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Advances in Concrete Construction Volume 17, Number 1, January 2024 , pages 27-36 DOI: https://doi.org/10.12989/acc.2024.17.1.027 |
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Optimized machine learning algorithms for predicting the punching shear capacity of RC flat slabs |
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Huajun Yan, Nan Xie and Dandan Shen
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| Abstract | ||
| Reinforced concrete (RC) flat slabs should be designed based on punching shear strength. As part of this study, machine learning (ML) algorithms were developed to accurately predict the punching shear strength of RC flat slabs without shear reinforcement. It is based on Bayesian optimization (BO), combined with four standard algorithms (Support vector regression, Decision trees, Random forests, Extreme gradient boosting) on 446 datasets that contain six design parameters. Furthermore, an analysis of feature importance is carried out by Shapley additive explanation (SHAP), in order to quantify the effect of design parameters on punching shear strength. According to the results, the BO method produces high prediction accuracy by selecting the optimal hyperparameters for each model. With R2 = 0.985, MAE = 0.0155 MN, RMSE = 0.0244 MN, the BO-XGBoost model performed better than the original XGBoost prediction, which had R2 = 0.917, MAE = 0.064 MN, RMSE = 0.121 MN in total dataset. Additionally, recommendations are provided on how to select factors that will influence punching shear resistance of RC flat slabs without shear reinforcement. | ||
| Key Words | ||
| Bayesian optimization; flat slabs; machine learning; punching shear strength | ||
| Address | ||
| (1) Huajun Yan, Nan Xie: School of Civil Engineering, Beijing Jiaotong University, Beijing, 100044, China; (2) Dandan Shen: SANY Heavy Industry Co., LTD, Beijing, 100044, China. | ||
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