Prediction Method of Rock Uniaxial Compressive Strength Based on Feature Optimization and SSA-XGBoost

Huihui Xie, Peng Lin (), Jintao Kang, Chenyu Zhai and Yuchao Du ()
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Huihui Xie: Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China
Peng Lin: Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China
Jintao Kang: Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China
Chenyu Zhai: Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China
Yuchao Du: Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China

Sustainability, 2024, vol. 16, issue 19, 1-19

Abstract: In order to establish an optimal model for reasonably predicting the uniaxial compressive strength (UCS) of rocks, a method based on feature optimization and SSA-XGBoost was proposed. Firstly, the UCS predictor system of rocks, considering petrographic and physical parameters, was determined based on the systematic discussion of the factors affecting the UCS of rocks. Then, a feature selection method combining the RReliefF algorithm and Pearson correlation coefficient was proposed to further determine the optional input features. The XGBoost algorithm was used to establish the prediction model for rock UCS. In the process of model training, the Sparrow Search Algorithm (SSA) was used to optimize the hyperparameters. Finally, model evaluation was carried out to test the performance of the UCS prediction model. The method was applied and validated in a granitic tunnel. The results show that the proposed UCS prediction model can effectively predict the UCS of granitic rocks. Compared with simply adopting petrographic or physical parameters as the input features of the model, the UCS predictor considering petrographic and physical characteristics can improve the generalization ability of the SSA-XGBoost UCS prediction model effectively. The prediction method proposed in this study is reasonable and can provide some reference for establishing a universal method for accurately and quickly predicting the UCS of rocks.

Keywords: UCS prediction; petrographic characteristic; physical characteristic; feature optimization; SSA-XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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