Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays

Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the infl...
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Gespeichert in:

Autor*in:	Nagaraju, T. Vamsi [verfasserIn] Prasad, Ch. Durga [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Expansive clay Free swell index Compression index PSO

Übergeordnetes Werk:	Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 13(2020), 9 vom: 27. Apr.
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:9 ; day:27 ; month:04

Links:	Volltext

DOI / URN:	10.1007/s12517-020-05287-w

Katalog-ID:	SPR039534162

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520			\|a Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper.
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allfields	10.1007/s12517-020-05287-w doi (DE-627)SPR039534162 (SPR)s12517-020-05287-w-e DE-627 ger DE-627 rakwb eng 550 ASE Nagaraju, T. Vamsi verfasserin aut Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper. Expansive clay (dpeaa)DE-He213 Free swell index (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Prasad, Ch. Durga verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 9 vom: 27. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:9 day:27 month:04 https://dx.doi.org/10.1007/s12517-020-05287-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 9 27 04
spelling	10.1007/s12517-020-05287-w doi (DE-627)SPR039534162 (SPR)s12517-020-05287-w-e DE-627 ger DE-627 rakwb eng 550 ASE Nagaraju, T. Vamsi verfasserin aut Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper. Expansive clay (dpeaa)DE-He213 Free swell index (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Prasad, Ch. Durga verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 9 vom: 27. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:9 day:27 month:04 https://dx.doi.org/10.1007/s12517-020-05287-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 9 27 04
allfields_unstemmed	10.1007/s12517-020-05287-w doi (DE-627)SPR039534162 (SPR)s12517-020-05287-w-e DE-627 ger DE-627 rakwb eng 550 ASE Nagaraju, T. Vamsi verfasserin aut Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper. Expansive clay (dpeaa)DE-He213 Free swell index (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Prasad, Ch. Durga verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 9 vom: 27. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:9 day:27 month:04 https://dx.doi.org/10.1007/s12517-020-05287-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 9 27 04
allfieldsGer	10.1007/s12517-020-05287-w doi (DE-627)SPR039534162 (SPR)s12517-020-05287-w-e DE-627 ger DE-627 rakwb eng 550 ASE Nagaraju, T. Vamsi verfasserin aut Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper. Expansive clay (dpeaa)DE-He213 Free swell index (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Prasad, Ch. Durga verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 9 vom: 27. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:9 day:27 month:04 https://dx.doi.org/10.1007/s12517-020-05287-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 9 27 04
allfieldsSound	10.1007/s12517-020-05287-w doi (DE-627)SPR039534162 (SPR)s12517-020-05287-w-e DE-627 ger DE-627 rakwb eng 550 ASE Nagaraju, T. Vamsi verfasserin aut Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper. Expansive clay (dpeaa)DE-He213 Free swell index (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Prasad, Ch. Durga verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 9 vom: 27. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:9 day:27 month:04 https://dx.doi.org/10.1007/s12517-020-05287-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 9 27 04
language	English
source	Enthalten in Arabian journal of geosciences 13(2020), 9 vom: 27. Apr. volume:13 year:2020 number:9 day:27 month:04
sourceStr	Enthalten in Arabian journal of geosciences 13(2020), 9 vom: 27. Apr. volume:13 year:2020 number:9 day:27 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Expansive clay Free swell index Compression index PSO
dewey-raw	550
isfreeaccess_bool	false
container_title	Arabian journal of geosciences
authorswithroles_txt_mv	Nagaraju, T. Vamsi @@aut@@ Prasad, Ch. Durga @@aut@@
publishDateDaySort_date	2020-04-27T00:00:00Z
hierarchy_top_id	572421877
dewey-sort	3550
id	SPR039534162
language_de	englisch
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author	Nagaraju, T. Vamsi
spellingShingle	Nagaraju, T. Vamsi ddc 550 misc Expansive clay misc Free swell index misc Compression index misc PSO Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays
authorStr	Nagaraju, T. Vamsi
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topic_title	550 ASE Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays Expansive clay (dpeaa)DE-He213 Free swell index (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 PSO (dpeaa)DE-He213
topic	ddc 550 misc Expansive clay misc Free swell index misc Compression index misc PSO
topic_unstemmed	ddc 550 misc Expansive clay misc Free swell index misc Compression index misc PSO
topic_browse	ddc 550 misc Expansive clay misc Free swell index misc Compression index misc PSO
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title	Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays
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title_full	Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays
author_sort	Nagaraju, T. Vamsi
journal	Arabian journal of geosciences
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author_browse	Nagaraju, T. Vamsi Prasad, Ch. Durga
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author-letter	Nagaraju, T. Vamsi
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title_sort	swarm-assisted multiple linear regression models for compression index (cc) estimation of blended expansive clays
title_auth	Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays
abstract	Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper.
abstractGer	Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper.
abstract_unstemmed	Abstract Many innovative ameliorating techniques including chemical stabilization have been in practice for enhancing the behavior of expansive clays. However, cement and lime are quite effective and successful chemical additives. Based on an experimental investigation, this paper discusses the influence of cement and lime on significant index and swelling properties of expansive clays. Cement and lime were mixed in various quantities (depending on chemicals) with highly swelling montmorillonitic expansive clays for investigating the variation of liquid limit (LL), plasticity index (PI), free swell index (FSI), rate of heave, swell potential (S%), and compression index (Cc). In geotechnical designs, the compression index is one of the major parameters to determine settlements in soils. This paper employed the heuristic models for the prediction of Cc of blended expansive clays. Particle swarm optimization (PSO) is an efficient and inspired computational search for various engineering disciplines. Hence, the PSO technique is used to estimate the compression index value from available data using a linear model. With availability of limited test data, close estimation is possible with PSO for the prediction of Cc. In the linear model Cc equation, the effect of additional coefficients chosen in the PSO prediction model is the key factor presented in this paper.
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container_issue	9
title_short	Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays
url	https://dx.doi.org/10.1007/s12517-020-05287-w
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author2	Prasad, Ch. Durga
author2Str	Prasad, Ch. Durga
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doi_str	10.1007/s12517-020-05287-w
up_date	2024-07-04T00:22:31.979Z
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Swarm-assisted multiple linear regression models for compression index (Cc) estimation of blended expansive clays

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