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

Abstract

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 intelligent approach for predicting rock strength and cuttability. A database comprising 132 data sets is established, containing cutting parameters (such as cutting depth and pick angle), cutting responses (such as specific energy and instantaneous cutting rate), and rock mechanical 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 cuttability is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method, and subsequently identified 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.

Keywords

conical picks / strength prediction / cuttability identification / machine learning / monitoring while cutting / Information and Computing Sciences / Artificial Intelligence and Image Processing

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