Transformer-Based Deep Learning Model for Bored Pile Load-Deformation Prediction in Bangkok Subsoil
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917626687520768 |
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| author | Youwai, Sompote Thongnoo, Chissanupong |
| author_facet | Youwai, Sompote Thongnoo, Chissanupong |
| contents | This paper presents a novel deep learning model based on the transformer architecture to predict the load-deformation behavior of large bored piles in Bangkok subsoil. The model encodes the soil profile and pile features as tokenization input, and generates the load-deformation curve as output. The model also incorporates the previous sequential data of load-deformation curve into the decoder to improve the prediction accuracy. The model also incorporates the previous sequential data of load-deformation curve into the decoder. The model shows a satisfactory accuracy and generalization ability for the load-deformation curve prediction, with a mean absolute error of 5.72% for the test data. The model could also be used for parametric analysis and design optimization of piles under different soil and pile conditions, pile cross section, pile length and type of pile. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03041 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Deep Learning Model for Bored Pile Load-Deformation Prediction in Bangkok Subsoil Youwai, Sompote Thongnoo, Chissanupong Machine Learning Computational Engineering, Finance, and Science This paper presents a novel deep learning model based on the transformer architecture to predict the load-deformation behavior of large bored piles in Bangkok subsoil. The model encodes the soil profile and pile features as tokenization input, and generates the load-deformation curve as output. The model also incorporates the previous sequential data of load-deformation curve into the decoder to improve the prediction accuracy. The model also incorporates the previous sequential data of load-deformation curve into the decoder. The model shows a satisfactory accuracy and generalization ability for the load-deformation curve prediction, with a mean absolute error of 5.72% for the test data. The model could also be used for parametric analysis and design optimization of piles under different soil and pile conditions, pile cross section, pile length and type of pile. |
| title | Transformer-Based Deep Learning Model for Bored Pile Load-Deformation Prediction in Bangkok Subsoil |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2312.03041 |