Analysis and Prognosis of Surface Subsidence in the Jiu Valley
Abstract
Surface subsidence resulting from underground mining activities is a critical environmental and engineering concern that has garnered substantial attention in both academic and practical contexts. The phenomenon occurs when the removal of underground materials, such as coal, minerals, or ores, leads to the collapse or settling of the surface. This study examines mining operations conducted within the thick coal seams of the Jiu Valley Coal Basin in Romania, which utilize longwall mining techniques featuring roof control through caving or top coal caving methods. The analysis focuses on the complex deformations of the ground surface that have occurred over time as a direct result of coal extraction activities in specific mining sectors of the basin. Furthermore, the phenomenon of ground surface subsidence is investigated using the CESAR-LCPC finite element code. The modeling is conducted under the assumptions of elastic and elasto-plastic behavior. A temporal analysis of ground surface deformation is also conducted using a profile function. The results obtained from the modeling are subsequently compared with a comprehensive dataset of in situ measurements.
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References
[2]. Cheng Y., et al., A review of surface subsidence prediction models in mining areas, Environmental Earth Sciences, 71(8), p. 3653-3665, 2014.
[3]. Zhang Y., et al., Stress redistribution and its role in surface subsidence due to mining, Geotechnique, 68(4), p. 332-340, 2018.
[4]. Xu C., et al., The influence of groundwater on subsidence in mining areas: A case study, Hydrogeology Journal, 25(5), p. 1351-1365, 2017.
[5]. Floarea D. A., et al., Necessity of Following up the Land Surface Deformation for Closed Mining Areas, International Multidisciplinary Scientific GeoConference - SGEM 2015, Sofia, Bulgaria, vol. 5, issue 2, p. 733-738, 2015.
[6]. Onica I., Marian D. P., Introduction to finite element modeling. Stability of mining excavations, Universitas Publishing House, Petroșani, 2016.
[7]. Onica I., et al., Prognosis of the Maximum Subsidence and Displacement of the Ground Surface in the Jiu Valley Coal Basin Conditions, International Multidisciplinary Scientific GeoConference - SGEM 2014, Sofia, Bulgaria, vol. 3, p. 465, 2014.
[8]. Floarea D. A., et al., Necessity of Subsidence Phenomenon Monitoring in the Case of Sustainable Development of Jiu Valley Coal Basin Conditions, Annals of the University of Petrosani Mining Engineering, vol. 15, 2014.
[9]. Marian D. P., et al., Sensibility Analysis of the Subsidence Parameters at the Variation of the Main Geo-Mining Factors, Mining Revue/Revista Minelor, vol. 17, issue 3, 2011.
[10]. Li J., et al., Structural impacts of surface subsidence on urban infrastructure, Journal of Structural Engineering, 141(10), 04015039, 2015.
[11]. Marian R. R., et al., 3D Finite Element Analysis of Deformation of Buildings Located in Areas Affected by Underground Mining, International Multidisciplinary Scientific GeoConference: SGEM 2018, vol. 18, issue 1.3, p. 3-10, 2018.
[12]. Huang Y., et al., Ecological impacts of surface subsidence in mining areas: A review, Environmental Management, 63(5), p. 663-674, 2019.
[13]. Marian D. P., et al., Surface Subsidence Prognosis Using the Influence Function Method in the Case of Livezeni Mine, Mining Revue/Revista Minelor, vol. 23, issue 1, 2017.
[14]. Onica I., Marian D. P., Applications of the finite element method in the analysis of surface stability and underground structures, Universitas Publishing House, Petrosani, 2016.
[15]. O’Neill P., et al., Socio-economic impacts of subsidence on mining communities, Resources Policy, 68, 101724, 2020.
[16]. Miller R., et al., Psychological impacts of living in subsidence-prone areas. Journal of Environmental Psychology, 75, 101629, 2021.
[17]. Bock Y., et al., Monitoring ground deformation using InSAR: Applications to mining subsidence, Geophysical Research Letters, 43(10), p. 4904-4911, 2016.
[18]. Bishop D., et al., Stope design principles for minimizing subsidence risk, International Journal of Mining Science and Technology, 28(4), p. 617-625, 2018.
[19]. Chen H., et al., The effectiveness of backfilling techniques in reducing surface subsidence, Mining Engineering, 71(6), p. 49-58, 2019.
[20]. Smith T., et al., Community engagement in subsidence monitoring: Lessons learned, Journal of Community Engagement and Scholarship, 13(1), p. 45-56, 2020.
[21]. Kumar A., et al., Predictive modeling of subsidence in coal mining areas: A review, International Journal of Coal Geology, 253, 103889, 2021.
[22]. Feng X., et al., Climate change impacts on groundwater dynamics and subsidence in mining regions, Water Resources Research, 58(3), e2021WR030123, 2022.
[23]. Zhang L., et al., Application of machine learning in subsidence prediction and monitoring, Computers and Geotechnics, 139, 104290, 2023.
