Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques
Abstract This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-77396-5 |
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| author | Hazem Samih Mohamed Tang Qiong Haytham F. Isleem Rupesh Kumar Tipu Ramy I. Shahin Saad A. Yehia Pradeep Jangir Arpita Mohammad Khishe |
| author_facet | Hazem Samih Mohamed Tang Qiong Haytham F. Isleem Rupesh Kumar Tipu Ramy I. Shahin Saad A. Yehia Pradeep Jangir Arpita Mohammad Khishe |
| author_sort | Hazem Samih Mohamed |
| collection | DOAJ |
| description | Abstract This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns. These techniques include Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), MLP Regressor (MLPR), K-nearest Neighbours Regressor (KNNR), and Naive Bayes Regressor (NBR). ML models accuracy is assessed by comparing their predictions with FE results. Among the models, GBR and XGBR exhibited outstanding results with high test R2 scores of 0.9888 and 0.9885, respectively. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP indicate that the eccentric loading ratio (e/2a) has the most significant effect on the load-carrying capacity of elliptical CFST short columns, followed by the yield strength of the outer steel tube ( $$\:{f}_{yo}$$ ) and the inner width of the inner steel tube ( $$\:2{a}_{ii}$$ ). Additionally, a user interface platform has been developed to streamline the practical application of the proposed ML. |
| format | Article |
| id | doaj-art-8cccb0ed2b92496d8f801696c191554d |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8cccb0ed2b92496d8f801696c191554d2025-08-20T02:13:24ZengNature PortfolioScientific Reports2045-23222024-11-0114113110.1038/s41598-024-77396-5Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniquesHazem Samih Mohamed0Tang Qiong1Haytham F. Isleem2Rupesh Kumar Tipu3Ramy I. Shahin4Saad A. Yehia5Pradeep Jangir6Arpita7Mohammad Khishe8College of Transportation and Civil Engineering, Fujian Agriculture and Forestry UniversitySchool of Applied Technologies, Qujing Normal UniversityJadara Research Center, Jadara UniversityDepartment of Civil Engineering, School of Engineering & Technology, K. R. Mangalam UniversityDepartment of Civil Engineering, School of Engineering & Technology, K. R. Mangalam UniversityDepartment of Civil Engineering, Higher Institute of Engineering and TechnologyUniversity Centre for Research and Development, Chandigarh UniversityDepartment of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical SciencesDepartment of Electrical Engineering, Imam Khomeini Naval Science University of NowshahrAbstract This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns. These techniques include Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), MLP Regressor (MLPR), K-nearest Neighbours Regressor (KNNR), and Naive Bayes Regressor (NBR). ML models accuracy is assessed by comparing their predictions with FE results. Among the models, GBR and XGBR exhibited outstanding results with high test R2 scores of 0.9888 and 0.9885, respectively. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP indicate that the eccentric loading ratio (e/2a) has the most significant effect on the load-carrying capacity of elliptical CFST short columns, followed by the yield strength of the outer steel tube ( $$\:{f}_{yo}$$ ) and the inner width of the inner steel tube ( $$\:2{a}_{ii}$$ ). Additionally, a user interface platform has been developed to streamline the practical application of the proposed ML.https://doi.org/10.1038/s41598-024-77396-5Elliptical concrete-filled steel tubularLoad-carrying capacityEccentric loadingMachine learning modelsXG-Boost model |
| spellingShingle | Hazem Samih Mohamed Tang Qiong Haytham F. Isleem Rupesh Kumar Tipu Ramy I. Shahin Saad A. Yehia Pradeep Jangir Arpita Mohammad Khishe Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques Scientific Reports Elliptical concrete-filled steel tubular Load-carrying capacity Eccentric loading Machine learning models XG-Boost model |
| title | Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques |
| title_full | Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques |
| title_fullStr | Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques |
| title_full_unstemmed | Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques |
| title_short | Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques |
| title_sort | compressive behavior of elliptical concrete filled steel tubular short columns using numerical investigation and machine learning techniques |
| topic | Elliptical concrete-filled steel tubular Load-carrying capacity Eccentric loading Machine learning models XG-Boost model |
| url | https://doi.org/10.1038/s41598-024-77396-5 |
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