Abstract
This study presents an eXtreme Gradient Boosting (XGBoost) algorithm for predicting the torsional capacity of circular Concrete-Filled Double Skin Tubular (CFDST) steel columns under pure torsion. Utilizing a dataset of 806 columns generated through non-linear finite element analysis, the of XGBoost model outperforms existing empirical models with R² values of 99.5% (training) and 97.6% (testing). SHapley Additive exPlanations (SHAP) framework aided in interpreting predictions at both global and local levels. Key influencing variables include concrete compressive strength, outer steel tube yield strength, outer steel tube thickness, and inner steel tube thickness. The study highlights the effectiveness XGBoost as a promising alternative to traditional empirical models for accurate torsional capacity predictions in CFDST steel columns.
Original language | English |
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Title of host publication | Proceedings of Nordic Steel Construction Conference 2024 |
Publisher | The Swedish Institute of Steel Construction |
DOIs | |
Publication status | Published - 2024 |
Event | 15th Nordic Steel Construction Conference - Luleå, Sweden Duration: 26 Jun 2024 → 28 Jun 2024 https://nordicsteel2024.se/ |
Conference
Conference | 15th Nordic Steel Construction Conference |
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Country/Territory | Sweden |
City | Luleå |
Period | 26/06/24 → 28/06/24 |
Internet address |