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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Vydané v:Ain Shams Engineering Journal Ročník 12; číslo 4; s. 3603 - 3617
Hlavní autori: Chu, Hong-Hu, Khan, Mohsin Ali, Javed, Muhammad, Zafar, Adeel, Ijaz Khan, M., Alabduljabbar, Hisham, Qayyum, Sumaira
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2021
Elsevier
Predmet:
Artificial intelligence (AI)
Fly-ash
Gene expression programming (GEP)
Geopolymer concrete (GPC)
Multi expression programming (MEP)
Waste material
Geopolymer concrete (GPC)
Gene expression programming (GEP)
Fly-ash
Waste material
Artificial intelligence (AI)
Multi expression programming (MEP)
ISSN:2090-4479
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Shrnutí: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.
ISSN:2090-4479
DOI:10.1016/j.asej.2021.03.018