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A new approach to predicting mining induced surface subsidence

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Abstract

There are many parameters influencing mining induced surface subsidence. These parameters usually interact with one another and some of them have the characteristic of fuzziness. Current approaches to predicting the subsidence cannot take into account of such interactions and fuzziness. In order to overcome this disadvantage, many mining induced surface subsidence cases were accumulated, and an artificial neuro fuzzy inference system(ANFIS) was used to set up 4 ANFIS models to predict the rise angle, dip angle, center angle and the maximum subsidence, respectively. The fitting and generalization prediction capabilities of the models were tested. The test results show that the models have very good fitting and generalization prediction capabilities and the approach can be applied to predict the mining induced surface subsidence.

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, 410083, Changsha, China

    Ding De-xin PhD (Professor), Zhang Zhi-jun & Bi Zhong-wei

  2. School of Architectural, Resources and Environment Engineering, Nanhua University, 421001, Hengyang, China

    Ding De-xin PhD (Professor), Zhang Zhi-jun & Bi Zhong-wei

Authors
  1. Ding De-xin PhD
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  2. Zhang Zhi-jun
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  3. Bi Zhong-wei
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Corresponding author

Correspondence to Ding De-xin PhD.

Additional information

Foundation item: Project(50274043) supported by the National Natural Science Foundation of China; project (01JJY1004) supported by the Natural Science Foundation of Hunan Province; project (01A015) supported by the Natural Science Foundation of Hunan Provincial Education Committee

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Ding, Dx., Zhang, Zj. & Bi, Zw. A new approach to predicting mining induced surface subsidence. J Cent. South Univ. Technol. 13, 438–444 (2006). https://doi.org/10.1007/s11771-006-0064-y

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  • DOI: https://doi.org/10.1007/s11771-006-0064-y

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