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- W4313703989 abstract "As a damming material, cemented sand and gravel (CSG) is proposed to predict the cleaving strength of CSG based on the eXtreme Gradient Boosting (XG-Boost) and Random forest algorithm (RF) in view of the relatively cumbersome test process, the great influence of human and other factors, and the relatively long test time. First, XG Boost model is used to select two variables with high correlation to splitting tensile strength in CSG mix proportion as input features for preliminary prediction, and the prediction results are substituted into the original features as new features, and together as input variables into the random forest model (RF). Taking R2, RMSE, MAE and MAPE as evaluation indexes, and comparing the prediction results with XG Boost model and RF model respectively, it proves that the proposed model is effective and accurate in predicting the splitting tensile strength of CSG, which provides a theoretical basis for engineering practice." @default.
- W4313703989 created "2023-01-08" @default.
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- W4313703989 date "2023-03-01" @default.
- W4313703989 modified "2023-10-01" @default.
- W4313703989 title "Prediction of CSG splitting tensile strength based on XGBoost-RF model" @default.
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- W4313703989 doi "https://doi.org/10.1016/j.mtcomm.2023.105350" @default.
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