Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios

Sophia V. Kuhn, Rafael Bischof, Marius Weber, Antoine Binggeli, Michael Kraus, Walter Kaufmann, Fernando Perez-Cruz

Keywords: Bayesian neural networks, Surrogate modeling, Uncertainty quantification, Structural engineering, Decision support, Infrastructure portfolios, Non-linear finite element analysis

TL;DR: Bayesian neural network surrogates enable fast, uncertainty-aware assessment of aging bridge portfolios, supporting scalable triage decisions from limited available structural information.

Abstract:

Aging infrastructure portfolios pose a critical resource allocation challenge: deciding which structures require intervention and which can safely remain in service.
Today’s structural assessment approaches do not scale to portfolio level, as they require time-consuming manual digital modeling and computationally expensive simulations for each individual structure.
We propose Bayesian neural network (BNN) surrogates for rapid structural pre-assessment of worldwide common bridge types, such as reinforced concrete frame bridges. Trained on a large-scale database of non-linear finite element analyses generated via a parametric pipeline and developed based on the Swiss Federal Railway's bridge portfolio, the models accurately and efficiently estimate high-fidelity structural analysis results by predicting code compliance factors with calibrated epistemic uncertainty.
Our BNN surrogate enables fast, uncertainty-aware triage: flagging likely critical structures and providing guidance where refined analysis is pertinent.
We demonstrate the framework's effectiveness in a real-world case study of a railway underpass, showing its potential to significantly reduce costs and emissions by avoiding unnecessary analyses and physical interventions across entire infrastructure portfolios.

Submission Number: 238