Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables, and UCS is regarded… More > Graphic Abstract

Zida Liu¹, Danial Jahed Armaghani^2,*, Pouyan Fakharian³, Diyuan Li⁴, Dmitrii Vladimirovich Ulrikh⁵, Natalia Nikolaevna Orekhova⁶, Khaled Mohamed Khedher^7,8

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 799-824, 2022, DOI:10.32604/cmes.2022.021165

Abstract The uniaxial compressive strength (UCS) of rock is an essential property of rock material in different relevant applications, such as rock slope, tunnel construction, and foundation. It takes enormous time and effort to obtain the UCS values directly in the laboratory. Accordingly, an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance. This study presents powerful boosting trees evaluation framework, i.e., adaptive boosting machine, extreme gradient boosting machine (XGBoost), and category gradient boosting machine, for estimating the UCS of sandstone. Schmidt hammer rebound number, P-wave velocity,… More >

Bo Zhang^1,2, Keqing Li^1,2, Siqi Zhang^1,2, Yafei Hu^1,2, Bin Han^1,2,*

Journal of Renewable Materials, Vol.10, No.12, pp. 3539-3558, 2022, DOI:10.32604/jrm.2022.021845

Abstract Cemented paste backfill (CPB) is a sustainable mining technology that is widely used in mines and helps to improve the mine environment. To investigate the relationship between aggregate grading and different affecting factors and the uniaxial compressive strength (UCS) of the cemented paste backfill (CPB), Talbol gradation theory and neural networks is used to evaluate aggregate gradation to determine the optimum aggregate ratio. The mixed aggregate ratio with the least amount of cement (waste stone content river sand content = 7:3) is obtained by using Talbol grading theory and pile compactness function and combined with experiments. In addition, the response… More > Graphic Abstract