Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909987224158208 |
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| author | Kuhn, Sophia V. Bischof, Rafael Weber, Marius Binggeli, Antoine Kraus, Michael A. Kaufmann, Walter Pérez-Cruz, Fernando |
| author_facet | Kuhn, Sophia V. Bischof, Rafael Weber, Marius Binggeli, Antoine Kraus, Michael A. Kaufmann, Walter Pérez-Cruz, Fernando |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios Kuhn, Sophia V. Bischof, Rafael Weber, Marius Binggeli, Antoine Kraus, Michael A. Kaufmann, Walter Pérez-Cruz, Fernando Machine Learning 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. |
| title | Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.25031 |