A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete

Abstract The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristi...
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Autor*in:	Prakash, Shubhum [verfasserIn] Kumar, Sanjay Rai, Baboo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Ultrahigh performance concrete Artificial neural network Gorilla troops optimization Uncertainty analysis Accuracy matrix

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: 28. Juli, Seite 923-938
Übergeordnetes Werk:	volume:25 ; year:2023 ; number:1 ; day:28 ; month:07 ; pages:923-938

Links:	Volltext

DOI / URN:	10.1007/s42107-023-00822-y

Katalog-ID:	SPR054313473

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245	1	2	\|a A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
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520			\|a Abstract The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects.
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allfields	10.1007/s42107-023-00822-y doi (DE-627)SPR054313473 (SPR)s42107-023-00822-y-e DE-627 ger DE-627 rakwb eng Prakash, Shubhum verfasserin aut A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. Ultrahigh performance concrete (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Gorilla troops optimization (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Accuracy matrix (dpeaa)DE-He213 Kumar, Sanjay aut Rai, Baboo aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 28. Juli, Seite 923-938 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:28 month:07 pages:923-938 https://dx.doi.org/10.1007/s42107-023-00822-y 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 28 07 923-938
spelling	10.1007/s42107-023-00822-y doi (DE-627)SPR054313473 (SPR)s42107-023-00822-y-e DE-627 ger DE-627 rakwb eng Prakash, Shubhum verfasserin aut A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. Ultrahigh performance concrete (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Gorilla troops optimization (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Accuracy matrix (dpeaa)DE-He213 Kumar, Sanjay aut Rai, Baboo aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 28. Juli, Seite 923-938 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:28 month:07 pages:923-938 https://dx.doi.org/10.1007/s42107-023-00822-y 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 28 07 923-938
allfields_unstemmed	10.1007/s42107-023-00822-y doi (DE-627)SPR054313473 (SPR)s42107-023-00822-y-e DE-627 ger DE-627 rakwb eng Prakash, Shubhum verfasserin aut A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. Ultrahigh performance concrete (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Gorilla troops optimization (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Accuracy matrix (dpeaa)DE-He213 Kumar, Sanjay aut Rai, Baboo aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 28. Juli, Seite 923-938 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:28 month:07 pages:923-938 https://dx.doi.org/10.1007/s42107-023-00822-y 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 28 07 923-938
allfieldsGer	10.1007/s42107-023-00822-y doi (DE-627)SPR054313473 (SPR)s42107-023-00822-y-e DE-627 ger DE-627 rakwb eng Prakash, Shubhum verfasserin aut A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. Ultrahigh performance concrete (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Gorilla troops optimization (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Accuracy matrix (dpeaa)DE-He213 Kumar, Sanjay aut Rai, Baboo aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 28. Juli, Seite 923-938 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:28 month:07 pages:923-938 https://dx.doi.org/10.1007/s42107-023-00822-y 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 28 07 923-938
allfieldsSound	10.1007/s42107-023-00822-y doi (DE-627)SPR054313473 (SPR)s42107-023-00822-y-e DE-627 ger DE-627 rakwb eng Prakash, Shubhum verfasserin aut A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. Ultrahigh performance concrete (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Gorilla troops optimization (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Accuracy matrix (dpeaa)DE-He213 Kumar, Sanjay aut Rai, Baboo aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 25(2023), 1 vom: 28. Juli, Seite 923-938 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2023 number:1 day:28 month:07 pages:923-938 https://dx.doi.org/10.1007/s42107-023-00822-y 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 28 07 923-938
language	English
source	Enthalten in Asian journal of civil engineering 25(2023), 1 vom: 28. Juli, Seite 923-938 volume:25 year:2023 number:1 day:28 month:07 pages:923-938
sourceStr	Enthalten in Asian journal of civil engineering 25(2023), 1 vom: 28. Juli, Seite 923-938 volume:25 year:2023 number:1 day:28 month:07 pages:923-938
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Ultrahigh performance concrete Artificial neural network Gorilla troops optimization Uncertainty analysis Accuracy matrix
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container_title	Asian journal of civil engineering
authorswithroles_txt_mv	Prakash, Shubhum @@aut@@ Kumar, Sanjay @@aut@@ Rai, Baboo @@aut@@
publishDateDaySort_date	2023-07-28T00:00:00Z
hierarchy_top_id	101384565X
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author	Prakash, Shubhum
spellingShingle	Prakash, Shubhum misc Ultrahigh performance concrete misc Artificial neural network misc Gorilla troops optimization misc Uncertainty analysis misc Accuracy matrix A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
authorStr	Prakash, Shubhum
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topic_title	A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete Ultrahigh performance concrete (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Gorilla troops optimization (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Accuracy matrix (dpeaa)DE-He213
topic	misc Ultrahigh performance concrete misc Artificial neural network misc Gorilla troops optimization misc Uncertainty analysis misc Accuracy matrix
topic_unstemmed	misc Ultrahigh performance concrete misc Artificial neural network misc Gorilla troops optimization misc Uncertainty analysis misc Accuracy matrix
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title	A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
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title_full	A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
author_sort	Prakash, Shubhum
journal	Asian journal of civil engineering
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author_browse	Prakash, Shubhum Kumar, Sanjay Rai, Baboo
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doi_str_mv	10.1007/s42107-023-00822-y
title_sort	new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
title_auth	A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
abstract	Abstract The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. © 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. © 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 The present work presents hybrid machine learning paradigms built with nine widely used optimization algorithms in determining the compressive strength (CS) of ultrahigh performance concrete (UHPC). Nine hybrid artificial neural network (ANN) models were constructed using nine meta-heuristic algorithms such as Ant Lion Optimization (ALO), Grey Wolf Optimization (GWO),Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Harish Hawk Optimization (HHO) Slime Mould Optimization (SMO), Gorilla Troops Optimization (GTO). A total number of 308 observations were acquired and modelled to estimate the CS of UHPC concrete produced with manufactured sand. The developed hybrid model of ANN and Gorilla Troop Optimization (i.e., ANN-GTO) achieved the most accurate prediction of the CS with R2 = 0.9629, VAF = 96.28, RMSE = 0.0518 in the model construction stage and R2 = 0.9578, VAF = 95.78, RMSE = 0.0540 in the testing phase. The outcomes of the sensitivity analysis show that the developed ANN-FF accurately captures the strength of the relationship between influential variables and the CS concrete. The assessment of results was investigated based on the Taylor diagram, accuracy matrix, and uncertainty analysis. Overall, the built ANN-GTO secured the first rank in terms of uncertainty analysis. According to the results, the built ANN-GTO can be a new option for assisting engineers in civil engineering projects. © 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	A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete
url	https://dx.doi.org/10.1007/s42107-023-00822-y
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author2	Kumar, Sanjay Rai, Baboo
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doi_str	10.1007/s42107-023-00822-y
up_date	2024-07-04T01:01:08.080Z
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A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete

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