Strength prediction and cuttability identification of rock based on monitoring while cutting(MWC)using a conical pick  

作　　者：Shaofeng Wang Yumeng Wu Xinlei Shi Xin Cai Zilong Zhou 

机构地区：[1]School of Resources and Safety Engineering,Central South University,Changsha 410083,China [2]NORIN Mining Company Limited,Beijing 100053,China

出　　处：《International Journal of Minerals,Metallurgy and Materials》2025年第5期1025-1043,共19页矿物冶金与材料学报(英文版)

基　　金：supported by the National Natural Science Foundation of China(Nos.52174099 and 52474168);the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3050);the Natural Science Foundation of Hunan,China(No.2024JJ4064);the Open Fund of the State Key Laboratory of Safety Technology of Metal Mines(No.kfkt2023-01).

摘　　要：Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.

关 键 词：conical picks strength prediction cuttability identification machine learning monitoring while cutting 

分 类 号：TU45[建筑科学—岩土工程]