Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total...
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Autor*in:	Fazal E. Jalal [verfasserIn] Mudassir Iqbal [verfasserIn] Mohsin Ali Khan [verfasserIn] Babatunde A. Salami [verfasserIn] Shahid Ullah [verfasserIn] Hayat Khan [verfasserIn] Marwa Nabil [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Übergeordnetes Werk:	In: Advances in Materials Science and Engineering - Hindawi Limited, 2009, (2023)
Übergeordnetes Werk:	year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1155/2023/1827117

Katalog-ID:	DOAJ080933904

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245	1	0	\|a Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
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520			\|a In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.
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allfields	10.1155/2023/1827117 doi (DE-627)DOAJ080933904 (DE-599)DOAJe4496e918e6948809118736965ae628d DE-627 ger DE-627 rakwb eng TA401-492 Fazal E. Jalal verfasserin aut Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing. Materials of engineering and construction. Mechanics of materials Mudassir Iqbal verfasserin aut Mohsin Ali Khan verfasserin aut Babatunde A. Salami verfasserin aut Shahid Ullah verfasserin aut Hayat Khan verfasserin aut Marwa Nabil verfasserin aut In Advances in Materials Science and Engineering Hindawi Limited, 2009 (2023) (DE-627)602540895 (DE-600)2501025-6 16878442 nnns year:2023 https://doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/article/e4496e918e6948809118736965ae628d kostenfrei http://dx.doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/toc/1687-8442 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4700 AR 2023
spelling	10.1155/2023/1827117 doi (DE-627)DOAJ080933904 (DE-599)DOAJe4496e918e6948809118736965ae628d DE-627 ger DE-627 rakwb eng TA401-492 Fazal E. Jalal verfasserin aut Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing. Materials of engineering and construction. Mechanics of materials Mudassir Iqbal verfasserin aut Mohsin Ali Khan verfasserin aut Babatunde A. Salami verfasserin aut Shahid Ullah verfasserin aut Hayat Khan verfasserin aut Marwa Nabil verfasserin aut In Advances in Materials Science and Engineering Hindawi Limited, 2009 (2023) (DE-627)602540895 (DE-600)2501025-6 16878442 nnns year:2023 https://doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/article/e4496e918e6948809118736965ae628d kostenfrei http://dx.doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/toc/1687-8442 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4700 AR 2023
allfields_unstemmed	10.1155/2023/1827117 doi (DE-627)DOAJ080933904 (DE-599)DOAJe4496e918e6948809118736965ae628d DE-627 ger DE-627 rakwb eng TA401-492 Fazal E. Jalal verfasserin aut Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing. Materials of engineering and construction. Mechanics of materials Mudassir Iqbal verfasserin aut Mohsin Ali Khan verfasserin aut Babatunde A. Salami verfasserin aut Shahid Ullah verfasserin aut Hayat Khan verfasserin aut Marwa Nabil verfasserin aut In Advances in Materials Science and Engineering Hindawi Limited, 2009 (2023) (DE-627)602540895 (DE-600)2501025-6 16878442 nnns year:2023 https://doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/article/e4496e918e6948809118736965ae628d kostenfrei http://dx.doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/toc/1687-8442 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4700 AR 2023
allfieldsGer	10.1155/2023/1827117 doi (DE-627)DOAJ080933904 (DE-599)DOAJe4496e918e6948809118736965ae628d DE-627 ger DE-627 rakwb eng TA401-492 Fazal E. Jalal verfasserin aut Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing. Materials of engineering and construction. Mechanics of materials Mudassir Iqbal verfasserin aut Mohsin Ali Khan verfasserin aut Babatunde A. Salami verfasserin aut Shahid Ullah verfasserin aut Hayat Khan verfasserin aut Marwa Nabil verfasserin aut In Advances in Materials Science and Engineering Hindawi Limited, 2009 (2023) (DE-627)602540895 (DE-600)2501025-6 16878442 nnns year:2023 https://doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/article/e4496e918e6948809118736965ae628d kostenfrei http://dx.doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/toc/1687-8442 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4700 AR 2023
allfieldsSound	10.1155/2023/1827117 doi (DE-627)DOAJ080933904 (DE-599)DOAJe4496e918e6948809118736965ae628d DE-627 ger DE-627 rakwb eng TA401-492 Fazal E. Jalal verfasserin aut Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing. Materials of engineering and construction. Mechanics of materials Mudassir Iqbal verfasserin aut Mohsin Ali Khan verfasserin aut Babatunde A. Salami verfasserin aut Shahid Ullah verfasserin aut Hayat Khan verfasserin aut Marwa Nabil verfasserin aut In Advances in Materials Science and Engineering Hindawi Limited, 2009 (2023) (DE-627)602540895 (DE-600)2501025-6 16878442 nnns year:2023 https://doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/article/e4496e918e6948809118736965ae628d kostenfrei http://dx.doi.org/10.1155/2023/1827117 kostenfrei https://doaj.org/toc/1687-8442 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4700 AR 2023
language	English
source	In Advances in Materials Science and Engineering (2023) year:2023
sourceStr	In Advances in Materials Science and Engineering (2023) year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Materials of engineering and construction. Mechanics of materials
isfreeaccess_bool	true
container_title	Advances in Materials Science and Engineering
authorswithroles_txt_mv	Fazal E. Jalal @@aut@@ Mudassir Iqbal @@aut@@ Mohsin Ali Khan @@aut@@ Babatunde A. Salami @@aut@@ Shahid Ullah @@aut@@ Hayat Khan @@aut@@ Marwa Nabil @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	602540895
id	DOAJ080933904
language_de	englisch
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author	Fazal E. Jalal
spellingShingle	Fazal E. Jalal misc TA401-492 misc Materials of engineering and construction. Mechanics of materials Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
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topic_title	TA401-492 Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
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title	Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
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title_full	Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
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title_sort	indirect estimation of swelling pressure of expansive soil: gep versus mep modelling
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title_auth	Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
abstract	In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.
abstractGer	In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.
abstract_unstemmed	In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.
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title_short	Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling
url	https://doi.org/10.1155/2023/1827117 https://doaj.org/article/e4496e918e6948809118736965ae628d http://dx.doi.org/10.1155/2023/1827117 https://doaj.org/toc/1687-8442
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Jalal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Materials of engineering and construction. 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Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

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