Prediction and analysis of concrete compressive strength based on XGBoost and SHAP

Abstract

[Objective] To accurately predict the compressive strength of concrete, highlight the predictive advantages of the XGBoost model, and realize the interpretable function of the XGBoost model, [Methods] a data set of 1030 samples with eight factors such as cement, age, water and others as input features and compressive strength as target features is constructed, and machine learning algorithm models of Support Vector Regression(SVR), Random Forest(RF) and Extreme Gradient Boosting Tree(XGBoost) to research on concrete compressive strength prediction, comparing the prediction result of the XGBoost model and the ACI209 formula, and meanwhile, introducing the SHAP model to explain and analyze the XGBoost model. [Results] The result show that the XGBoost model has the highest prediction accuracy with R² of 0. 952, MAE of 2. 48, MAPE of 9. 16, and RMSE of 3. 58; however, the prediction error of the XGBoost model for low compressive strength samples less than 30 MPa is larger, and the prediction accuracy of the XGBoost model improves as the compressive strength increases, and the proportion of exceeding the limit samples decreases from 25% to 2. 7%; compared with the prediction result of ACI209 formula, the mean absolute error rate of the XGBoost model's prediction values for samples of age 56 d and 100 d are 4. 10% and 3. 64%, compared with 11. 27% and 17. 96% for ACI209 formula. [Conclusion] The XGBoost model is suitable for the prediction of samples with concrete strength greater than 30 MPa; The SHAP model can not only quantitatively give the ranking of feature importance, but also qualitatively give the influence of each feature parameter on compressive strength, which can provide a reference for concreterelated research and other studies that need to explain machine learning models.

Keywords

machine learning / XGBoost / SHAP / compressive strength prediction / concrete / mechanical properties