Predictive structural assessment with Bayesian deep learning

Across Europe, bridges are reaching their planned service life, creating an urgent need for efficient structural assessments, as conventional methods prove time-consuming and costly. This research presents a data-driven framework for efficient structural pre-assessment of reinforced concrete frame bridges in the ultimate limit state. Developed based on the bridge inventory of the Swiss Federal Railways (SBB), a parametric, materially non-linear finite element analysis pipeline was created and harnessed to generate a simulation database. Bayesian Neural Networks were trained to predict structural code compliance factors with calibrated uncertainty estimates. The trained machine learning models enable a straightforward and fast pre-assessment of structural capacity. A real-world application on a railway underpass demonstrates the models’ ability to identify critical structures and provide decision support to asset owners and engineers. While the prototype focuses on railway frame bridges, the framework is transferable to other structure types, offering a scalable solution to prioritise maintenance interventions and optimise the allocation of limited resources. • Machine learning model for rapid structural pre-assessment with uncertainty estimates. • Parametric pipeline enables refined analysis of frame bridges and data generation. • Real-life case study on a railway underpass demonstrates practical applicability. • Scalable approach supports assessment prioritisation within infrastructure portfolios.