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Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach
Highlights Explainable machine learning-based approach is developed for interface shear strength prediction of cold joints; The XGBoost model has high accuracy for interface shear strength prediction; The XGBoost model is superior over conventional empirical models; The most important input variable is the reinforcement ratio followed by the surface type.
Abstract Accurate prediction of the shear strength of the interface between old and new concrete (cold joints) is essential for the design or assessment of precast and retrofitted concrete structures. However, the shear mechanism of the interface under shear loading is complicated and many factors can affect the shear strength. The conventional empirical models developed based on specific and limited dataset cannot well predict the interface shear strength. This study adopts the machine learning-based approaches and Shapley Additive exPlanations technique to develop an explainable ML-model for interface shear strength prediction of the cold joints. A comprehensive interface shear strength database consisting of 217 cold joints with variant design attributes and two types of interfaces (rough and smooth) were developed. The eXtreme Gradient Boosting (XGBoost) algorithm was selected to develop the predictive model. The model performance of the XGBoost model was thoroughly compared with three additional ML-models (DT, RF, ANN) and six empirical models (ACI, AASHTO, CSA, Kahn and Mitchell, Patnaik, Mattock). Four quantitative measures (R2, RMSE, MAE, and MAPE) were utilized to evaluate the prediction accuracy and the results show that the XGBoost model has the best model performance among the four ML-models. Meanwhile the XGBoost model is superior to the empirical models. The most significant parameter affecting the predictions of the XGBoost model is the reinforcement ratio. The surface type, section width of the interface and concrete strength can significantly affect the shear strength.
Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach
Highlights Explainable machine learning-based approach is developed for interface shear strength prediction of cold joints; The XGBoost model has high accuracy for interface shear strength prediction; The XGBoost model is superior over conventional empirical models; The most important input variable is the reinforcement ratio followed by the surface type.
Abstract Accurate prediction of the shear strength of the interface between old and new concrete (cold joints) is essential for the design or assessment of precast and retrofitted concrete structures. However, the shear mechanism of the interface under shear loading is complicated and many factors can affect the shear strength. The conventional empirical models developed based on specific and limited dataset cannot well predict the interface shear strength. This study adopts the machine learning-based approaches and Shapley Additive exPlanations technique to develop an explainable ML-model for interface shear strength prediction of the cold joints. A comprehensive interface shear strength database consisting of 217 cold joints with variant design attributes and two types of interfaces (rough and smooth) were developed. The eXtreme Gradient Boosting (XGBoost) algorithm was selected to develop the predictive model. The model performance of the XGBoost model was thoroughly compared with three additional ML-models (DT, RF, ANN) and six empirical models (ACI, AASHTO, CSA, Kahn and Mitchell, Patnaik, Mattock). Four quantitative measures (R2, RMSE, MAE, and MAPE) were utilized to evaluate the prediction accuracy and the results show that the XGBoost model has the best model performance among the four ML-models. Meanwhile the XGBoost model is superior to the empirical models. The most significant parameter affecting the predictions of the XGBoost model is the reinforcement ratio. The surface type, section width of the interface and concrete strength can significantly affect the shear strength.
Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach
Xu, Ji-Gang (author) / Chen, Shi-Zhi (author) / Xu, Wei-Jie (author) / Shen, Zi-Sen (author)
2021-09-27
Article (Journal)
Electronic Resource
English
| BASE | 2021