Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model

Bing Xu, Youcheng Tan, Weibang Sun, Tianxing Ma (), Hengyu Liu and Daguo Wang
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Bing Xu: School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
Youcheng Tan: School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
Weibang Sun: School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
Tianxing Ma: Ocean College, Zhejiang University, Zhoushan 316021, China
Hengyu Liu: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Daguo Wang: School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China

Sustainability, 2023, vol. 15, issue 6, 1-17

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 ( R n ), longitudinal wave velocity ( V p , m/s), and point load strength ( I s (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 ( R 2 ), mean absolute error (MAE), and variance interpretation (VAF). The results calculated by the SSA-XGBoost model ( R 2 = 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.

Keywords: UCS; prediction; empirical formulas; SSA-XGBoost model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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