumented with the SHM system.