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A platform for research: civil engineering, architecture and urbanism
Prediction of shear strength of RC deep beams based on interpretable machine learning
https://orcid.org/0000-0003-3320-1485
https://orcid.org/0000-0002-0716-2593
Highlights A data and mechanism co-driven model for predicting shear strength of RC deep beams. Six typical machine learning models and five mechanism models are selected and compared. An interpretable approach combined with shear mechanism is proposed. The contribution rates of different shear components are discussed in depth.
Abstract The purpose of this paper is to explore a data and mechanism co-driven model for predicting the shear strength of reinforced concrete (RC) deep beams. The established experimental database contains 457 RC deep beams with or without web reinforcements and 9 key input features are determined by the shear mechanism of the RC deep beam. Six typical machine-learning models and five mechanism models are selected and compared. The comparison results show that the XGBoost model performs well in terms of prediction accuracy and generalization ability (R2 = 0.992 and 0.917 in the training and testing sets, respectively). The XGBoost model is explained by the Shapley additive explanation (SHAP) approach and the proposed interpretable approach combined with the shear mechanism. This interpretable approach is proposed based on SHAP and the contribution rates of main shear components. It can be qualitatively proved that the results of the XGBoost model conform to shear mechanism based on SHAP feature importance and dependency. The interpretability of prediction results is further quantitatively confirmed by comparing the contribution rates of different shear components obtained from the proposed interpretable approach and two mechanism models. As can be concluded from the above, the proposed interpretable approach and the data and mechanism co-driven model can be recommended for similar shear issues of RC members.
Prediction of shear strength of RC deep beams based on interpretable machine learning
https://orcid.org/0000-0003-3320-1485
https://orcid.org/0000-0002-0716-2593
Highlights A data and mechanism co-driven model for predicting shear strength of RC deep beams. Six typical machine learning models and five mechanism models are selected and compared. An interpretable approach combined with shear mechanism is proposed. The contribution rates of different shear components are discussed in depth.
Abstract The purpose of this paper is to explore a data and mechanism co-driven model for predicting the shear strength of reinforced concrete (RC) deep beams. The established experimental database contains 457 RC deep beams with or without web reinforcements and 9 key input features are determined by the shear mechanism of the RC deep beam. Six typical machine-learning models and five mechanism models are selected and compared. The comparison results show that the XGBoost model performs well in terms of prediction accuracy and generalization ability (R2 = 0.992 and 0.917 in the training and testing sets, respectively). The XGBoost model is explained by the Shapley additive explanation (SHAP) approach and the proposed interpretable approach combined with the shear mechanism. This interpretable approach is proposed based on SHAP and the contribution rates of main shear components. It can be qualitatively proved that the results of the XGBoost model conform to shear mechanism based on SHAP feature importance and dependency. The interpretability of prediction results is further quantitatively confirmed by comparing the contribution rates of different shear components obtained from the proposed interpretable approach and two mechanism models. As can be concluded from the above, the proposed interpretable approach and the data and mechanism co-driven model can be recommended for similar shear issues of RC members.
Prediction of shear strength of RC deep beams based on interpretable machine learning
https://orcid.org/0000-0003-3320-1485
https://orcid.org/0000-0002-0716-2593
Highlights A data and mechanism co-driven model for predicting shear strength of RC deep beams. Six typical machine learning models and five mechanism models are selected and compared. An interpretable approach combined with shear mechanism is proposed. The contribution rates of different shear components are discussed in depth.
Abstract The purpose of this paper is to explore a data and mechanism co-driven model for predicting the shear strength of reinforced concrete (RC) deep beams. The established experimental database contains 457 RC deep beams with or without web reinforcements and 9 key input features are determined by the shear mechanism of the RC deep beam. Six typical machine-learning models and five mechanism models are selected and compared. The comparison results show that the XGBoost model performs well in terms of prediction accuracy and generalization ability (R2 = 0.992 and 0.917 in the training and testing sets, respectively). The XGBoost model is explained by the Shapley additive explanation (SHAP) approach and the proposed interpretable approach combined with the shear mechanism. This interpretable approach is proposed based on SHAP and the contribution rates of main shear components. It can be qualitatively proved that the results of the XGBoost model conform to shear mechanism based on SHAP feature importance and dependency. The interpretability of prediction results is further quantitatively confirmed by comparing the contribution rates of different shear components obtained from the proposed interpretable approach and two mechanism models. As can be concluded from the above, the proposed interpretable approach and the data and mechanism co-driven model can be recommended for similar shear issues of RC members.
Prediction of shear strength of RC deep beams based on interpretable machine learning
Ma, Cailong (author) / Wang, Sixuan (author) / Zhao, Jianping (author) / Xiao, Xufeng (author) / Xie, Chenxi (author) / Feng, Xinlong (author)
2023-05-01
Article (Journal)
Electronic Resource
English