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Advancing Roof Fall Rate Prediction in Underground Coal Mines: A Comprehensive Analysis Using the Rock Engineering System Method

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Abstract

Despite the significant role of coal in the economic progress of nations, the occupational and health risks associated with its mining pose a major concern for industry stakeholders. The occurrence of roof collapses in coal mines remains a critical factor leading to substantial loss of life and financial damages for miners. Therefore, accurately predicting the roof fall rate (RFR) holds paramount importance. However, the uncertainty surrounding rock parameters in mines hinders the application of conventional methods to assess roof collapse rates in coal mines. To tackle the challenges associated with predicting roof fall rates in underground coal mines, this study proposes a novel solution by leveraging the Rock Engineering System (RES) method. The investigation is grounded in a dataset comprising 109 data points, encompassing crucial input parameters like depth of cover (DOF), primary roof support (PRSUP), intersection diagonal span (IS), mining height (MH), and coal mine roof rating (CMRR). In the model construction phase, 80% of the data (87 points) were utilized to build the RES model. A critical aspect of this study involves the evaluation of the RES model’s performance against alternative regression techniques, namely linear, power, exponential, polynomial, and logarithmic regression. This comparison was executed using the remaining 24 data points (20% of the dataset) for rigorous evaluation. Employing key statistical metrics such as mean square error (MSE), root mean square error (RMSE), and squared correlation coefficient (R2), the study systematically demonstrated the superior accuracy of the RES-based method compared to other approaches. In conclusion, the outcomes strongly support the efficacy of the RES method in predicting roof fall rates, not only in the specific case studied but also indicating promise for its application in other underground coal projects. This underscores the potential of the RES method as a reliable and versatile tool for forecasting roof fall rates in the complex and critical context of underground coal mining.

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Data availability

To reproduce the simulations in the present study, the corresponding files can be found in [49].

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Authors and Affiliations

  1. Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran

    Hadi Fattahi & Hossein Ghaedi

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  1. Hadi Fattahi
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  2. Hossein Ghaedi
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Correspondence to Hadi Fattahi.

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Highlights

• Precise forecasting of roof fall rates in underground coal mines through RES-based approach.

• Comprehensive and non-linear model considering uncertainties in soil and rock behavior.

• Optimizes coal mine design, enhances safety, and addresses potential failures.

• Revolutionizes geotechnical engineering, benefiting mining projects.

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Fattahi, H., Ghaedi, H. Advancing Roof Fall Rate Prediction in Underground Coal Mines: A Comprehensive Analysis Using the Rock Engineering System Method. Mining, Metallurgy & Exploration 41, 957–970 (2024). https://doi.org/10.1007/s42461-024-00962-y

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  • DOI: https://doi.org/10.1007/s42461-024-00962-y

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