Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS

Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Torabi, S. R. [verfasserIn] Shirazi, H. [verfasserIn] Hajali, H. [verfasserIn] Monjezi, M. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Geotechnical parameters Penetration rate Utilization factor Artificial neural network SPSS

Übergeordnetes Werk:	Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 6(2011), 4 vom: 15. Okt., Seite 1215-1227
Übergeordnetes Werk:	volume:6 ; year:2011 ; number:4 ; day:15 ; month:10 ; pages:1215-1227

Links:	Volltext

DOI / URN:	10.1007/s12517-011-0415-3

Katalog-ID:	SPR025934910

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520			\|a Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach.
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allfields	10.1007/s12517-011-0415-3 doi (DE-627)SPR025934910 (SPR)s12517-011-0415-3-e DE-627 ger DE-627 rakwb eng 550 ASE Torabi, S. R. verfasserin aut Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach. Geotechnical parameters (dpeaa)DE-He213 Penetration rate (dpeaa)DE-He213 Utilization factor (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 SPSS (dpeaa)DE-He213 Shirazi, H. verfasserin aut Hajali, H. verfasserin aut Monjezi, M. verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 6(2011), 4 vom: 15. Okt., Seite 1215-1227 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227 https://dx.doi.org/10.1007/s12517-011-0415-3 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_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_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2011 4 15 10 1215-1227
spelling	10.1007/s12517-011-0415-3 doi (DE-627)SPR025934910 (SPR)s12517-011-0415-3-e DE-627 ger DE-627 rakwb eng 550 ASE Torabi, S. R. verfasserin aut Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach. Geotechnical parameters (dpeaa)DE-He213 Penetration rate (dpeaa)DE-He213 Utilization factor (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 SPSS (dpeaa)DE-He213 Shirazi, H. verfasserin aut Hajali, H. verfasserin aut Monjezi, M. verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 6(2011), 4 vom: 15. Okt., Seite 1215-1227 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227 https://dx.doi.org/10.1007/s12517-011-0415-3 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_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_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2011 4 15 10 1215-1227
allfields_unstemmed	10.1007/s12517-011-0415-3 doi (DE-627)SPR025934910 (SPR)s12517-011-0415-3-e DE-627 ger DE-627 rakwb eng 550 ASE Torabi, S. R. verfasserin aut Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach. Geotechnical parameters (dpeaa)DE-He213 Penetration rate (dpeaa)DE-He213 Utilization factor (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 SPSS (dpeaa)DE-He213 Shirazi, H. verfasserin aut Hajali, H. verfasserin aut Monjezi, M. verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 6(2011), 4 vom: 15. Okt., Seite 1215-1227 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227 https://dx.doi.org/10.1007/s12517-011-0415-3 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_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_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2011 4 15 10 1215-1227
allfieldsGer	10.1007/s12517-011-0415-3 doi (DE-627)SPR025934910 (SPR)s12517-011-0415-3-e DE-627 ger DE-627 rakwb eng 550 ASE Torabi, S. R. verfasserin aut Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach. Geotechnical parameters (dpeaa)DE-He213 Penetration rate (dpeaa)DE-He213 Utilization factor (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 SPSS (dpeaa)DE-He213 Shirazi, H. verfasserin aut Hajali, H. verfasserin aut Monjezi, M. verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 6(2011), 4 vom: 15. Okt., Seite 1215-1227 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227 https://dx.doi.org/10.1007/s12517-011-0415-3 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_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_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2011 4 15 10 1215-1227
allfieldsSound	10.1007/s12517-011-0415-3 doi (DE-627)SPR025934910 (SPR)s12517-011-0415-3-e DE-627 ger DE-627 rakwb eng 550 ASE Torabi, S. R. verfasserin aut Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach. Geotechnical parameters (dpeaa)DE-He213 Penetration rate (dpeaa)DE-He213 Utilization factor (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 SPSS (dpeaa)DE-He213 Shirazi, H. verfasserin aut Hajali, H. verfasserin aut Monjezi, M. verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 6(2011), 4 vom: 15. Okt., Seite 1215-1227 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227 https://dx.doi.org/10.1007/s12517-011-0415-3 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_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_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2011 4 15 10 1215-1227
language	English
source	Enthalten in Arabian journal of geosciences 6(2011), 4 vom: 15. Okt., Seite 1215-1227 volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227
sourceStr	Enthalten in Arabian journal of geosciences 6(2011), 4 vom: 15. Okt., Seite 1215-1227 volume:6 year:2011 number:4 day:15 month:10 pages:1215-1227
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Geotechnical parameters Penetration rate Utilization factor Artificial neural network SPSS
dewey-raw	550
isfreeaccess_bool	false
container_title	Arabian journal of geosciences
authorswithroles_txt_mv	Torabi, S. R. @@aut@@ Shirazi, H. @@aut@@ Hajali, H. @@aut@@ Monjezi, M. @@aut@@
publishDateDaySort_date	2011-10-15T00:00:00Z
hierarchy_top_id	572421877
dewey-sort	3550
id	SPR025934910
language_de	englisch
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author	Torabi, S. R.
spellingShingle	Torabi, S. R. ddc 550 misc Geotechnical parameters misc Penetration rate misc Utilization factor misc Artificial neural network misc SPSS Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS
authorStr	Torabi, S. R.
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topic_title	550 ASE Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS Geotechnical parameters (dpeaa)DE-He213 Penetration rate (dpeaa)DE-He213 Utilization factor (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 SPSS (dpeaa)DE-He213
topic	ddc 550 misc Geotechnical parameters misc Penetration rate misc Utilization factor misc Artificial neural network misc SPSS
topic_unstemmed	ddc 550 misc Geotechnical parameters misc Penetration rate misc Utilization factor misc Artificial neural network misc SPSS
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title	Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS
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title_full	Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS
author_sort	Torabi, S. R.
journal	Arabian journal of geosciences
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author_browse	Torabi, S. R. Shirazi, H. Hajali, H. Monjezi, M.
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author-letter	Torabi, S. R.
doi_str_mv	10.1007/s12517-011-0415-3
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title_sort	study of the influence of geotechnical parameters on the tbm performance in tehran–shomal highway project using ann and spss
title_auth	Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS
abstract	Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach.
abstractGer	Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach.
abstract_unstemmed	Abstract Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach.
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title_short	Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS
url	https://dx.doi.org/10.1007/s12517-011-0415-3
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author2	Shirazi, H. Hajali, H. Monjezi, M.
author2Str	Shirazi, H. Hajali, H. Monjezi, M.
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doi_str	10.1007/s12517-011-0415-3
up_date	2024-07-03T17:50:33.890Z
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Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS

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