eprintid: 16787
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/67/87
datestamp: 2023-12-19 03:23:18
lastmod: 2023-12-19 03:23:18
status_changed: 2023-12-19 03:06:54
type: article
metadata_visibility: show
creators_name: Khan, K.
creators_name: Jalal, F.E.
creators_name: Iqbal, M.
creators_name: Khan, M.I.
creators_name: Amin, M.N.
creators_name: Al-Faiad, M.A.
title: Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
ispublished: pub
keywords: Cost engineering; Economic analysis; Fly ash; Gene expression; Mean square error; Mortar; Plastic bottles; Sensitivity analysis; Silica fume; Wages, Cementitious; Compression strength; Gene-expression programming; Mean absolute error; Predictive models; Programming models; Root mean squared errors; Statistical indices; Supplementary cementitious material; Waste polyethylene terephthalates, Compressive strength
note: cited By 11
abstract: 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 (CS28d ) 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 (�). 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: CS7d (30.47) > CS1d (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 CS28d 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129098772&doi=10.3390%2fma15093077&partnerID=40&md5=3cce8eddd5a457ece76a4e9baae1a05e
id_number: 10.3390/ma15093077
full_text_status: none
publication: Materials
volume: 15
number: 9
refereed: TRUE
issn: 19961944
citation:   Khan, K. and Jalal, F.E. and Iqbal, M. and Khan, M.I. and Amin, M.N. and Al-Faiad, M.A.  (2022) Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming.  Materials, 15 (9).   ISSN 19961944