Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete

Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expr...

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Autores principales:	Hong-Hu Chu, Mohsin Ali Khan, Muhammad Javed, Adeel Zafar, M. Ijaz Khan, Hisham Alabduljabbar, Sumaira Qayyum
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Artificial intelligence (AI) Gene expression programming (GEP) Multi expression programming (MEP) Fly-ash Waste material Geopolymer concrete (GPC) Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/5a4058d33bd748e8b41479e25320ab54
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spelling	oai:doaj.org-article:5a4058d33bd748e8b41479e25320ab542021-11-22T04:21:52ZSustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete2090-447910.1016/j.asej.2021.03.018https://doaj.org/article/5a4058d33bd748e8b41479e25320ab542021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001830https://doaj.org/toc/2090-4479Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expression programming (GEP) and multi expression programming (MEP) are employed for forecasting the compressive strength of FGC. The comprehensive database is constructed comprising of 311 compressive strength results. The obtained equations relate the compressive strength of FGC with eight most effective parameters i.e., curing regime (T), time for curing (t) in hours, age of samples (A) in days, percentage of total aggregate by volume (% Ag), molarity of sodium hydroxide (NaOH) solution (M), silica (SiO2) solids percentage in sodium silicate (Na2SiO3) solution (%S), superplasticizer (%P) and extra water (%EW) as percent FA. The accurateness and predictive capacity of both GEP and MEP model is assessed via statistical checks, external validation criteria suggested by different researcher and then compared with linear regression (LR) and non-linear regression (NLR) models. In comparison with MEP equation, the GEP equation has lesser statistical error and higher correlation coefficient. Also, the GEP equation is short and it would be easy to use in the field. So, the GEP model is further utilized for sensitivity and parametric study. This research will increase the re-usage of hazardous FA in the development of green concrete that would leads to environmental safety and monetarist reliefs.Hong-Hu ChuMohsin Ali KhanMuhammad JavedAdeel ZafarM. Ijaz KhanHisham AlabduljabbarSumaira QayyumElsevierarticleArtificial intelligence (AI)Gene expression programming (GEP)Multi expression programming (MEP)Fly-ashWaste materialGeopolymer concrete (GPC)Engineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3603-3617 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence (AI) Gene expression programming (GEP) Multi expression programming (MEP) Fly-ash Waste material Geopolymer concrete (GPC) Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Artificial intelligence (AI) Gene expression programming (GEP) Multi expression programming (MEP) Fly-ash Waste material Geopolymer concrete (GPC) Engineering (General). Civil engineering (General) TA1-2040 Hong-Hu Chu Mohsin Ali Khan Muhammad Javed Adeel Zafar M. Ijaz Khan Hisham Alabduljabbar Sumaira Qayyum Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
description	Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expression programming (GEP) and multi expression programming (MEP) are employed for forecasting the compressive strength of FGC. The comprehensive database is constructed comprising of 311 compressive strength results. The obtained equations relate the compressive strength of FGC with eight most effective parameters i.e., curing regime (T), time for curing (t) in hours, age of samples (A) in days, percentage of total aggregate by volume (% Ag), molarity of sodium hydroxide (NaOH) solution (M), silica (SiO2) solids percentage in sodium silicate (Na2SiO3) solution (%S), superplasticizer (%P) and extra water (%EW) as percent FA. The accurateness and predictive capacity of both GEP and MEP model is assessed via statistical checks, external validation criteria suggested by different researcher and then compared with linear regression (LR) and non-linear regression (NLR) models. In comparison with MEP equation, the GEP equation has lesser statistical error and higher correlation coefficient. Also, the GEP equation is short and it would be easy to use in the field. So, the GEP model is further utilized for sensitivity and parametric study. This research will increase the re-usage of hazardous FA in the development of green concrete that would leads to environmental safety and monetarist reliefs.
format	article
author	Hong-Hu Chu Mohsin Ali Khan Muhammad Javed Adeel Zafar M. Ijaz Khan Hisham Alabduljabbar Sumaira Qayyum
author_facet	Hong-Hu Chu Mohsin Ali Khan Muhammad Javed Adeel Zafar M. Ijaz Khan Hisham Alabduljabbar Sumaira Qayyum
author_sort	Hong-Hu Chu
title	Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
title_short	Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
title_full	Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
title_fullStr	Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
title_full_unstemmed	Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
title_sort	sustainable use of fly-ash: use of gene-expression programming (gep) and multi-expression programming (mep) for forecasting the compressive strength geopolymer concrete
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5a4058d33bd748e8b41479e25320ab54
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Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete

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