Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling
Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Yaxu [verfasserIn] Liu, Bin [verfasserIn] Wang, Jiwen [verfasserIn] Meng, Qingyang [verfasserIn] Liu, Zhengyu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Bulletin of engineering geology and the environment - Springer Berlin Heidelberg, 1970, 83(2024), 8 vom: 16. Juli |
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| Übergeordnetes Werk: |
volume:83 ; year:2024 ; number:8 ; day:16 ; month:07 |
| Links: |
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| DOI / URN: |
10.1007/s10064-024-03805-8 |
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SPR056587724 |
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| 245 | 1 | 0 | |a Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling |
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| 520 | |a Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. | ||
| 650 | 4 | |a Tunnel boring machine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Laser-induced breakdown spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Quartz content |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Artificial intelligence algorithms |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, Qingyang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zhengyu |e verfasserin |4 aut | |
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10.1007/s10064-024-03805-8 doi (DE-627)SPR056587724 (SPR)s10064-024-03805-8-e DE-627 ger DE-627 rakwb eng 550 600 VZ 38.58 bkl 56.00 bkl 56.20 bkl Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. Tunnel boring machine (dpeaa)DE-He213 Laser-induced breakdown spectroscopy (dpeaa)DE-He213 Quartz content (dpeaa)DE-He213 Artificial intelligence algorithms (dpeaa)DE-He213 Liu, Bin verfasserin aut Wang, Jiwen verfasserin aut Meng, Qingyang verfasserin aut Liu, Zhengyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Springer Berlin Heidelberg, 1970 83(2024), 8 vom: 16. Juli (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:83 year:2024 number:8 day:16 month:07 https://dx.doi.org/10.1007/s10064-024-03805-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.58 VZ 56.00 VZ 56.20 VZ AR 83 2024 8 16 07 |
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10.1007/s10064-024-03805-8 doi (DE-627)SPR056587724 (SPR)s10064-024-03805-8-e DE-627 ger DE-627 rakwb eng 550 600 VZ 38.58 bkl 56.00 bkl 56.20 bkl Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. Tunnel boring machine (dpeaa)DE-He213 Laser-induced breakdown spectroscopy (dpeaa)DE-He213 Quartz content (dpeaa)DE-He213 Artificial intelligence algorithms (dpeaa)DE-He213 Liu, Bin verfasserin aut Wang, Jiwen verfasserin aut Meng, Qingyang verfasserin aut Liu, Zhengyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Springer Berlin Heidelberg, 1970 83(2024), 8 vom: 16. Juli (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:83 year:2024 number:8 day:16 month:07 https://dx.doi.org/10.1007/s10064-024-03805-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.58 VZ 56.00 VZ 56.20 VZ AR 83 2024 8 16 07 |
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10.1007/s10064-024-03805-8 doi (DE-627)SPR056587724 (SPR)s10064-024-03805-8-e DE-627 ger DE-627 rakwb eng 550 600 VZ 38.58 bkl 56.00 bkl 56.20 bkl Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. Tunnel boring machine (dpeaa)DE-He213 Laser-induced breakdown spectroscopy (dpeaa)DE-He213 Quartz content (dpeaa)DE-He213 Artificial intelligence algorithms (dpeaa)DE-He213 Liu, Bin verfasserin aut Wang, Jiwen verfasserin aut Meng, Qingyang verfasserin aut Liu, Zhengyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Springer Berlin Heidelberg, 1970 83(2024), 8 vom: 16. Juli (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:83 year:2024 number:8 day:16 month:07 https://dx.doi.org/10.1007/s10064-024-03805-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.58 VZ 56.00 VZ 56.20 VZ AR 83 2024 8 16 07 |
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10.1007/s10064-024-03805-8 doi (DE-627)SPR056587724 (SPR)s10064-024-03805-8-e DE-627 ger DE-627 rakwb eng 550 600 VZ 38.58 bkl 56.00 bkl 56.20 bkl Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. Tunnel boring machine (dpeaa)DE-He213 Laser-induced breakdown spectroscopy (dpeaa)DE-He213 Quartz content (dpeaa)DE-He213 Artificial intelligence algorithms (dpeaa)DE-He213 Liu, Bin verfasserin aut Wang, Jiwen verfasserin aut Meng, Qingyang verfasserin aut Liu, Zhengyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Springer Berlin Heidelberg, 1970 83(2024), 8 vom: 16. Juli (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:83 year:2024 number:8 day:16 month:07 https://dx.doi.org/10.1007/s10064-024-03805-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.58 VZ 56.00 VZ 56.20 VZ AR 83 2024 8 16 07 |
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10.1007/s10064-024-03805-8 doi (DE-627)SPR056587724 (SPR)s10064-024-03805-8-e DE-627 ger DE-627 rakwb eng 550 600 VZ 38.58 bkl 56.00 bkl 56.20 bkl Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. Tunnel boring machine (dpeaa)DE-He213 Laser-induced breakdown spectroscopy (dpeaa)DE-He213 Quartz content (dpeaa)DE-He213 Artificial intelligence algorithms (dpeaa)DE-He213 Liu, Bin verfasserin aut Wang, Jiwen verfasserin aut Meng, Qingyang verfasserin aut Liu, Zhengyu verfasserin aut Enthalten in Bulletin of engineering geology and the environment Springer Berlin Heidelberg, 1970 83(2024), 8 vom: 16. Juli (DE-627)271597011 (DE-600)1480689-7 1435-9537 nnns volume:83 year:2024 number:8 day:16 month:07 https://dx.doi.org/10.1007/s10064-024-03805-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.58 VZ 56.00 VZ 56.20 VZ AR 83 2024 8 16 07 |
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Enthalten in Bulletin of engineering geology and the environment 83(2024), 8 vom: 16. Juli volume:83 year:2024 number:8 day:16 month:07 |
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Wang, Yaxu @@aut@@ Liu, Bin @@aut@@ Wang, Jiwen @@aut@@ Meng, Qingyang @@aut@@ Liu, Zhengyu @@aut@@ |
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The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. 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Wang, Yaxu |
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Wang, Yaxu ddc 550 bkl 38.58 bkl 56.00 bkl 56.20 misc Tunnel boring machine misc Laser-induced breakdown spectroscopy misc Quartz content misc Artificial intelligence algorithms Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling |
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550 600 VZ 38.58 bkl 56.00 bkl 56.20 bkl Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling Tunnel boring machine (dpeaa)DE-He213 Laser-induced breakdown spectroscopy (dpeaa)DE-He213 Quartz content (dpeaa)DE-He213 Artificial intelligence algorithms (dpeaa)DE-He213 |
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ddc 550 bkl 38.58 bkl 56.00 bkl 56.20 misc Tunnel boring machine misc Laser-induced breakdown spectroscopy misc Quartz content misc Artificial intelligence algorithms |
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analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in tbm tunneling |
| title_auth |
Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling |
| abstract |
Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. © The Author(s) 2024 |
| abstractGer |
Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. © The Author(s) 2024 |
| abstract_unstemmed |
Abstract Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method. © The Author(s) 2024 |
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Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling |
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Liu, Bin Wang, Jiwen Meng, Qingyang Liu, Zhengyu |
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| score |
7.168107 |