Machine-learning-assisted design of high strength steel I-section columns
High strength steel has been attracting attention in the building industry due to its superior mechanical properties. The accurate design of high strength steel structures is crucial to boost its wide application. In this paper, an accurate and unified design approach for high strength steel I-secti...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179387 |
| _version_ | 1824453713510531072 |
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| author | Cheng, Jinpeng Li, Xuelai Jiang, Ke Li, Shuai Su, Andi Zhao, Ou |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Cheng, Jinpeng Li, Xuelai Jiang, Ke Li, Shuai Su, Andi Zhao, Ou |
| author_sort | Cheng, Jinpeng |
| collection | NTU |
| description | High strength steel has been attracting attention in the building industry due to its superior mechanical properties. The accurate design of high strength steel structures is crucial to boost its wide application. In this paper, an accurate and unified design approach for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions (including cross-section sizes and member lengths) and failure modes is proposed based on machine learning. Firstly, 871 experimental and numerical data were collected from the literature to establish a database. Then, seven machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Adaptive Boosting, Extreme Gradient Boosting and Categorical Boosting, were applied to establish machine learning regression models to predict buckling resistances of high strength steel I-section columns. The model performance was then evaluated through statistic indices, with the evaluation results indicating that the Categorical Boosting trained model yields the highest level of accuracy. Based on the data in the collected database, the regression model trained by Categorical Boosting and existing codified design provisions, as given in the European code and American specification, were assessed and compared. The European code and American specification were found to yield scattered and inaccurate failure load predictions, while the Categorical Boosting trained model led to substantially more accurate and consistent failure load predictions for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions and failure modes. |
| first_indexed | 2025-02-19T03:10:48Z |
| format | Journal Article |
| id | ntu-10356/179387 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:10:48Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1793872024-07-29T06:12:26Z Machine-learning-assisted design of high strength steel I-section columns Cheng, Jinpeng Li, Xuelai Jiang, Ke Li, Shuai Su, Andi Zhao, Ou School of Civil and Environmental Engineering Engineering Buckling Columns High strength steel has been attracting attention in the building industry due to its superior mechanical properties. The accurate design of high strength steel structures is crucial to boost its wide application. In this paper, an accurate and unified design approach for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions (including cross-section sizes and member lengths) and failure modes is proposed based on machine learning. Firstly, 871 experimental and numerical data were collected from the literature to establish a database. Then, seven machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Adaptive Boosting, Extreme Gradient Boosting and Categorical Boosting, were applied to establish machine learning regression models to predict buckling resistances of high strength steel I-section columns. The model performance was then evaluated through statistic indices, with the evaluation results indicating that the Categorical Boosting trained model yields the highest level of accuracy. Based on the data in the collected database, the regression model trained by Categorical Boosting and existing codified design provisions, as given in the European code and American specification, were assessed and compared. The European code and American specification were found to yield scattered and inaccurate failure load predictions, while the Categorical Boosting trained model led to substantially more accurate and consistent failure load predictions for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions and failure modes. 2024-07-29T06:12:26Z 2024-07-29T06:12:26Z 2024 Journal Article Cheng, J., Li, X., Jiang, K., Li, S., Su, A. & Zhao, O. (2024). Machine-learning-assisted design of high strength steel I-section columns. Engineering Structures, 308, 118018-. https://dx.doi.org/10.1016/j.engstruct.2024.118018 0141-0296 https://hdl.handle.net/10356/179387 10.1016/j.engstruct.2024.118018 2-s2.0-85190719516 308 118018 en Engineering Structures © 2024 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering Buckling Columns Cheng, Jinpeng Li, Xuelai Jiang, Ke Li, Shuai Su, Andi Zhao, Ou Machine-learning-assisted design of high strength steel I-section columns |
| title | Machine-learning-assisted design of high strength steel I-section columns |
| title_full | Machine-learning-assisted design of high strength steel I-section columns |
| title_fullStr | Machine-learning-assisted design of high strength steel I-section columns |
| title_full_unstemmed | Machine-learning-assisted design of high strength steel I-section columns |
| title_short | Machine-learning-assisted design of high strength steel I-section columns |
| title_sort | machine learning assisted design of high strength steel i section columns |
| topic | Engineering Buckling Columns |
| url | https://hdl.handle.net/10356/179387 |
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