Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929289656532992 |
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| author | Bull, Lawrence A. Jeon, Chiho Girolami, Mark Duncan, Andrew Schooling, Jennifer Haro, Miguel Bravo |
| author_facet | Bull, Lawrence A. Jeon, Chiho Girolami, Mark Duncan, Andrew Schooling, Jennifer Haro, Miguel Bravo |
| contents | We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commuter train. The measurements are treated as a longitudinal dataset and represented with a (low-rank approximation) hierarchical Gaussian process. For each unit in the combined model, we encode domain expertise as boundary condition constraints and work towards a general representation of the strain response. Looking forward, this should allow for the simulation of train types that were previously unobserved in the training data. For example, trains with more passengers or freights with a heavier payload. The strain event simulations are valuable since they can inform further experiments (including FEM calibration, fatigue analysis, or design) to test the bridge in hypothesised scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17820 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge Bull, Lawrence A. Jeon, Chiho Girolami, Mark Duncan, Andrew Schooling, Jennifer Haro, Miguel Bravo Applications Machine Learning We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commuter train. The measurements are treated as a longitudinal dataset and represented with a (low-rank approximation) hierarchical Gaussian process. For each unit in the combined model, we encode domain expertise as boundary condition constraints and work towards a general representation of the strain response. Looking forward, this should allow for the simulation of train types that were previously unobserved in the training data. For example, trains with more passengers or freights with a heavier payload. The strain event simulations are valuable since they can inform further experiments (including FEM calibration, fatigue analysis, or design) to test the bridge in hypothesised scenarios. |
| title | Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2403.17820 |