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About this paper
Sophia V. Kuhn, Rafael Bischof, Marius Weber, Antoine Binggeli, Michael A. Kraus, Walter Kaufmann, Fernando Pérez-Cruz
The authors develop a Bayesian neural-network surrogate to estimate bridge code-compliance factors and uncertainty from nonlinear finite-element analysis (NLFEA) simulations. Its uncertainty-aware triage policy recommends structures for refined analysis rather than certifying safety. In one Swiss railway underpass, NLFEA confirmed compliance and reserve capacity; SBB estimated about $370,000 saved by avoiding intervention. This is a single-structure case.
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