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    ARTICLE

    Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

    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 - 16 April 2024

    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,… More > Graphic Abstract

    Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

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