Prediction of shear capacity of steel channel sections using machine learning algorithms
This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 238 results of carbon steel LiteSteel sections were...
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| Published in: | Thin-walled structures Vol. 175; p. 109152 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.06.2022
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| ISSN: | 0263-8231, 1879-3223 |
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| Abstract | This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 238 results of carbon steel LiteSteel sections were gathered to train machine learning models including support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGB). The cross-validation with 10 folds has been conducted in the training process to avoid over-fitting. The optimal hyperparameter combinations for each machine learning model were found during the hyperparameter tuning process and four performance indicators were used to evaluate the performance of the trained models. The comparison results suggest that all four implemented machine learning models reliably predict the shear capacity of both stainless steel lipped channel sections and carbon steel LiteSteel sections while the implemented SVR algorithm is found to be the best performing model. Moreover, it is shown that the implemented machine learning models exceed the prediction accuracy of the available design equations in estimating the shear capacity of steel channel sections.
•Optimal hyperparameter combinations were found for SVR, MLP, GBR and XGB models.•Each machine learning model was evaluated based on four performance indicators.•The implemented SVR algorithm is proved to be the best performing model.•Four implemented models perform better than the available design equations. |
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| AbstractList | This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 238 results of carbon steel LiteSteel sections were gathered to train machine learning models including support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGB). The cross-validation with 10 folds has been conducted in the training process to avoid over-fitting. The optimal hyperparameter combinations for each machine learning model were found during the hyperparameter tuning process and four performance indicators were used to evaluate the performance of the trained models. The comparison results suggest that all four implemented machine learning models reliably predict the shear capacity of both stainless steel lipped channel sections and carbon steel LiteSteel sections while the implemented SVR algorithm is found to be the best performing model. Moreover, it is shown that the implemented machine learning models exceed the prediction accuracy of the available design equations in estimating the shear capacity of steel channel sections.
•Optimal hyperparameter combinations were found for SVR, MLP, GBR and XGB models.•Each machine learning model was evaluated based on four performance indicators.•The implemented SVR algorithm is proved to be the best performing model.•Four implemented models perform better than the available design equations. |
| ArticleNumber | 109152 |
| Author | Nguyen, Hoang Perampalam, Gatheeshgar Rajanayagam, Heshachanaa Dissanayake, Madhushan Poologanathan, Keerthan Upasiri, Irindu Suntharalingam, Thadshajini |
| Author_xml | – sequence: 1 givenname: Madhushan surname: Dissanayake fullname: Dissanayake, Madhushan organization: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom – sequence: 2 givenname: Hoang surname: Nguyen fullname: Nguyen, Hoang organization: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom – sequence: 3 givenname: Keerthan surname: Poologanathan fullname: Poologanathan, Keerthan organization: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom – sequence: 4 givenname: Gatheeshgar surname: Perampalam fullname: Perampalam, Gatheeshgar organization: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom – sequence: 5 givenname: Irindu surname: Upasiri fullname: Upasiri, Irindu email: irinduupasiri@sjp.ac.lk organization: Faculty of Engineering, University of Sri Jayewardenepura, Ratmalana, Sri Lanka – sequence: 6 givenname: Heshachanaa surname: Rajanayagam fullname: Rajanayagam, Heshachanaa organization: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom – sequence: 7 givenname: Thadshajini surname: Suntharalingam fullname: Suntharalingam, Thadshajini organization: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom |
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| SubjectTerms | Channel sections Design rules Machine learning Shear capacity |
| Title | Prediction of shear capacity of steel channel sections using machine learning algorithms |
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