Publications

Hierarchical Bayesian modeling of highway bridge network extreme traffic loading

This study proposes a hierarchical Bayesian model that can estimate the traffic load effect of multiple bridges simultaneously, and subsequently create predictions for the remaining (unexamined) bridges within the road network. The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. This paper also presents a potential new strategy to reduce estimation uncertainty, and a method to predict parameters and return levels for bridges across an entire network made possible by the proposed hierarchical Bayesian model.

Akbar Rizqiansyah and Colin C. Caprani (2024), Structural Safety.

On the Upper Bound of the Distribution of Bridge Traffic Loading

This work argues that bridge traffic loading probability models should be bounded. A method based on Bayesian statistics incorporating the physical boundedness of traffic and limits based on engineering information is proposed. Diagnostic tools to detect misspecified engineering information are developed. Common causes of supposed observations of unbounded traffic load distributions in the literature are presented. The approach demonstrates a reduction in lifetime load estimation uncertainty and avoidance of physically impossible results, compared to the conventional unbounded bridge traffic load model.

Akbar Rizqiansyah and Colin C. Caprani (Under review), Reliability Engineering and System Safety