Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq

Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged...
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Autor*in:	Mawlood, Yousif [verfasserIn] Salih, Ahmed [verfasserIn] Hummadi, Rizgar [verfasserIn] Hasan, Ahmed [verfasserIn] Ibrahim, Hawkar [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Unconfined compressive strength Compression index Statistical assessment Modelling

Übergeordnetes Werk:	Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 14(2021), 6 vom: März
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:6 ; month:03

Links:	Volltext

DOI / URN:	10.1007/s12517-021-06712-4

Katalog-ID:	SPR043466044

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245	1	0	\|a Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq
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520			\|a Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset.
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700	1		\|a Ibrahim, Hawkar \|e verfasserin \|4 aut
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allfields	10.1007/s12517-021-06712-4 doi (DE-627)SPR043466044 (DE-599)SPRs12517-021-06712-4-e (SPR)s12517-021-06712-4-e DE-627 ger DE-627 rakwb eng 550 ASE Mawlood, Yousif verfasserin aut Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset. Unconfined compressive strength (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 Statistical assessment (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Salih, Ahmed verfasserin aut Hummadi, Rizgar verfasserin aut Hasan, Ahmed verfasserin aut Ibrahim, Hawkar verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 6 vom: März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:6 month:03 https://dx.doi.org/10.1007/s12517-021-06712-4 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 14 2021 6 03
spelling	10.1007/s12517-021-06712-4 doi (DE-627)SPR043466044 (DE-599)SPRs12517-021-06712-4-e (SPR)s12517-021-06712-4-e DE-627 ger DE-627 rakwb eng 550 ASE Mawlood, Yousif verfasserin aut Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset. Unconfined compressive strength (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 Statistical assessment (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Salih, Ahmed verfasserin aut Hummadi, Rizgar verfasserin aut Hasan, Ahmed verfasserin aut Ibrahim, Hawkar verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 6 vom: März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:6 month:03 https://dx.doi.org/10.1007/s12517-021-06712-4 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 14 2021 6 03
allfields_unstemmed	10.1007/s12517-021-06712-4 doi (DE-627)SPR043466044 (DE-599)SPRs12517-021-06712-4-e (SPR)s12517-021-06712-4-e DE-627 ger DE-627 rakwb eng 550 ASE Mawlood, Yousif verfasserin aut Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset. Unconfined compressive strength (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 Statistical assessment (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Salih, Ahmed verfasserin aut Hummadi, Rizgar verfasserin aut Hasan, Ahmed verfasserin aut Ibrahim, Hawkar verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 6 vom: März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:6 month:03 https://dx.doi.org/10.1007/s12517-021-06712-4 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 14 2021 6 03
allfieldsGer	10.1007/s12517-021-06712-4 doi (DE-627)SPR043466044 (DE-599)SPRs12517-021-06712-4-e (SPR)s12517-021-06712-4-e DE-627 ger DE-627 rakwb eng 550 ASE Mawlood, Yousif verfasserin aut Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset. Unconfined compressive strength (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 Statistical assessment (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Salih, Ahmed verfasserin aut Hummadi, Rizgar verfasserin aut Hasan, Ahmed verfasserin aut Ibrahim, Hawkar verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 6 vom: März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:6 month:03 https://dx.doi.org/10.1007/s12517-021-06712-4 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 14 2021 6 03
allfieldsSound	10.1007/s12517-021-06712-4 doi (DE-627)SPR043466044 (DE-599)SPRs12517-021-06712-4-e (SPR)s12517-021-06712-4-e DE-627 ger DE-627 rakwb eng 550 ASE Mawlood, Yousif verfasserin aut Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset. Unconfined compressive strength (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 Statistical assessment (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Salih, Ahmed verfasserin aut Hummadi, Rizgar verfasserin aut Hasan, Ahmed verfasserin aut Ibrahim, Hawkar verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 6 vom: März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:6 month:03 https://dx.doi.org/10.1007/s12517-021-06712-4 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 14 2021 6 03
language	English
source	Enthalten in Arabian journal of geosciences 14(2021), 6 vom: März volume:14 year:2021 number:6 month:03
sourceStr	Enthalten in Arabian journal of geosciences 14(2021), 6 vom: März volume:14 year:2021 number:6 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Unconfined compressive strength Compression index Statistical assessment Modelling
dewey-raw	550
isfreeaccess_bool	false
container_title	Arabian journal of geosciences
authorswithroles_txt_mv	Mawlood, Yousif @@aut@@ Salih, Ahmed @@aut@@ Hummadi, Rizgar @@aut@@ Hasan, Ahmed @@aut@@ Ibrahim, Hawkar @@aut@@
publishDateDaySort_date	2021-03-01T00:00:00Z
hierarchy_top_id	572421877
dewey-sort	3550
id	SPR043466044
language_de	englisch
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author	Mawlood, Yousif
spellingShingle	Mawlood, Yousif ddc 550 misc Unconfined compressive strength misc Compression index misc Statistical assessment misc Modelling Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq
authorStr	Mawlood, Yousif
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topic_title	550 ASE Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq Unconfined compressive strength (dpeaa)DE-He213 Compression index (dpeaa)DE-He213 Statistical assessment (dpeaa)DE-He213 Modelling (dpeaa)DE-He213
topic	ddc 550 misc Unconfined compressive strength misc Compression index misc Statistical assessment misc Modelling
topic_unstemmed	ddc 550 misc Unconfined compressive strength misc Compression index misc Statistical assessment misc Modelling
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title	Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq
ctrlnum	(DE-627)SPR043466044 (DE-599)SPRs12517-021-06712-4-e (SPR)s12517-021-06712-4-e
title_full	Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq
author_sort	Mawlood, Yousif
journal	Arabian journal of geosciences
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author_browse	Mawlood, Yousif Salih, Ahmed Hummadi, Rizgar Hasan, Ahmed Ibrahim, Hawkar
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class	550 ASE
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author-letter	Mawlood, Yousif
doi_str_mv	10.1007/s12517-021-06712-4
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title_sort	comparison of artificial neural network (ann) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for erbil city soils, kurdistan-iraq
title_auth	Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq
abstract	Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset.
abstractGer	Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset.
abstract_unstemmed	Abstract The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset.
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title_short	Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq
url	https://dx.doi.org/10.1007/s12517-021-06712-4
remote_bool	true
author2	Salih, Ahmed Hummadi, Rizgar Hasan, Ahmed Ibrahim, Hawkar
author2Str	Salih, Ahmed Hummadi, Rizgar Hasan, Ahmed Ibrahim, Hawkar
ppnlink	572421877
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isOA_txt	false
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doi_str	10.1007/s12517-021-06712-4
up_date	2024-07-03T18:51:38.296Z
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The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density ($ γ_{d} $), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. 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Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq

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