Key Takeaways & Executive Findings
- •• A novel kinetic energy calculation model for roadway excavation is proposed, based on fracture and energy states of the rock mass. • Energy evolution during excavation is categorized into four stages: in-situ stress accumulation, coal compression, plastic deformation, and coal failure. • Kinetic energy and energy release per unit volume increase nonlinearly with mining depth and stress concentration factor, but decrease linearly with plastic zone width. • The proposed rock burst risk evaluation method is validated by field observations and aligns with the drilling cuttings method.
Abstract
The accumulation and release of deformation energy within the rock mass of a roadway are primary contributors to the occurrence of rock bursts. This study introduces a calculation model for the kinetic energy generated during roadway excavation, which is based on the fracture and energy states of the rock mass. The relationships among the mining depth, width of the plastic zone, rebound range of the roof and floor, stress concentration factor, and the induced kinetic energy are systematically explored. Furthermore, a rock burst risk evaluation method is proposed. The findings indicate that the energy evolution of the rock mass can be categorized into four stages: energy accumulation due to in-situ stress, energy accumulation resulting from coal compression, energy dissipation through coal plastic deformation, and energy consumption due to coal failure. The energy release from the rock mass is influenced by several factors, including mining depth, stress concentration factor, the width of the plastic zone, and the rebound range of the roof and floor. Within the plastic zone of coal, the energy released per unit volume of coal and the induced kinetic energy exhibit a nonlinear increase with mining depth and stress concentration factor, while they decrease linearly as the width of the plastic zone increases. Similarly, the driving energy per unit volume of the roof and floor shows a nonlinear increase with mining depth and stress concentration factor, a linear increase with the rebound range of the roof and floor, and a linear decrease with the width of the plastic zone. A rock burst risk evaluation method is developed based on the kinetic energy model. Field observations demonstrate that this method aligns with the drilling cuttings rock burst risk assessment method, thereby confirming its validity.
1. Introduction
In recent years, the increasing depth and intensity of coal exploitation have led to a rise in the frequency and severity of rock burst incidents [1–4]. The complex geological conditions associated with coal seams and elevated in-situ stress levels exacerbate the occurrence of dynamic disasters during deep mining operations, such as rock bursts. In China, rock bursts predominantly occur in roadways [5–8]. Notable incidents include the rock burst during roadway excavation at Longyun Coal Mine in Shandong Province on October 20, 2018 [9], the rock burst at the central main roadway of Mengcun Coal Mine in Shaanxi Province on May 4, 2020 [10], and the rock burst at the entrance of Xinjulong Coal Mine in Shandong Province on February 22, 2020 [11]. Additionally, a significant rock burst occurred at the mining face of Liuhuanggou Coal Mine in Xinjiang Province on January 1, 2023 [12].
The primary energy source for the rock bursts is the energy released from the coal and rock mass in the roadways or mining faces [13–15]. Therefore, understanding the mechanisms of energy accumulation and release in the rock mass due to mining activities has become a critical area of research aimed at elucidating the underlying processes and developing effective risk assessment methods.
Loading authentic research manuscript (Pages 1–5)...
TAN Yunliang, TAN Yan, GUO Weiyao, LI Bo, HE Shudong, ZHANG Lei, ZHANG Qiuyuan (2025). Calculation model for kinetic energy and rock burst risk evaluation method during roadway excavation. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.03.006
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a calculation model for kinetic energy generated during roadway excavation and a rock burst risk evaluation method based on the model, validated by field observations.
How is the energy evolution of rock mass categorized?
The energy evolution is categorized into four stages: energy accumulation due to in-situ stress, energy accumulation from coal compression, energy dissipation through coal plastic deformation, and energy consumption due to coal failure.
What factors influence the energy release from the rock mass?
The energy release is influenced by mining depth, stress concentration factor, width of the plastic zone, and rebound range of the roof and floor.
How does the proposed evaluation method compare to existing methods?
Field observations show that the proposed method aligns with the drilling cuttings rock burst risk assessment method, confirming its validity.
What are the practical implications of this research?
The research provides a quantitative tool for assessing rock burst risk during roadway excavation, which can help in designing safer mining operations and mitigating dynamic disasters.
Related Technical Papers & Translations
A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning
Brain tumor segmentation from multimodal MRI is crucial for diagnosis and treatment planning. In this study, we propose a novel deep learning framework that integrates structural and functional imaging modalities to improve segmentation accuracy. Our method employs a multi-scale attention mechanism and a hybrid loss function to handle class imbalance and boundary ambiguity. Evaluated on the BraTS benchmark, our approach achieves state-of-the-art performance, with Dice scores of 0.91, 0.87, and 0.84 for whole tumor, core, and enhancing tumor, respectively. Furthermore, we demonstrate the generalizability of our model across different scanners and protocols. Our findings suggest that the proposed method can significantly aid clinical decision-making and surgical planning.
Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage
Cemented rockfill (CRF) combines structural support with sustainable reuse of coal-derived solid waste. This study integrates digital image correlation, acoustic emission monitoring, and finite–discrete element simulations to investigate mechanical behavior, fracture development, and energy evolution of CRF containing 54% aggregate content with three grain-size distributions (5–10, 10–20, and 20–30 mm). Results indicate finer aggregates raise compressive strength and elastic modulus, and increase post-peak softening and residual stiffness. Fracture patterns transition from dominantly unidirectional failure in coarse specimens to pronounced X-shaped conjugate shear in fine specimens, with cracks initiating at boundaries and propagating inward. The proportion of failed joints at comparable strains decreases markedly with finer gradation, reflecting a more homogeneous crack network that enhances post-peak load retention and produces frequent minor stress fluctuations. Energy analyses reveal a coarse > medium > fine ordering in cumulative dissipation; however, finer aggregates delay rapid kinetic and dissipative energy release, promoting slower energy redistribution and improved load resistance. These findings quantify how aggregate gradation controls deformational mechanisms, crack topology, and energy partitioning, and provide design guidance for optimizing aggregate size and cementitious composition to enhance ductility, energy absorption, and structural reliability of CRF in underground engineering.