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Steel and Composite Structures Volume 51, Number 6, June 25 2024 , pages 679-695 DOI: https://doi.org/10.12989/scs.2024.51.6.679 |
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Hybrid machine learning with moth-flame optimization methods for strength prediction of CFDST columns under compression |
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Quang-Viet Vu, Dai-Nhan Le, Thai-Hoan Pham, Wei Gao and Sawekchai Tangaramvong
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| Abstract | ||
| This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise. | ||
| Key Words | ||
| categorical gradient boosting; CFDST columns; machine learning; moth-flame optimization; SHapley Additive exPlanations | ||
| Address | ||
| Quang-Viet Vu:1)Laboratory for Computational Civil Engineering, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam 2)Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University, Bangkok 10330, Thailand Dai-Nhan Le:Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, Vietnam Thai-Hoan Pham:Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, Vietnam Wei Gao:Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia Sawekchai Tangaramvong:Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University, Bangkok 10330, Thailand | ||