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Prediction of Longwall Progressive Subsidence Basin Using the Gompertz Time Function
It is imperative for the mining area to timely and accurately predict the longwall progressive subsidence basin. However, the mainstream method that uses time function still has limitations, which impede extensive application. In this study, we analyzed the flaws of the optimized segmented Knothe ti...
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Published in: | Rock mechanics and rock engineering 2022, Vol.55 (1), p.379-398 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | It is imperative for the mining area to timely and accurately predict the longwall progressive subsidence basin. However, the mainstream method that uses time function still has limitations, which impede extensive application. In this study, we analyzed the flaws of the optimized segmented Knothe time function in detail, and expounded the possible improvement directions. Subsequently, we proposed the Gompertz time function for predicting longwall progressive subsidence basin, based on the modeling idea that three origins are consistent. Afterward, we analyzed the variation law between parameters of the Gompertz time function and the geological mining conditions, and elaborated the parameter calculation method and the prediction algorithm for the longwall progressive subsidence basin. Finally, we demonstrated the practical application effect of this method with experiments. The average RMSE and average relative RMSE of predicted progressive subsidence using the Gompertz time function are 58.4 mm and 6.9%, respectively, and compared with the same statistics using the optimized segmented Knothe time function, the accuracy is increased by 27.9% on average. The results show that the accuracy of this article proposed method can achieve centimeter-level, meet the requirements of practical engineering application, and this method is expected to enable the mining proceeding in a safe, effective and environmentally sustainable way. |
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ISSN: | 0723-2632 1434-453X |
DOI: | 10.1007/s00603-021-02664-z |