Comptes Rendus
no. 1
Mécanismes
Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model
Min Wang1   ; Wen Wan2 ; Yanlin Zhao3
1 School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, China
2 School of Resource Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan, China
3 Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Hunan University of Science and Technology, Xiangtan, China
Comptes Rendus. Mécanique, Volume 348 (2020) no. 1, pp. 3-32.

Uniaxial compressive strength (UCS) is an important mechanical parameter for stability assessments in rock mass engineering. In practice, obtaining the UCS simply, accurately and economically has attracted substantial attention. In this paper, studies related to UCS estimation using indirect tests were reviewed, it was found that regression techniques and soft computing techniques were mainly used to evaluate the UCS value, and theses soft computing techniques can accurately and effectively predict the UCS. To select the proper indirect parameters to predict the UCS, statistical analysis was performed on the relationships between UCS and indirect parameters, and based on the analysis, two indirect parameters (the Schmidt hammer rebound value (L-type) and ultrasonic P-wave velocity) were deemed adequate to predict UCS. To establish the UCS predictive model, the random forest algorithm was employed, the predictive model was verified by data collected from references. To further verify the validity of the predictive model, laboratory tests were performed, and the predictive results were consistent with the measured results, thus the UCS value predictive model can be applied to the fields of rock mechanics and engineering geology.

Reçu le :
Révisé le :
Accepté le :
Publié le :
DOI : 10.5802/crmeca.3
Mots-clés : Uniaxial compressive strength (UCS), Indirect tests, Statistical analysis, Random forest algorithm

Min Wang 1 ; Wen Wan 2 ; Yanlin Zhao 3

1 School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, China
2 School of Resource Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan, China
3 Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Hunan University of Science and Technology, Xiangtan, China
Licence : CC-BY 4.0
Droits d'auteur : Les auteurs conservent leurs droits 
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Min Wang; Wen Wan; Yanlin Zhao. Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model. Comptes Rendus. Mécanique, Volume 348 (2020) no. 1, pp. 3-32. doi : 10.5802/crmeca.3. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.3/

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  • Hai Rong; Longyue He; Yadi Wang; Xiangzhi Wei; Shilong Wei; Suihan Yang; Jincheng Li; Linlin Chen Overlying strata movement law and rockburst prevention control for fully mechanized top coal caving face in extra-thick coal seams, Scientific Reports, Volume 15 (2025) no. 1 | DOI:10.1038/s41598-025-89133-7
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