Surface subsidence prediction method of backfill-strip mining in coal mining
Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Xiaojun [verfasserIn] Guo, Guangli [verfasserIn] Liu, Hui [verfasserIn] Yang, Xiaoyu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Bulletin of engineering geology and the environment - Berlin : Springer, 1970, 78(2019), 8 vom: 15. März, Seite 6235-6248 |
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| Übergeordnetes Werk: |
volume:78 ; year:2019 ; number:8 ; day:15 ; month:03 ; pages:6235-6248 |
| Links: |
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| DOI / URN: |
10.1007/s10064-019-01485-3 |
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| Katalog-ID: |
SPR008479089 |
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| 520 | |a Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. | ||
| 650 | 4 | |a Backfill-strip mining |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Coal underground mining |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Surface subsidence |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Prediction method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Probability integral method |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Guo, Guangli |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaoyu |e verfasserin |4 aut | |
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10.1007/s10064-019-01485-3 doi (DE-627)SPR008479089 (SPR)s10064-019-01485-3-e DE-627 ger DE-627 rakwb eng 550 600 ASE 38.58 bkl 56.00 bkl 56.20 bkl Zhu, Xiaojun verfasserin aut Surface subsidence prediction method of backfill-strip mining in coal mining 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. Backfill-strip mining (dpeaa)DE-He213 Coal underground mining (dpeaa)DE-He213 Surface subsidence (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Probability integral method (dpeaa)DE-He213 Guo, Guangli verfasserin aut Liu, Hui verfasserin aut Yang, Xiaoyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Berlin : Springer, 1970 78(2019), 8 vom: 15. März, Seite 6235-6248 (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:78 year:2019 number:8 day:15 month:03 pages:6235-6248 https://dx.doi.org/10.1007/s10064-019-01485-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE 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_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_267 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 38.58 ASE 56.00 ASE 56.20 ASE AR 78 2019 8 15 03 6235-6248 |
| spelling |
10.1007/s10064-019-01485-3 doi (DE-627)SPR008479089 (SPR)s10064-019-01485-3-e DE-627 ger DE-627 rakwb eng 550 600 ASE 38.58 bkl 56.00 bkl 56.20 bkl Zhu, Xiaojun verfasserin aut Surface subsidence prediction method of backfill-strip mining in coal mining 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. Backfill-strip mining (dpeaa)DE-He213 Coal underground mining (dpeaa)DE-He213 Surface subsidence (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Probability integral method (dpeaa)DE-He213 Guo, Guangli verfasserin aut Liu, Hui verfasserin aut Yang, Xiaoyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Berlin : Springer, 1970 78(2019), 8 vom: 15. März, Seite 6235-6248 (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:78 year:2019 number:8 day:15 month:03 pages:6235-6248 https://dx.doi.org/10.1007/s10064-019-01485-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE 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_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_267 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 38.58 ASE 56.00 ASE 56.20 ASE AR 78 2019 8 15 03 6235-6248 |
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10.1007/s10064-019-01485-3 doi (DE-627)SPR008479089 (SPR)s10064-019-01485-3-e DE-627 ger DE-627 rakwb eng 550 600 ASE 38.58 bkl 56.00 bkl 56.20 bkl Zhu, Xiaojun verfasserin aut Surface subsidence prediction method of backfill-strip mining in coal mining 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. Backfill-strip mining (dpeaa)DE-He213 Coal underground mining (dpeaa)DE-He213 Surface subsidence (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Probability integral method (dpeaa)DE-He213 Guo, Guangli verfasserin aut Liu, Hui verfasserin aut Yang, Xiaoyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Berlin : Springer, 1970 78(2019), 8 vom: 15. März, Seite 6235-6248 (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:78 year:2019 number:8 day:15 month:03 pages:6235-6248 https://dx.doi.org/10.1007/s10064-019-01485-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE 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_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_267 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 38.58 ASE 56.00 ASE 56.20 ASE AR 78 2019 8 15 03 6235-6248 |
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10.1007/s10064-019-01485-3 doi (DE-627)SPR008479089 (SPR)s10064-019-01485-3-e DE-627 ger DE-627 rakwb eng 550 600 ASE 38.58 bkl 56.00 bkl 56.20 bkl Zhu, Xiaojun verfasserin aut Surface subsidence prediction method of backfill-strip mining in coal mining 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. Backfill-strip mining (dpeaa)DE-He213 Coal underground mining (dpeaa)DE-He213 Surface subsidence (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Probability integral method (dpeaa)DE-He213 Guo, Guangli verfasserin aut Liu, Hui verfasserin aut Yang, Xiaoyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Berlin : Springer, 1970 78(2019), 8 vom: 15. März, Seite 6235-6248 (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:78 year:2019 number:8 day:15 month:03 pages:6235-6248 https://dx.doi.org/10.1007/s10064-019-01485-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE 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_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_267 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 38.58 ASE 56.00 ASE 56.20 ASE AR 78 2019 8 15 03 6235-6248 |
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10.1007/s10064-019-01485-3 doi (DE-627)SPR008479089 (SPR)s10064-019-01485-3-e DE-627 ger DE-627 rakwb eng 550 600 ASE 38.58 bkl 56.00 bkl 56.20 bkl Zhu, Xiaojun verfasserin aut Surface subsidence prediction method of backfill-strip mining in coal mining 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. Backfill-strip mining (dpeaa)DE-He213 Coal underground mining (dpeaa)DE-He213 Surface subsidence (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Probability integral method (dpeaa)DE-He213 Guo, Guangli verfasserin aut Liu, Hui verfasserin aut Yang, Xiaoyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Berlin : Springer, 1970 78(2019), 8 vom: 15. März, Seite 6235-6248 (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:78 year:2019 number:8 day:15 month:03 pages:6235-6248 https://dx.doi.org/10.1007/s10064-019-01485-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE 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_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_267 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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 38.58 ASE 56.00 ASE 56.20 ASE AR 78 2019 8 15 03 6235-6248 |
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Zhu, Xiaojun @@aut@@ Guo, Guangli @@aut@@ Liu, Hui @@aut@@ Yang, Xiaoyu @@aut@@ |
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In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Backfill-strip mining</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Coal underground mining</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Surface subsidence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Probability integral method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Guangli</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Xiaoyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Bulletin of engineering geology and the environment</subfield><subfield code="d">Berlin : Springer, 1970</subfield><subfield code="g">78(2019), 8 vom: 15. 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Zhu, Xiaojun |
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Zhu, Xiaojun ddc 550 bkl 38.58 bkl 56.00 bkl 56.20 misc Backfill-strip mining misc Coal underground mining misc Surface subsidence misc Prediction method misc Probability integral method Surface subsidence prediction method of backfill-strip mining in coal mining |
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550 600 ASE 38.58 bkl 56.00 bkl 56.20 bkl Surface subsidence prediction method of backfill-strip mining in coal mining Backfill-strip mining (dpeaa)DE-He213 Coal underground mining (dpeaa)DE-He213 Surface subsidence (dpeaa)DE-He213 Prediction method (dpeaa)DE-He213 Probability integral method (dpeaa)DE-He213 |
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ddc 550 bkl 38.58 bkl 56.00 bkl 56.20 misc Backfill-strip mining misc Coal underground mining misc Surface subsidence misc Prediction method misc Probability integral method |
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ddc 550 bkl 38.58 bkl 56.00 bkl 56.20 misc Backfill-strip mining misc Coal underground mining misc Surface subsidence misc Prediction method misc Probability integral method |
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Zhu, Xiaojun |
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10.1007/s10064-019-01485-3 |
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surface subsidence prediction method of backfill-strip mining in coal mining |
| title_auth |
Surface subsidence prediction method of backfill-strip mining in coal mining |
| abstract |
Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. |
| abstractGer |
Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. |
| abstract_unstemmed |
Abstract Intensive and massive coal mining causes a series of geological hazards and environmental problems, especially surface subsidence. In recent years, backfill-strip mining has been applied to control mining subsidence in order to realize sustainable development of the mining environment. To accurately predict the surface subsidence of backfill-strip mining, a prediction method of subsidence superposition of backfill-strip mining is proposed on the basis of the traditional probability integral method prediction model. In analyzing the distribution of the actual subsidence space, the surface subsidence problem of backfill-strip mining can be regarded as the superposition of surface subsidence caused by backfill mining and strip mining. Then, the appropriate prediction parameters will be chosen, and the surface subsidence caused by the backfill mining and strip mining will be predicted separately. The surface subsidence values of the backfill-strip mining are equal to the superposition subsidence values predicted by the backfill mining and strip mining prediction method at the same surface location. A similar material model and a numerical simulation model have been built to verify the feasibility and accuracy of the superposition prediction method. The comparison results of the surface subsidence values show that the superposition surface subsidence prediction method is reasonable. The average relative error of this superposition prediction method is less than 6.7%, and its accuracy is 3.9%~11.4% higher than that of the conventional prediction method. The superposition prediction method can satisfy the precision requirement of engineering applications. This study provides a scientific technical reference for safe mining engineering design and surface disaster protection for backfill-strip mining. |
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Surface subsidence prediction method of backfill-strip mining in coal mining |
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Guo, Guangli Liu, Hui Yang, Xiaoyu |
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| score |
7.4010983 |