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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Abstract 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.
AbstractList 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.
Author Khan, Mohsin Ali
Zafar, Adeel
Alabduljabbar, Hisham
Javed, Muhammad
Ijaz Khan, M.
Chu, Hong-Hu
Qayyum, Sumaira
Author_xml – sequence: 1
  givenname: Hong-Hu
  surname: Chu
  fullname: Chu, Hong-Hu
  organization: College of Civil Engineering, Hunan University, Changsha 410082, PR China
– sequence: 2
  givenname: Mohsin Ali
  surname: Khan
  fullname: Khan, Mohsin Ali
  organization: Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan
– sequence: 3
  givenname: Muhammad
  surname: Javed
  fullname: Javed, Muhammad
  organization: Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
– sequence: 4
  givenname: Adeel
  surname: Zafar
  fullname: Zafar, Adeel
  organization: Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan
– sequence: 5
  givenname: M.
  surname: Ijaz Khan
  fullname: Ijaz Khan, M.
  email: mikhan@math.qau.edu.pk
  organization: Department of Mathematics and Statistics, Riphah International University, I-14, Islamabad 44000, Pakistan
– sequence: 6
  givenname: Hisham
  surname: Alabduljabbar
  fullname: Alabduljabbar, Hisham
  organization: Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
– sequence: 7
  givenname: Sumaira
  surname: Qayyum
  fullname: Qayyum, Sumaira
  organization: Department of Mathematics, Quaid-I-Azam University, 45320, Islamabad 44000, Pakistan
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Copyright 2021
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Issue 4
Keywords Geopolymer concrete (GPC)
Gene expression programming (GEP)
Fly-ash
Waste material
Artificial intelligence (AI)
Multi expression programming (MEP)
Language English
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Snippet 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...
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SubjectTerms Artificial intelligence (AI)
Fly-ash
Gene expression programming (GEP)
Geopolymer concrete (GPC)
Multi expression programming (MEP)
Waste material
Title Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
URI https://dx.doi.org/10.1016/j.asej.2021.03.018
https://doaj.org/article/5a4058d33bd748e8b41479e25320ab54
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