A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm
Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Yaxu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© Korean Society of Civil Engineers 2023 |
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| Übergeordnetes Werk: |
Enthalten in: KSCE journal of civil engineering - Seoul : Korean Soc. of Civil Engineers, 1997, 27(2023), 7 vom: 05. Juni, Seite 3148-3162 |
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| Übergeordnetes Werk: |
volume:27 ; year:2023 ; number:7 ; day:05 ; month:06 ; pages:3148-3162 |
| Links: |
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| DOI / URN: |
10.1007/s12205-023-2475-9 |
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| Katalog-ID: |
SPR052071626 |
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| 245 | 1 | 2 | |a A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm |
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| 520 | |a Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. | ||
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| 650 | 4 | |a Tunnel boring machine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wave velocity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Catboost |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bayesian optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Ruirui |0 (orcid)0000-0001-5375-4955 |4 aut | |
| 700 | 1 | |a Wang, Jiwen |4 aut | |
| 700 | 1 | |a Li, Ningbo |4 aut | |
| 700 | 1 | |a Cao, Hongyi |0 (orcid)0000-0002-3588-3478 |4 aut | |
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10.1007/s12205-023-2475-9 doi (DE-627)SPR052071626 (SPR)s12205-023-2475-9-e DE-627 ger DE-627 rakwb eng Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2023 Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. Uniaxial compressive strength of rock mass (dpeaa)DE-He213 Tunnel boring machine (dpeaa)DE-He213 Wave velocity (dpeaa)DE-He213 Catboost (dpeaa)DE-He213 Bayesian optimization (dpeaa)DE-He213 Wang, Ruirui (orcid)0000-0001-5375-4955 aut Wang, Jiwen aut Li, Ningbo aut Cao, Hongyi (orcid)0000-0002-3588-3478 aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 27(2023), 7 vom: 05. Juni, Seite 3148-3162 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:27 year:2023 number:7 day:05 month:06 pages:3148-3162 https://dx.doi.org/10.1007/s12205-023-2475-9 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_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 AR 27 2023 7 05 06 3148-3162 |
| spelling |
10.1007/s12205-023-2475-9 doi (DE-627)SPR052071626 (SPR)s12205-023-2475-9-e DE-627 ger DE-627 rakwb eng Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2023 Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. Uniaxial compressive strength of rock mass (dpeaa)DE-He213 Tunnel boring machine (dpeaa)DE-He213 Wave velocity (dpeaa)DE-He213 Catboost (dpeaa)DE-He213 Bayesian optimization (dpeaa)DE-He213 Wang, Ruirui (orcid)0000-0001-5375-4955 aut Wang, Jiwen aut Li, Ningbo aut Cao, Hongyi (orcid)0000-0002-3588-3478 aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 27(2023), 7 vom: 05. Juni, Seite 3148-3162 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:27 year:2023 number:7 day:05 month:06 pages:3148-3162 https://dx.doi.org/10.1007/s12205-023-2475-9 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_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 AR 27 2023 7 05 06 3148-3162 |
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10.1007/s12205-023-2475-9 doi (DE-627)SPR052071626 (SPR)s12205-023-2475-9-e DE-627 ger DE-627 rakwb eng Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2023 Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. Uniaxial compressive strength of rock mass (dpeaa)DE-He213 Tunnel boring machine (dpeaa)DE-He213 Wave velocity (dpeaa)DE-He213 Catboost (dpeaa)DE-He213 Bayesian optimization (dpeaa)DE-He213 Wang, Ruirui (orcid)0000-0001-5375-4955 aut Wang, Jiwen aut Li, Ningbo aut Cao, Hongyi (orcid)0000-0002-3588-3478 aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 27(2023), 7 vom: 05. Juni, Seite 3148-3162 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:27 year:2023 number:7 day:05 month:06 pages:3148-3162 https://dx.doi.org/10.1007/s12205-023-2475-9 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_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 AR 27 2023 7 05 06 3148-3162 |
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10.1007/s12205-023-2475-9 doi (DE-627)SPR052071626 (SPR)s12205-023-2475-9-e DE-627 ger DE-627 rakwb eng Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2023 Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. Uniaxial compressive strength of rock mass (dpeaa)DE-He213 Tunnel boring machine (dpeaa)DE-He213 Wave velocity (dpeaa)DE-He213 Catboost (dpeaa)DE-He213 Bayesian optimization (dpeaa)DE-He213 Wang, Ruirui (orcid)0000-0001-5375-4955 aut Wang, Jiwen aut Li, Ningbo aut Cao, Hongyi (orcid)0000-0002-3588-3478 aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 27(2023), 7 vom: 05. Juni, Seite 3148-3162 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:27 year:2023 number:7 day:05 month:06 pages:3148-3162 https://dx.doi.org/10.1007/s12205-023-2475-9 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_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 AR 27 2023 7 05 06 3148-3162 |
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10.1007/s12205-023-2475-9 doi (DE-627)SPR052071626 (SPR)s12205-023-2475-9-e DE-627 ger DE-627 rakwb eng Wang, Yaxu verfasserin (orcid)0009-0005-7217-2412 aut A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2023 Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. Uniaxial compressive strength of rock mass (dpeaa)DE-He213 Tunnel boring machine (dpeaa)DE-He213 Wave velocity (dpeaa)DE-He213 Catboost (dpeaa)DE-He213 Bayesian optimization (dpeaa)DE-He213 Wang, Ruirui (orcid)0000-0001-5375-4955 aut Wang, Jiwen aut Li, Ningbo aut Cao, Hongyi (orcid)0000-0002-3588-3478 aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 27(2023), 7 vom: 05. Juni, Seite 3148-3162 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:27 year:2023 number:7 day:05 month:06 pages:3148-3162 https://dx.doi.org/10.1007/s12205-023-2475-9 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_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 AR 27 2023 7 05 06 3148-3162 |
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Enthalten in KSCE journal of civil engineering 27(2023), 7 vom: 05. Juni, Seite 3148-3162 volume:27 year:2023 number:7 day:05 month:06 pages:3148-3162 |
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Uniaxial compressive strength of rock mass Tunnel boring machine Wave velocity Catboost Bayesian optimization |
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Wang, Yaxu @@aut@@ Wang, Ruirui @@aut@@ Wang, Jiwen @@aut@@ Li, Ningbo @@aut@@ Cao, Hongyi @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR052071626</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230628064833.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230628s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12205-023-2475-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR052071626</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12205-023-2475-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Yaxu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0009-0005-7217-2412</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Korean Society of Civil Engineers 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. 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Wang, Yaxu |
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Wang, Yaxu misc Uniaxial compressive strength of rock mass misc Tunnel boring machine misc Wave velocity misc Catboost misc Bayesian optimization A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm |
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A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm Uniaxial compressive strength of rock mass (dpeaa)DE-He213 Tunnel boring machine (dpeaa)DE-He213 Wave velocity (dpeaa)DE-He213 Catboost (dpeaa)DE-He213 Bayesian optimization (dpeaa)DE-He213 |
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misc Uniaxial compressive strength of rock mass misc Tunnel boring machine misc Wave velocity misc Catboost misc Bayesian optimization |
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misc Uniaxial compressive strength of rock mass misc Tunnel boring machine misc Wave velocity misc Catboost misc Bayesian optimization |
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A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm |
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A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm |
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Wang, Yaxu |
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Wang, Yaxu Wang, Ruirui Wang, Jiwen Li, Ningbo Cao, Hongyi |
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Wang, Yaxu |
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rock mass strength prediction method integrating wave velocity and operational parameters based on the bayesian optimization catboost algorithm |
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A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm |
| abstract |
Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. © Korean Society of Civil Engineers 2023 |
| abstractGer |
Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. © Korean Society of Civil Engineers 2023 |
| abstract_unstemmed |
Abstract Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering. © Korean Society of Civil Engineers 2023 |
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A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm |
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https://dx.doi.org/10.1007/s12205-023-2475-9 |
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Wang, Ruirui Wang, Jiwen Li, Ningbo Cao, Hongyi |
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Wang, Ruirui Wang, Jiwen Li, Ningbo Cao, Hongyi |
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10.1007/s12205-023-2475-9 |
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2024-07-04T01:07:31.264Z |
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|
| score |
7.399766 |