Surface dynamic subsidence prediction model of solid backfill mining

Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhu, Xiaojun [verfasserIn] Guo, Guangli Zha, Jianfeng Chen, Tao Fang, Qi Yang, Xiaoyu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Solid backfill mining Mining subsidence Dynamic subsidence Prediction model

Anmerkung:	© Springer-Verlag Berlin Heidelberg 2016

Übergeordnetes Werk:	Enthalten in: Environmental earth sciences - Berlin : Springer, 2009, 75(2016), 12 vom: 14. Juni
Übergeordnetes Werk:	volume:75 ; year:2016 ; number:12 ; day:14 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s12665-016-5817-9

Katalog-ID:	SPR02671826X

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520			\|a Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage.
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650		4	\|a Mining subsidence \|7 (dpeaa)DE-He213
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700	1		\|a Yang, Xiaoyu \|4 aut
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912			\|a GBV_ILN_293
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912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
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matchkey_str	article:18666299:2016----::ufcdnmcusdnerdcinoeosl
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publishDate	2016
allfields	10.1007/s12665-016-5817-9 doi (DE-627)SPR02671826X (SPR)s12665-016-5817-9-e DE-627 ger DE-627 rakwb eng Zhu, Xiaojun verfasserin aut Surface dynamic subsidence prediction model of solid backfill mining 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. Solid backfill mining (dpeaa)DE-He213 Mining subsidence (dpeaa)DE-He213 Dynamic subsidence (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Guo, Guangli aut Zha, Jianfeng aut Chen, Tao aut Fang, Qi aut Yang, Xiaoyu aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 12 vom: 14. Juni (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:12 day:14 month:06 https://dx.doi.org/10.1007/s12665-016-5817-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 12 14 06
spelling	10.1007/s12665-016-5817-9 doi (DE-627)SPR02671826X (SPR)s12665-016-5817-9-e DE-627 ger DE-627 rakwb eng Zhu, Xiaojun verfasserin aut Surface dynamic subsidence prediction model of solid backfill mining 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. Solid backfill mining (dpeaa)DE-He213 Mining subsidence (dpeaa)DE-He213 Dynamic subsidence (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Guo, Guangli aut Zha, Jianfeng aut Chen, Tao aut Fang, Qi aut Yang, Xiaoyu aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 12 vom: 14. Juni (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:12 day:14 month:06 https://dx.doi.org/10.1007/s12665-016-5817-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 12 14 06
allfields_unstemmed	10.1007/s12665-016-5817-9 doi (DE-627)SPR02671826X (SPR)s12665-016-5817-9-e DE-627 ger DE-627 rakwb eng Zhu, Xiaojun verfasserin aut Surface dynamic subsidence prediction model of solid backfill mining 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. Solid backfill mining (dpeaa)DE-He213 Mining subsidence (dpeaa)DE-He213 Dynamic subsidence (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Guo, Guangli aut Zha, Jianfeng aut Chen, Tao aut Fang, Qi aut Yang, Xiaoyu aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 12 vom: 14. Juni (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:12 day:14 month:06 https://dx.doi.org/10.1007/s12665-016-5817-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 12 14 06
allfieldsGer	10.1007/s12665-016-5817-9 doi (DE-627)SPR02671826X (SPR)s12665-016-5817-9-e DE-627 ger DE-627 rakwb eng Zhu, Xiaojun verfasserin aut Surface dynamic subsidence prediction model of solid backfill mining 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. Solid backfill mining (dpeaa)DE-He213 Mining subsidence (dpeaa)DE-He213 Dynamic subsidence (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Guo, Guangli aut Zha, Jianfeng aut Chen, Tao aut Fang, Qi aut Yang, Xiaoyu aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 12 vom: 14. Juni (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:12 day:14 month:06 https://dx.doi.org/10.1007/s12665-016-5817-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 12 14 06
allfieldsSound	10.1007/s12665-016-5817-9 doi (DE-627)SPR02671826X (SPR)s12665-016-5817-9-e DE-627 ger DE-627 rakwb eng Zhu, Xiaojun verfasserin aut Surface dynamic subsidence prediction model of solid backfill mining 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. Solid backfill mining (dpeaa)DE-He213 Mining subsidence (dpeaa)DE-He213 Dynamic subsidence (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Guo, Guangli aut Zha, Jianfeng aut Chen, Tao aut Fang, Qi aut Yang, Xiaoyu aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 12 vom: 14. Juni (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:12 day:14 month:06 https://dx.doi.org/10.1007/s12665-016-5817-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 12 14 06
language	English
source	Enthalten in Environmental earth sciences 75(2016), 12 vom: 14. Juni volume:75 year:2016 number:12 day:14 month:06
sourceStr	Enthalten in Environmental earth sciences 75(2016), 12 vom: 14. Juni volume:75 year:2016 number:12 day:14 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Solid backfill mining Mining subsidence Dynamic subsidence Prediction model
isfreeaccess_bool	false
container_title	Environmental earth sciences
authorswithroles_txt_mv	Zhu, Xiaojun @@aut@@ Guo, Guangli @@aut@@ Zha, Jianfeng @@aut@@ Chen, Tao @@aut@@ Fang, Qi @@aut@@ Yang, Xiaoyu @@aut@@
publishDateDaySort_date	2016-06-14T00:00:00Z
hierarchy_top_id	599673451
id	SPR02671826X
language_de	englisch
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author	Zhu, Xiaojun
spellingShingle	Zhu, Xiaojun misc Solid backfill mining misc Mining subsidence misc Dynamic subsidence misc Prediction model Surface dynamic subsidence prediction model of solid backfill mining
authorStr	Zhu, Xiaojun
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topic_title	Surface dynamic subsidence prediction model of solid backfill mining Solid backfill mining (dpeaa)DE-He213 Mining subsidence (dpeaa)DE-He213 Dynamic subsidence (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213
topic	misc Solid backfill mining misc Mining subsidence misc Dynamic subsidence misc Prediction model
topic_unstemmed	misc Solid backfill mining misc Mining subsidence misc Dynamic subsidence misc Prediction model
topic_browse	misc Solid backfill mining misc Mining subsidence misc Dynamic subsidence misc Prediction model
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title	Surface dynamic subsidence prediction model of solid backfill mining
ctrlnum	(DE-627)SPR02671826X (SPR)s12665-016-5817-9-e
title_full	Surface dynamic subsidence prediction model of solid backfill mining
author_sort	Zhu, Xiaojun
journal	Environmental earth sciences
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author_browse	Zhu, Xiaojun Guo, Guangli Zha, Jianfeng Chen, Tao Fang, Qi Yang, Xiaoyu
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doi_str_mv	10.1007/s12665-016-5817-9
title_sort	surface dynamic subsidence prediction model of solid backfill mining
title_auth	Surface dynamic subsidence prediction model of solid backfill mining
abstract	Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. © Springer-Verlag Berlin Heidelberg 2016
abstractGer	Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. © Springer-Verlag Berlin Heidelberg 2016
abstract_unstemmed	Abstract Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage. © Springer-Verlag Berlin Heidelberg 2016
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title_short	Surface dynamic subsidence prediction model of solid backfill mining
url	https://dx.doi.org/10.1007/s12665-016-5817-9
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author2	Guo, Guangli Zha, Jianfeng Chen, Tao Fang, Qi Yang, Xiaoyu
author2Str	Guo, Guangli Zha, Jianfeng Chen, Tao Fang, Qi Yang, Xiaoyu
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doi_str	10.1007/s12665-016-5817-9
up_date	2024-07-03T22:21:36.805Z
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