Prediction of the Height of Fracturing via Gene Expression Programming in Australian Longwall Panels: A Comparative Study

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DOI:

https://doi.org/10.17794/rgn.2022.1.9

Keywords:

Longwall mining, Height of fracturing, Gene Expression Programming, Empirical model

Abstract

The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and function as opposed to it. The height of fracturing (HoF) includes the caved and continuous fractured zones affected by a high degree of bending. Among the various empirical models, the Ditton's geometry and geology models are widely used in the Australian coalfields. The application of genetic programming (GP) and gene expression programming (GEP) in longwall mining is entirely new and original. This work uses a GEP method in order to predict HoF. The model variables, including the panel width (W), cover depth (H), mining height (T), unit thickness (t), and its distance from the extracted seam (y), are selected via the dimensional analysis and the Buckingham's P-theorem. The dataset involving 31 longwall panels is used to present a new nonlinear regression function. The statistical estimators, including the coefficient of determination (R**2), the average error (AE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE), are used to compare the performance of the discussed models. The  value for the GEP model (99%) is considerably higher than the corresponding values of the Ditton's geometry (= 61%) and geology (= 81%) models. Moreover, the maximum values of the statistical error estimators (AE, MAPE, and RMSE) for the GEP model are 12 %, 14%, and 16%, respectively, of the corresponding values of the Ditton's models.

 

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Published

2022-02-01

How to Cite

Rasouli, H., Shahriar, K. ., & Madani, S. H. (2022). Prediction of the Height of Fracturing via Gene Expression Programming in Australian Longwall Panels: A Comparative Study. Rudarsko-geološko-Naftni Zbornik, 37(1). https://doi.org/10.17794/rgn.2022.1.9

Issue

Vol. 37 No. 1 (2022): No. 57

Section

Mining

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