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Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming

The central aim of this study is to evaluate the effect of polyethylene terephthalate (PET) alongside two supplementary cementitious materials (SCMs)—i.e., fly ash (FA) and silica fume (SF)—on the 28-day compressive strength (CS(28d)) of cementitious grouts by using. For the gene expression programm...

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Autores principales: Khan, Kaffayatullah, Jalal, Fazal E., Iqbal, Mudassir, Khan, Muhammad Imran, Amin, Muhammad Nasir, Al-Faiad, Majdi Adel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102582/
https://www.ncbi.nlm.nih.gov/pubmed/35591409
http://dx.doi.org/10.3390/ma15093077
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author Khan, Kaffayatullah
Jalal, Fazal E.
Iqbal, Mudassir
Khan, Muhammad Imran
Amin, Muhammad Nasir
Al-Faiad, Majdi Adel
author_facet Khan, Kaffayatullah
Jalal, Fazal E.
Iqbal, Mudassir
Khan, Muhammad Imran
Amin, Muhammad Nasir
Al-Faiad, Majdi Adel
author_sort Khan, Kaffayatullah
collection PubMed
description The central aim of this study is to evaluate the effect of polyethylene terephthalate (PET) alongside two supplementary cementitious materials (SCMs)—i.e., fly ash (FA) and silica fume (SF)—on the 28-day compressive strength (CS(28d)) of cementitious grouts by using. For the gene expression programming (GEP) approach, a total of 156 samples were prepared in the laboratory using variable percentages of PET and SCM (0–10%, each). To achieve the best hyper parameter setting of the optimized GEP model, 10 trials were undertaken by varying the genetic parameters while observing the models’ performance in terms of statistical indices, i.e., correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), comparison of regression slopes, and predicted to experimental ratios (ρ). Sensitivity analysis and parametric study were performed on the best GEP model (obtained at; chromosomes = 50, head size = 9, and genes = 3) to evaluate the effect of contributing input parameters. The sensitivity analysis showed that: CS(7d) (30.47%) > CS(1d) (28.89%) > SCM (18.88%) > Flow (18.53%) > PET (3.23%). The finally selected GEP model exhibited optimal statistical indices (R = 0.977 and 0.975, RMSE = 2.423 and 2.531, MAE = 1.918 and 2.055) for training and validation datasets, respectively. The role of PET/SCM has no negative influence on the CS(28d) of cementitious grouts, which renders the PET a suitable alternative toward achieving sustainable and green concrete. Hence, the simple mathematical expression of GEP is efficacious, which leads to saving time and reducing labor costs of testing in civil engineering projects.
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spelling pubmed-91025822022-05-14 Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming Khan, Kaffayatullah Jalal, Fazal E. Iqbal, Mudassir Khan, Muhammad Imran Amin, Muhammad Nasir Al-Faiad, Majdi Adel Materials (Basel) Article The central aim of this study is to evaluate the effect of polyethylene terephthalate (PET) alongside two supplementary cementitious materials (SCMs)—i.e., fly ash (FA) and silica fume (SF)—on the 28-day compressive strength (CS(28d)) of cementitious grouts by using. For the gene expression programming (GEP) approach, a total of 156 samples were prepared in the laboratory using variable percentages of PET and SCM (0–10%, each). To achieve the best hyper parameter setting of the optimized GEP model, 10 trials were undertaken by varying the genetic parameters while observing the models’ performance in terms of statistical indices, i.e., correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), comparison of regression slopes, and predicted to experimental ratios (ρ). Sensitivity analysis and parametric study were performed on the best GEP model (obtained at; chromosomes = 50, head size = 9, and genes = 3) to evaluate the effect of contributing input parameters. The sensitivity analysis showed that: CS(7d) (30.47%) > CS(1d) (28.89%) > SCM (18.88%) > Flow (18.53%) > PET (3.23%). The finally selected GEP model exhibited optimal statistical indices (R = 0.977 and 0.975, RMSE = 2.423 and 2.531, MAE = 1.918 and 2.055) for training and validation datasets, respectively. The role of PET/SCM has no negative influence on the CS(28d) of cementitious grouts, which renders the PET a suitable alternative toward achieving sustainable and green concrete. Hence, the simple mathematical expression of GEP is efficacious, which leads to saving time and reducing labor costs of testing in civil engineering projects. MDPI 2022-04-23 /pmc/articles/PMC9102582/ /pubmed/35591409 http://dx.doi.org/10.3390/ma15093077 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khan, Kaffayatullah
Jalal, Fazal E.
Iqbal, Mudassir
Khan, Muhammad Imran
Amin, Muhammad Nasir
Al-Faiad, Majdi Adel
Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
title Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
title_full Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
title_fullStr Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
title_full_unstemmed Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
title_short Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
title_sort predictive modeling of compression strength of waste pet/scm blended cementitious grout using gene expression programming
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102582/
https://www.ncbi.nlm.nih.gov/pubmed/35591409
http://dx.doi.org/10.3390/ma15093077
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