1. Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model
- Author
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Bing Xu, Youcheng Tan, Weibang Sun, Tianxing Ma, Hengyu Liu, and Daguo Wang
- Subjects
Renewable Energy, Sustainability and the Environment ,Geography, Planning and Development ,UCS ,prediction ,empirical formulas ,SSA-XGBoost model ,Building and Construction ,Management, Monitoring, Policy and Law - Abstract
The uniaxial compressive strength of rock is one of the important parameters characterizing the properties of rock masses in geotechnical engineering. To quickly and accurately predict the uniaxial compressive strength of rock, a new SSA-XGBoost optimizer prediction model was produced to predict the uniaxial compressive strength of 290 rock samples. With four parameters, namely, porosity (n,%), Schmidt rebound number (Rn), longitudinal wave velocity (Vp, m/s), and point load strength (Is(50), MPa) as input variables and uniaxial compressive strength (UCS, MPa) as the output variables, a prediction model of uniaxial compressive strength was built based on the SSA-XGBoost model. To verify the effectiveness of the SSA-XGBoost model, empirical formulas, XGBoost, SVM, RF, BPNN, KNN, PLSR, and other models were also established and compared with the SSA-XGBoost model. All models were evaluated using the root mean square error (RMSE), correlation coefficient (R2), mean absolute error (MAE), and variance interpretation (VAF). The results calculated by the SSA-XGBoost model (R2 = 0.84, RMSE = 19.85, MAE = 14.79, and VAF = 81.36), are the best among all prediction models. Therefore, the SSA-XGBoost model is the best model to predict the uniaxial compressive strength of rock, for the dataset tested.
- Published
- 2023