Application of machine learning models in the capacity prediction of RCFST columns
Abstract Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48044-1 |
| _version_ | 1797415275821268992 |
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| author | Khaled Megahed Nabil Said Mahmoud Saad Elden Mostafa Abd-Rabou |
| author_facet | Khaled Megahed Nabil Said Mahmoud Saad Elden Mostafa Abd-Rabou |
| author_sort | Khaled Megahed |
| collection | DOAJ |
| description | Abstract Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength. |
| first_indexed | 2024-03-09T05:46:28Z |
| format | Article |
| id | doaj.art-b0fbb0f3d3ab4451b91d56bcc37c3081 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T05:46:28Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b0fbb0f3d3ab4451b91d56bcc37c30812023-12-03T12:21:13ZengNature PortfolioScientific Reports2045-23222023-11-0113111310.1038/s41598-023-48044-1Application of machine learning models in the capacity prediction of RCFST columnsKhaled Megahed0Nabil Said Mahmoud1Saad Elden Mostafa Abd-Rabou2Department of Structural Engineering, Mansoura UniversityDepartment of Structural Engineering, Mansoura UniversityDepartment of Structural Engineering, Mansoura UniversityAbstract Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength.https://doi.org/10.1038/s41598-023-48044-1 |
| spellingShingle | Khaled Megahed Nabil Said Mahmoud Saad Elden Mostafa Abd-Rabou Application of machine learning models in the capacity prediction of RCFST columns Scientific Reports |
| title | Application of machine learning models in the capacity prediction of RCFST columns |
| title_full | Application of machine learning models in the capacity prediction of RCFST columns |
| title_fullStr | Application of machine learning models in the capacity prediction of RCFST columns |
| title_full_unstemmed | Application of machine learning models in the capacity prediction of RCFST columns |
| title_short | Application of machine learning models in the capacity prediction of RCFST columns |
| title_sort | application of machine learning models in the capacity prediction of rcfst columns |
| url | https://doi.org/10.1038/s41598-023-48044-1 |
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