Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges
Bridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure accep...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/4/70 |
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| author | Javier Grandío Brais Barros Manuel Cabaleiro Belén Riveiro |
| author_facet | Javier Grandío Brais Barros Manuel Cabaleiro Belén Riveiro |
| author_sort | Javier Grandío |
| collection | DOAJ |
| description | Bridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure acceptable levels of safety and service. Finite element (FE) models are traditionally used to reliably assess structural health, but their computational expense often prevents their extensive use in routine bridge assessments. To overcome this computational limitation, this paper presents an innovative deep learning-based surrogate model for predicting local displacements in bridge structures. By utilizing point cloud data and transformer neural networks, the model provides fast and accurate predictions of displacements, addressing the limitations of traditional methods. A case study of a historical bridge demonstrates the model’s efficiency. The proposed approach integrates spatial data processing techniques, offering a computationally efficient alternative for bridge health monitoring. Our results show that the model achieves mean absolute errors below 0.0213 mm, drastically reducing the time required for structural analysis. |
| format | Article |
| id | doaj-art-84bf2a05bdcd4ed0a2674c98ed069fba |
| institution | OA Journals |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-84bf2a05bdcd4ed0a2674c98ed069fba2025-08-20T02:18:05ZengMDPI AGInfrastructures2412-38112025-03-011047010.3390/infrastructures10040070Point Transformer Network-Based Surrogate Model for Spatial Prediction in BridgesJavier Grandío0Brais Barros1Manuel Cabaleiro2Belén Riveiro3CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, SpainICITECH, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, SpainCINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, SpainCINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, SpainBridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure acceptable levels of safety and service. Finite element (FE) models are traditionally used to reliably assess structural health, but their computational expense often prevents their extensive use in routine bridge assessments. To overcome this computational limitation, this paper presents an innovative deep learning-based surrogate model for predicting local displacements in bridge structures. By utilizing point cloud data and transformer neural networks, the model provides fast and accurate predictions of displacements, addressing the limitations of traditional methods. A case study of a historical bridge demonstrates the model’s efficiency. The proposed approach integrates spatial data processing techniques, offering a computationally efficient alternative for bridge health monitoring. Our results show that the model achieves mean absolute errors below 0.0213 mm, drastically reducing the time required for structural analysis.https://www.mdpi.com/2412-3811/10/4/70infrastructuredeep learningfinite element modellingsurrogate modellingsteel bridge |
| spellingShingle | Javier Grandío Brais Barros Manuel Cabaleiro Belén Riveiro Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges Infrastructures infrastructure deep learning finite element modelling surrogate modelling steel bridge |
| title | Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges |
| title_full | Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges |
| title_fullStr | Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges |
| title_full_unstemmed | Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges |
| title_short | Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges |
| title_sort | point transformer network based surrogate model for spatial prediction in bridges |
| topic | infrastructure deep learning finite element modelling surrogate modelling steel bridge |
| url | https://www.mdpi.com/2412-3811/10/4/70 |
| work_keys_str_mv | AT javiergrandio pointtransformernetworkbasedsurrogatemodelforspatialpredictioninbridges AT braisbarros pointtransformernetworkbasedsurrogatemodelforspatialpredictioninbridges AT manuelcabaleiro pointtransformernetworkbasedsurrogatemodelforspatialpredictioninbridges AT belenriveiro pointtransformernetworkbasedsurrogatemodelforspatialpredictioninbridges |