Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
This addresses the critical challenge of efficiently managing aging infrastructure for entities like railway authorities, though it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of resource allocation for aging bridge portfolios by proposing Bayesian neural network surrogates to estimate structural analysis results with calibrated uncertainty, enabling fast triage and reducing costs and emissions in a real-world case study.
Aging infrastructure portfolios pose a critical resource allocation challenge: deciding which structures require intervention and which can safely remain in service. Structural assessments must balance the trade-off between cheaper, conservative analysis methods and accurate but costly simulations that do not scale portfolio-wide. 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.