A prediction model for the surface residual subsidence in an abandoned goaf for sustainable development of resource-exhausted cities. (10th January 2021)

• Record Type: Journal Article
• Authors: Guo, Qingbiao; Meng, Xiangrui; Li, Yingming; Lv, Xin; Liu, Chao
• Is Part Of: Journal of cleaner production. Volume 279(2021)
• Publication Date: 2021-01-10
• Subjects: Abandoned goaf -- Surface residual subsidence -- Prediction model -- Sustainable development; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.123803
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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.