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Steel and Composite Structures Volume 37, Number 2, October25 2020 , pages 193-209 DOI: https://doi.org/10.12989/scs.2020.37.2.193 |
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Comparison of machine learning algorithms for regression and classification of ultimate load-carrying capacity of steel frames |
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Seung-Eock Kim, Quang-Viet Vu, George Papazafeiropoulos, Zhengyi Kong and Viet-Hung Truong
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| Abstract | ||
| In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered. | ||
| Key Words | ||
| gradient boosting; random forest; deep learning; support vector machine; nonlinear inelastic; steel frame | ||
| Address | ||
| Seung-Eock Kim: Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea Quang-Viet Vu: Faculty of Civil Engineering, Vietnam Maritime University, 484 Lach Tray Street, Haiphong city, Vietnam George Papazafeiropoulos: Department of Structural Engineering, National Technical University of Athens, Zografou, Athens 15780, Greece Zhengyi Kong: School of Civil Engineering and Architecture, Anhui University of Technology, Ma'anshan 243032, China Viet-Hung Truong: Faculty of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam | ||