Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar

Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmen...
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Autor*in:	Ahmad, Soran Abdrahman [verfasserIn] Rafiq, Serwan Khwrshid

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Compressive strength Flexural strength Modelling Mortar Waste glass

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Asian journal of civil engineering - Cham : Springer International Publishing, 2017, 25(2023), 1 vom: 16. Juni, Seite 19-37
Übergeordnetes Werk:	volume:25 ; year:2023 ; number:1 ; day:16 ; month:06 ; pages:19-37

Links:	Volltext

DOI / URN:	10.1007/s42107-023-00753-8

Katalog-ID:	SPR054312787

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allfields	10.1007/s42107-023-00753-8 doi (DE-627)SPR054312787 (SPR)s42107-023-00753-8-e DE-627 ger DE-627 rakwb eng Ahmad, Soran Abdrahman verfasserin aut Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. Compressive strength (dpeaa)DE-He213 Flexural strength (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Mortar (dpeaa)DE-He213 Waste glass (dpeaa)DE-He213 Rafiq, Serwan Khwrshid aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 16. Juni, Seite 19-37 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:16 month:06 pages:19-37 https://dx.doi.org/10.1007/s42107-023-00753-8 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_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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 25 2023 1 16 06 19-37
spelling	10.1007/s42107-023-00753-8 doi (DE-627)SPR054312787 (SPR)s42107-023-00753-8-e DE-627 ger DE-627 rakwb eng Ahmad, Soran Abdrahman verfasserin aut Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. Compressive strength (dpeaa)DE-He213 Flexural strength (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Mortar (dpeaa)DE-He213 Waste glass (dpeaa)DE-He213 Rafiq, Serwan Khwrshid aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 16. Juni, Seite 19-37 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:16 month:06 pages:19-37 https://dx.doi.org/10.1007/s42107-023-00753-8 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_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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 25 2023 1 16 06 19-37
allfields_unstemmed	10.1007/s42107-023-00753-8 doi (DE-627)SPR054312787 (SPR)s42107-023-00753-8-e DE-627 ger DE-627 rakwb eng Ahmad, Soran Abdrahman verfasserin aut Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. Compressive strength (dpeaa)DE-He213 Flexural strength (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Mortar (dpeaa)DE-He213 Waste glass (dpeaa)DE-He213 Rafiq, Serwan Khwrshid aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 16. Juni, Seite 19-37 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:16 month:06 pages:19-37 https://dx.doi.org/10.1007/s42107-023-00753-8 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_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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 25 2023 1 16 06 19-37
allfieldsGer	10.1007/s42107-023-00753-8 doi (DE-627)SPR054312787 (SPR)s42107-023-00753-8-e DE-627 ger DE-627 rakwb eng Ahmad, Soran Abdrahman verfasserin aut Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. Compressive strength (dpeaa)DE-He213 Flexural strength (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Mortar (dpeaa)DE-He213 Waste glass (dpeaa)DE-He213 Rafiq, Serwan Khwrshid aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 16. Juni, Seite 19-37 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:16 month:06 pages:19-37 https://dx.doi.org/10.1007/s42107-023-00753-8 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_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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 25 2023 1 16 06 19-37
allfieldsSound	10.1007/s42107-023-00753-8 doi (DE-627)SPR054312787 (SPR)s42107-023-00753-8-e DE-627 ger DE-627 rakwb eng Ahmad, Soran Abdrahman verfasserin aut Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. Compressive strength (dpeaa)DE-He213 Flexural strength (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Mortar (dpeaa)DE-He213 Waste glass (dpeaa)DE-He213 Rafiq, Serwan Khwrshid aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 16. Juni, Seite 19-37 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:16 month:06 pages:19-37 https://dx.doi.org/10.1007/s42107-023-00753-8 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_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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 25 2023 1 16 06 19-37
language	English
source	Enthalten in Asian journal of civil engineering 25(2023), 1 vom: 16. Juni, Seite 19-37 volume:25 year:2023 number:1 day:16 month:06 pages:19-37
sourceStr	Enthalten in Asian journal of civil engineering 25(2023), 1 vom: 16. Juni, Seite 19-37 volume:25 year:2023 number:1 day:16 month:06 pages:19-37
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Compressive strength Flexural strength Modelling Mortar Waste glass
isfreeaccess_bool	false
container_title	Asian journal of civil engineering
authorswithroles_txt_mv	Ahmad, Soran Abdrahman @@aut@@ Rafiq, Serwan Khwrshid @@aut@@
publishDateDaySort_date	2023-06-16T00:00:00Z
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author	Ahmad, Soran Abdrahman
spellingShingle	Ahmad, Soran Abdrahman misc Compressive strength misc Flexural strength misc Modelling misc Mortar misc Waste glass Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar
authorStr	Ahmad, Soran Abdrahman
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topic_title	Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar Compressive strength (dpeaa)DE-He213 Flexural strength (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Mortar (dpeaa)DE-He213 Waste glass (dpeaa)DE-He213
topic	misc Compressive strength misc Flexural strength misc Modelling misc Mortar misc Waste glass
topic_unstemmed	misc Compressive strength misc Flexural strength misc Modelling misc Mortar misc Waste glass
topic_browse	misc Compressive strength misc Flexural strength misc Modelling misc Mortar misc Waste glass
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title	Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar
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title_full	Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar
author_sort	Ahmad, Soran Abdrahman
journal	Asian journal of civil engineering
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author_browse	Ahmad, Soran Abdrahman Rafiq, Serwan Khwrshid
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author-letter	Ahmad, Soran Abdrahman
doi_str_mv	10.1007/s42107-023-00753-8
title_sort	numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar
title_auth	Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar
abstract	Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Construction production (industry) is the biggest natural resource user, and environmental sustainability is threatened. The environmental and economic concern is the most important challenge the construction industry (concrete and mortar) faces. In this article, the economic and environmental problems are dealt with by the use of waste glass as a partial replacement of fine aggregates in a mortar by using fly ash and granulated blast furnace slag (GGBS); for that reason, 116 data are collected from previous paper with different parameter and statically analyzed, and represented in three models (Linear regression model (LRM), non-linear regression model (NLRM) and Artificial neural network (ANN)) for predicted compressive strength of mortar and correlation to predict flexural strength. In the modeling process, these variables are important and affect the value of compressive strength, such as curing time, w/c, cement content, sand content, fly ash, GGBS, and waste glass content. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the efficiency and performance of the proposed models. The obtained results showed that the ANN-model showed better efficiency for predicting the compressive strength of mortar mixtures containing fine glass compared to the LR and NLR model. The SI and OBJ values of the LR model were 81% and 166% higher than the ANN model, and for NLR were 71% and 124% higher than the ANN model. The correlation between measured compressive strength and flexural strength was with an R-square value of 0.76. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar
url	https://dx.doi.org/10.1007/s42107-023-00753-8
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author2	Rafiq, Serwan Khwrshid
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doi_str	10.1007/s42107-023-00753-8
up_date	2024-07-04T01:00:55.308Z
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Numerical modeling to predict the impact of granular glass replacement on mechanical properties of mortar

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