4.7 Article

A prediction model for the surface residual subsidence in an abandoned goaf for sustainable development of resource-exhausted cities

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 279, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.123803

Keywords

Abandoned goaf; Surface residual subsidence; Prediction model; Sustainable development

Funding

  1. Natural Science Foundation of China [51904008, 51874002]
  2. Research Fund of the State Key Laboratory of Coal Resources and Safe Mining, CUMT [SKLCRSM19KF005]
  3. Natural Science Foundation of Anhui Colleges [KJ2019A0135]

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This paper developed a surface residual subsidence prediction model (SRSPM) to accurately predict residual subsidence caused by void compaction in abandoned goafs. The model was verified to provide scientific decision support for the choice of suitable land resource development models above abandoned goafs.
In resource-exhausted cities, the development and reuse of land resources above abandoned goafs will invigorate economy and form new regional economic development centers. However, the infrastructures to be built will be affected by the land above an abandoned goaf subjected to long-term residual subsidence. Under such background, accurate prediction of residual subsidence can provide quantitative criteria for land resources development. In this paper, a surface residual subsidence prediction model (SRSPM) was developed. The research results showed that the residual subsidence was caused by compaction of voids including gaps among fractured rocks, the boundary at coal wall and the overburden-separation between soft stratum and hard stratum, respectively. By analyzing the spatial distribution and shape characteristics of these voids, the SRSPM was developed using the theory of probability integral method. Its reliability was verified by comparing with an existed prediction model in a numerical simulated experiment. The SRSPM can provide scientific decision support for the choice of the suitable development model of the land resource above the abandoned goaf. (c) 2020 Elsevier Ltd. All rights reserved.

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