ZHAO Z M, WANG S B, JIANG D P,et al.Prediction of granite uniaxial compressive strength under multiple working conditions based on ensemble learning[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(4):114-122.

doi:10.16186/j.cnki.1673-9787.2024120030

Received:2024/12/13

Revised:2025/04/21

Published:2026/06/17

Prediction of granite uniaxial compressive strength under multiple working conditions based on ensemble learning

Zhao Zhongming1, Wang Shuaibin1, Jiang Dongping2, Wang Chun1,2, Fu Tianyu3, Cheng Bo1

1.School of Energy Science and Engineering， Henan Polytechnic University， Jiaozuo  454003， Henan， China;2.Sinosteel Maanshan General Institute of Mining Research Co.， Ltd.， Maanshan  243000， Anhui， China;3.School of Emergency Management and Safety Engineering， China University of Mining & Technology， Beijing  100083， China

Abstract: Objectives To address the problems of long testing periods and high costs associated with traditional methods for measuring rock compressive strength， a prediction method for granite uniaxial compressive strength based on ensemble learning was proposed. Methods Axial compression tests were conducted on granite specimens under different working conditions using an electronic universal testing machine. Based on the experimental results and published data， a dataset for granite the uniaxial compressive strength under multiple working conditions was established. Six different machine learning algorithms were selected to develop prediction models within an ensemble learning framework. The performance of the models was comprehensively evaluated using multiple evaluation metrics， and feature importance analysis was subsequently performed on the input variables.  Results The results indicate that the uniaxial compressive strength of granite is significantly affected by different working conditions. All established ensemble learning models exhibited good predictive performance. The overall model performance was ranked as follows： Random Forest， Gradient Boosting， Adaptive Boosting. Feature importance analysis further revealed that the relative importance of the influencing factors followed the order： heating temperature， geological conditions， heating-immersion cycle， immersion temperature， immersion time， cooling method， heat-transfer solution.  Conclusions Among the ensemble learning models， the RF model achieved the best overall predictive performance. The analysis also showed that high temperature has the most significant influence on granite uniaxial compressive strength. The proposed model provides a new approach for predicting the uniaxial compressive strength of granite under multiple working conditions.

Key words:granite;ensemble learning;random forest;uniaxial compressive strength prediction;feature importance analysis