Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization

This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framewo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yu, Hongjie [verfasserIn] Zhou, Xu [verfasserIn] Zhang, Xiaoli [verfasserIn] Mooney, Michael [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization

Übergeordnetes Werk:	Enthalten in: Automation in construction - Amsterdam [u.a.] : Elsevier Science Publ., 1992, 142
Übergeordnetes Werk:	volume:142

DOI / URN:	10.1016/j.autcon.2022.104457

Katalog-ID:	ELV05880529X

Internformat


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245	1	0	\|a Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
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520			\|a This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure.
650		4	\|a Earth pressure balance
650		4	\|a Tunneling performance
650		4	\|a Optimal operation
650		4	\|a Support vector regression
650		4	\|a Particle swarm optimization
700	1		\|a Zhou, Xu \|e verfasserin \|4 aut
700	1		\|a Zhang, Xiaoli \|e verfasserin \|4 aut
700	1		\|a Mooney, Michael \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Automation in construction \|d Amsterdam [u.a.] : Elsevier Science Publ., 1992 \|g 142 \|h Online-Ressource \|w (DE-627)320422259 \|w (DE-600)2002703-5 \|w (DE-576)094478813 \|7 nnns
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Indexfelder

author_variant	h y hy x z xz x z xz m m mm
matchkey_str	yuhongjiezhouxuzhangxiaolimooneymichael:2022----:nacnerhrsueaactnebrnmcieefracwtspotetrersi
hierarchy_sort_str	2022
bklnumber	56.03
publishDate	2022
allfields	10.1016/j.autcon.2022.104457 doi (DE-627)ELV05880529X (ELSEVIER)S0926-5805(22)00330-2 DE-627 ger DE-627 rda eng 690 VZ 56.03 bkl Yu, Hongjie verfasserin aut Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure. Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization Zhou, Xu verfasserin aut Zhang, Xiaoli verfasserin aut Mooney, Michael verfasserin aut Enthalten in Automation in construction Amsterdam [u.a.] : Elsevier Science Publ., 1992 142 Online-Ressource (DE-627)320422259 (DE-600)2002703-5 (DE-576)094478813 nnns volume:142 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 56.03 Methoden im Bauingenieurwesen VZ AR 142
spelling	10.1016/j.autcon.2022.104457 doi (DE-627)ELV05880529X (ELSEVIER)S0926-5805(22)00330-2 DE-627 ger DE-627 rda eng 690 VZ 56.03 bkl Yu, Hongjie verfasserin aut Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure. Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization Zhou, Xu verfasserin aut Zhang, Xiaoli verfasserin aut Mooney, Michael verfasserin aut Enthalten in Automation in construction Amsterdam [u.a.] : Elsevier Science Publ., 1992 142 Online-Ressource (DE-627)320422259 (DE-600)2002703-5 (DE-576)094478813 nnns volume:142 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 56.03 Methoden im Bauingenieurwesen VZ AR 142
allfields_unstemmed	10.1016/j.autcon.2022.104457 doi (DE-627)ELV05880529X (ELSEVIER)S0926-5805(22)00330-2 DE-627 ger DE-627 rda eng 690 VZ 56.03 bkl Yu, Hongjie verfasserin aut Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure. Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization Zhou, Xu verfasserin aut Zhang, Xiaoli verfasserin aut Mooney, Michael verfasserin aut Enthalten in Automation in construction Amsterdam [u.a.] : Elsevier Science Publ., 1992 142 Online-Ressource (DE-627)320422259 (DE-600)2002703-5 (DE-576)094478813 nnns volume:142 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 56.03 Methoden im Bauingenieurwesen VZ AR 142
allfieldsGer	10.1016/j.autcon.2022.104457 doi (DE-627)ELV05880529X (ELSEVIER)S0926-5805(22)00330-2 DE-627 ger DE-627 rda eng 690 VZ 56.03 bkl Yu, Hongjie verfasserin aut Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure. Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization Zhou, Xu verfasserin aut Zhang, Xiaoli verfasserin aut Mooney, Michael verfasserin aut Enthalten in Automation in construction Amsterdam [u.a.] : Elsevier Science Publ., 1992 142 Online-Ressource (DE-627)320422259 (DE-600)2002703-5 (DE-576)094478813 nnns volume:142 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 56.03 Methoden im Bauingenieurwesen VZ AR 142
allfieldsSound	10.1016/j.autcon.2022.104457 doi (DE-627)ELV05880529X (ELSEVIER)S0926-5805(22)00330-2 DE-627 ger DE-627 rda eng 690 VZ 56.03 bkl Yu, Hongjie verfasserin aut Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure. Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization Zhou, Xu verfasserin aut Zhang, Xiaoli verfasserin aut Mooney, Michael verfasserin aut Enthalten in Automation in construction Amsterdam [u.a.] : Elsevier Science Publ., 1992 142 Online-Ressource (DE-627)320422259 (DE-600)2002703-5 (DE-576)094478813 nnns volume:142 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 56.03 Methoden im Bauingenieurwesen VZ AR 142
language	English
source	Enthalten in Automation in construction 142 volume:142
sourceStr	Enthalten in Automation in construction 142 volume:142
format_phy_str_mv	Article
bklname	Methoden im Bauingenieurwesen
institution	findex.gbv.de
topic_facet	Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization
dewey-raw	690
isfreeaccess_bool	false
container_title	Automation in construction
authorswithroles_txt_mv	Yu, Hongjie @@aut@@ Zhou, Xu @@aut@@ Zhang, Xiaoli @@aut@@ Mooney, Michael @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320422259
dewey-sort	3690
id	ELV05880529X
language_de	englisch
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author	Yu, Hongjie
spellingShingle	Yu, Hongjie ddc 690 bkl 56.03 misc Earth pressure balance misc Tunneling performance misc Optimal operation misc Support vector regression misc Particle swarm optimization Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
authorStr	Yu, Hongjie
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topic_title	690 VZ 56.03 bkl Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization Earth pressure balance Tunneling performance Optimal operation Support vector regression Particle swarm optimization
topic	ddc 690 bkl 56.03 misc Earth pressure balance misc Tunneling performance misc Optimal operation misc Support vector regression misc Particle swarm optimization
topic_unstemmed	ddc 690 bkl 56.03 misc Earth pressure balance misc Tunneling performance misc Optimal operation misc Support vector regression misc Particle swarm optimization
topic_browse	ddc 690 bkl 56.03 misc Earth pressure balance misc Tunneling performance misc Optimal operation misc Support vector regression misc Particle swarm optimization
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title	Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
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title_full	Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
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author_browse	Yu, Hongjie Zhou, Xu Zhang, Xiaoli Mooney, Michael
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title_sort	enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
title_auth	Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
abstract	This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure.
abstractGer	This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure.
abstract_unstemmed	This paper combined data-driven modeling and optimal control for performance enhancement of earth pressure balance tunnel boring machine (EPBM). Two coupled processes, EPBM advance rate (AR) and cutterhead rotation torque, are modeled using support vector regression (SVR). An optimal control framework was formulated to maximize the AR, solved with particle swarm algorithm. Using the Seattle N125 project as case study, it is found the SVR model can predict EPBM AR with R 2 = 0.90, normalized root mean square error (nRMSE) = 0.30 and mean absolute percentage error (MAPE) = 31.2%, and R 2 = 0.65, nRMSE = 0.59 and MAPE = 6.7% for torque prediction. Compared to human operator, EPBM with optimal control can increase AR by 0.6–23.3 mm∕min on average, accompanied by an average torque reduction of 83.1 kN - m. It is found higher cutterhead rotation and lower chamber pressure always contribute to faster tunneling, but the optimal total thrust force to apply depends on the chamber pressure.
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title_short	Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
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author2	Zhou, Xu Zhang, Xiaoli Mooney, Michael
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doi_str	10.1016/j.autcon.2022.104457
up_date	2024-07-06T20:06:41.846Z
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Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization

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