Statistical analysis of geo-electric imaging and geotechnical test results – a case study

Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Ele...
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Autor*in:	Sastry, Rambhatla G [verfasserIn] Chahar, Sumedha [verfasserIn] Viladkar, Manohar N [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	SPT ERT/IPI multiple regression analysis geoelectric imaging geotechnical tests linear and nonlinear multiple regression analysis

Übergeordnetes Werk:	Enthalten in: Journal of earth system science - Bangalore : Indian Acad. of Sciences, 1980, 127(2018), 5 vom: 25. Juni
Übergeordnetes Werk:	volume:127 ; year:2018 ; number:5 ; day:25 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s12040-018-0963-y

Katalog-ID:	SPR024064351

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520			\|a Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations.
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912			\|a GBV_ILN_161
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912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
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912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
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912			\|a GBV_ILN_2108
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author_variant	r g s rg rgs s c sc m n v mn mnv
matchkey_str	article:0973774X:2018----::ttsiaaayiogolcrcmgnadetcncl
hierarchy_sort_str	2018
bklnumber	38.00 39.29
publishDate	2018
allfields	10.1007/s12040-018-0963-y doi (DE-627)SPR024064351 (SPR)s12040-018-0963-y-e DE-627 ger DE-627 rakwb eng 500 550 ASE 38.00 bkl 39.29 bkl Sastry, Rambhatla G verfasserin aut Statistical analysis of geo-electric imaging and geotechnical test results – a case study 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations. SPT (dpeaa)DE-He213 ERT/IPI (dpeaa)DE-He213 multiple regression analysis (dpeaa)DE-He213 geoelectric imaging (dpeaa)DE-He213 geotechnical tests (dpeaa)DE-He213 linear and nonlinear multiple regression analysis (dpeaa)DE-He213 Chahar, Sumedha verfasserin aut Viladkar, Manohar N verfasserin aut Enthalten in Journal of earth system science Bangalore : Indian Acad. of Sciences, 1980 127(2018), 5 vom: 25. Juni (DE-627)349877327 (DE-600)2081712-5 0973-774X nnns volume:127 year:2018 number:5 day:25 month:06 https://dx.doi.org/10.1007/s12040-018-0963-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-AST SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE 39.29 ASE AR 127 2018 5 25 06
spelling	10.1007/s12040-018-0963-y doi (DE-627)SPR024064351 (SPR)s12040-018-0963-y-e DE-627 ger DE-627 rakwb eng 500 550 ASE 38.00 bkl 39.29 bkl Sastry, Rambhatla G verfasserin aut Statistical analysis of geo-electric imaging and geotechnical test results – a case study 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations. SPT (dpeaa)DE-He213 ERT/IPI (dpeaa)DE-He213 multiple regression analysis (dpeaa)DE-He213 geoelectric imaging (dpeaa)DE-He213 geotechnical tests (dpeaa)DE-He213 linear and nonlinear multiple regression analysis (dpeaa)DE-He213 Chahar, Sumedha verfasserin aut Viladkar, Manohar N verfasserin aut Enthalten in Journal of earth system science Bangalore : Indian Acad. of Sciences, 1980 127(2018), 5 vom: 25. Juni (DE-627)349877327 (DE-600)2081712-5 0973-774X nnns volume:127 year:2018 number:5 day:25 month:06 https://dx.doi.org/10.1007/s12040-018-0963-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-AST SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE 39.29 ASE AR 127 2018 5 25 06
allfields_unstemmed	10.1007/s12040-018-0963-y doi (DE-627)SPR024064351 (SPR)s12040-018-0963-y-e DE-627 ger DE-627 rakwb eng 500 550 ASE 38.00 bkl 39.29 bkl Sastry, Rambhatla G verfasserin aut Statistical analysis of geo-electric imaging and geotechnical test results – a case study 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations. SPT (dpeaa)DE-He213 ERT/IPI (dpeaa)DE-He213 multiple regression analysis (dpeaa)DE-He213 geoelectric imaging (dpeaa)DE-He213 geotechnical tests (dpeaa)DE-He213 linear and nonlinear multiple regression analysis (dpeaa)DE-He213 Chahar, Sumedha verfasserin aut Viladkar, Manohar N verfasserin aut Enthalten in Journal of earth system science Bangalore : Indian Acad. of Sciences, 1980 127(2018), 5 vom: 25. Juni (DE-627)349877327 (DE-600)2081712-5 0973-774X nnns volume:127 year:2018 number:5 day:25 month:06 https://dx.doi.org/10.1007/s12040-018-0963-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-AST SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE 39.29 ASE AR 127 2018 5 25 06
allfieldsGer	10.1007/s12040-018-0963-y doi (DE-627)SPR024064351 (SPR)s12040-018-0963-y-e DE-627 ger DE-627 rakwb eng 500 550 ASE 38.00 bkl 39.29 bkl Sastry, Rambhatla G verfasserin aut Statistical analysis of geo-electric imaging and geotechnical test results – a case study 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations. SPT (dpeaa)DE-He213 ERT/IPI (dpeaa)DE-He213 multiple regression analysis (dpeaa)DE-He213 geoelectric imaging (dpeaa)DE-He213 geotechnical tests (dpeaa)DE-He213 linear and nonlinear multiple regression analysis (dpeaa)DE-He213 Chahar, Sumedha verfasserin aut Viladkar, Manohar N verfasserin aut Enthalten in Journal of earth system science Bangalore : Indian Acad. of Sciences, 1980 127(2018), 5 vom: 25. Juni (DE-627)349877327 (DE-600)2081712-5 0973-774X nnns volume:127 year:2018 number:5 day:25 month:06 https://dx.doi.org/10.1007/s12040-018-0963-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-AST SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE 39.29 ASE AR 127 2018 5 25 06
allfieldsSound	10.1007/s12040-018-0963-y doi (DE-627)SPR024064351 (SPR)s12040-018-0963-y-e DE-627 ger DE-627 rakwb eng 500 550 ASE 38.00 bkl 39.29 bkl Sastry, Rambhatla G verfasserin aut Statistical analysis of geo-electric imaging and geotechnical test results – a case study 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations. SPT (dpeaa)DE-He213 ERT/IPI (dpeaa)DE-He213 multiple regression analysis (dpeaa)DE-He213 geoelectric imaging (dpeaa)DE-He213 geotechnical tests (dpeaa)DE-He213 linear and nonlinear multiple regression analysis (dpeaa)DE-He213 Chahar, Sumedha verfasserin aut Viladkar, Manohar N verfasserin aut Enthalten in Journal of earth system science Bangalore : Indian Acad. of Sciences, 1980 127(2018), 5 vom: 25. Juni (DE-627)349877327 (DE-600)2081712-5 0973-774X nnns volume:127 year:2018 number:5 day:25 month:06 https://dx.doi.org/10.1007/s12040-018-0963-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-AST SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE 39.29 ASE AR 127 2018 5 25 06
language	English
source	Enthalten in Journal of earth system science 127(2018), 5 vom: 25. Juni volume:127 year:2018 number:5 day:25 month:06
sourceStr	Enthalten in Journal of earth system science 127(2018), 5 vom: 25. Juni volume:127 year:2018 number:5 day:25 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	SPT ERT/IPI multiple regression analysis geoelectric imaging geotechnical tests linear and nonlinear multiple regression analysis
dewey-raw	500
isfreeaccess_bool	false
container_title	Journal of earth system science
authorswithroles_txt_mv	Sastry, Rambhatla G @@aut@@ Chahar, Sumedha @@aut@@ Viladkar, Manohar N @@aut@@
publishDateDaySort_date	2018-06-25T00:00:00Z
hierarchy_top_id	349877327
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author	Sastry, Rambhatla G
spellingShingle	Sastry, Rambhatla G ddc 500 bkl 38.00 bkl 39.29 misc SPT misc ERT/IPI misc multiple regression analysis misc geoelectric imaging misc geotechnical tests misc linear and nonlinear multiple regression analysis Statistical analysis of geo-electric imaging and geotechnical test results – a case study
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topic_title	500 550 ASE 38.00 bkl 39.29 bkl Statistical analysis of geo-electric imaging and geotechnical test results – a case study SPT (dpeaa)DE-He213 ERT/IPI (dpeaa)DE-He213 multiple regression analysis (dpeaa)DE-He213 geoelectric imaging (dpeaa)DE-He213 geotechnical tests (dpeaa)DE-He213 linear and nonlinear multiple regression analysis (dpeaa)DE-He213
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title	Statistical analysis of geo-electric imaging and geotechnical test results – a case study
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title_full	Statistical analysis of geo-electric imaging and geotechnical test results – a case study
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author_browse	Sastry, Rambhatla G Chahar, Sumedha Viladkar, Manohar N
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title_sort	statistical analysis of geo-electric imaging and geotechnical test results – a case study
title_auth	Statistical analysis of geo-electric imaging and geotechnical test results – a case study
abstract	Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations.
abstractGer	Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations.
abstract_unstemmed	Abstract For conjunctive use of geoelectric imaging and geotechnical site investigations in geotechnical characterization of major civil engineering construction sites, an objective assessment of influencing factors is important. Here, we present multiple regression analyses of both geoelectric (Electrical Resistivity Tomography, ERT; Induced Polarization Imaging, IPI) and geotechnical site investigations (Standard Penetration Test, SPT) for two profiles at a construction site for CGEWHO Complex in Greater Noida region, Delhi to assess the role of influencing formation factors like sand, fines and water content. Achieved results show that SPT ‘N’ and IPI are well predicted by a linear multiple regression. On an average, the nonlinear regression has improved predicted SPT ‘N’, resistivity and chargeability by 28.55%, 22.45% and 9.58%, respectively. The influence of sand and fines content is more than that of water content in the prediction of chargeability and SPT ‘N’. RMS error is less in prediction of IPI chargeability (average error of 1.96%) in comparison to SPT ‘N’ value (average error of 11.35%). As factors affecting chargeability (IPI) and SPT ‘N’ are similar, non-invasive IPI can be used along with few geotechnical site investigations for detailed geotechnical site investigations.
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title_short	Statistical analysis of geo-electric imaging and geotechnical test results – a case study
url	https://dx.doi.org/10.1007/s12040-018-0963-y
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author2	Chahar, Sumedha Viladkar, Manohar N
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up_date	2024-07-03T23:15:52.603Z
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Statistical analysis of geo-electric imaging and geotechnical test results – a case study

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