Key Takeaways & Executive Findings
- •• An intelligent approach using monitoring while cutting (MWC) data enables real-time prediction of rock strength and cuttability, facilitating adaptive excavation. • A database of 132 conical pick-cutting experiments was established, integrating cutting parameters, responses, and rock mechanical properties for model training. • The genetic algorithm-optimized backpropagation neural network (GA-BP) achieved the highest accuracy for predicting uniaxial compressive and tensile strengths. • The radial basis neural network (RBF) proved most effective for classifying rock cuttability, combining AHP and fuzzy comprehensive evaluation.
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.
1. Introduction
Non-explosive mechanized excavation is a widely used rock-breaking method in mining engineering. Compared with traditional drilling and blasting, it offers significant advantages, including continuous excavation, superior construction quality, and minimal disturbance to the surrounding rock mass, making it a key alternative methodology [1]. Currently, non-explosive mechanized excavation is widely applied in soft to moderately hard rocks such as coal [2], bauxite [3], and potash [4]. However, studies have demonstrated that most roadheaders can excavate rocks with a strength below 120 MPa. When rock strength exceeds 70 MPa, cutting tools tend to wear rapidly, leading to reduced service life, lower excavation efficiency, and increased operational costs [5]. These limitations hinder the large-scale application of mechanized excavation technology in hard rock mining.
The effectiveness of mechanized excavation largely depends on the mechanical properties of the rock, including its physical and geomechanical characteristics and cuttability [6]. Physical and mechanical properties such as uniaxial compressive strength and tensile strength are important for accurately assessing rock cuttability. If rock properties can be pre-determined to identify hard-to-cut zones, appropriate measures—such as real-time adjustment of cutting tool parameters and targeted improvement of rock cuttability—can be implemented to enhance excavation efficiency. Therefore, rapid in-situ testing of rock strength and accurate assessment of cuttability are essential for the successful application of non-explosive mechanized excavation in hard rock mining.
Current approaches for measuring rock mechanical properties can be categorized into laboratory testing and in-situ monitoring. The former involves labor-intensive procedures, including drilling, coring, polishing, and subsequent laboratory analysis to derive rock mechanics parameters. This method is time-consuming and costly, significantly reducing engineering efficiency. To overcome the limitations of laboratory testing, numerous in-situ monitoring methods have been developed for assessing rock properties and geo-stress conditions [7–10]. These methods include rock strength prediction based on mining technology, and rock cuttability evaluation based on real-time feedback from rock-breaking equipment during mechanical operations. In general, these methods can be categorized into process signal monitoring and identification, infrared imaging identification, image feature identification, reflection spectrum identification, ultrasonic detection, and electromagnetic wave detection. This study mainly focuses on process signal monitoring and identification, which includes vibration, acoustic emission, current, and cutting mechanical signals.
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Shaofeng Wang, Yumeng Wu, Xinlei Shi, Xin Cai, Zilong Zhou (2025). Strength prediction and cuttability identification of rock based on monitoring while cutting (MWC) using a conical pick. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-025-3110-1
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Frequently Asked Questions
What is the main objective of this study?
The study aims to develop an intelligent approach for real-time prediction of rock strength (uniaxial compressive and tensile) and classification of rock cuttability using monitoring while cutting (MWC) data from conical pick cutting experiments.
How was the database for the study constructed?
A database of 132 data sets was established from conical pick-cutting experiments, including cutting parameters (e.g., cutting depth, pick angle), cutting responses (e.g., specific energy, instantaneous cutting rate), and rock mechanical properties.
Which machine learning models were compared for strength prediction?
Various regression and machine learning models were compared, and the genetic algorithm-optimized backpropagation neural network (GA-BP) was found to be the optimal model for predicting uniaxial compressive and tensile strengths.
How was rock cuttability classified in the study?
Rock cuttability was classified using a combination of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, and then identified using machine learning approaches, with the radial basis neural network (RBF) being the optimal classifier.
What are the practical implications of this research?
The proposed approach enables real-time adjustment of excavation parameters and in-situ modification of hard rocks, thereby enhancing excavation efficiency and reducing tool wear in mechanized excavation operations.
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