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Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result,...

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Autores principales: Amin, Muhammad Nasir, Iqbal, Mudassir, Salami, Babatunde Abiodun, Jamal, Arshad, Khan, Kaffayatullah, Abu-Arab, Abdullah Mohammad, Al-Ahmad, Qasem Mohammed Sultan, Imran, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182747/
https://www.ncbi.nlm.nih.gov/pubmed/35683818
http://dx.doi.org/10.3390/polym14112145
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author Amin, Muhammad Nasir
Iqbal, Mudassir
Salami, Babatunde Abiodun
Jamal, Arshad
Khan, Kaffayatullah
Abu-Arab, Abdullah Mohammad
Al-Ahmad, Qasem Mohammed Sultan
Imran, Muhammad
author_facet Amin, Muhammad Nasir
Iqbal, Mudassir
Salami, Babatunde Abiodun
Jamal, Arshad
Khan, Kaffayatullah
Abu-Arab, Abdullah Mohammad
Al-Ahmad, Qasem Mohammed Sultan
Imran, Muhammad
author_sort Amin, Muhammad Nasir
collection PubMed
description Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result, determining the bond strength (BS) qualities of FRP rebars is critical. In this work, BS data for FRP rebars was investigated, utilizing non-linear capabilities of gene expression programming (GEP) on 273 samples. The BS of FRP and concrete was considered a function of bar surface (Bs), bar diameter (d(b)), concrete compressive strength (f(c)′), concrete-cover-bar-diameter ratio (c/d), and embedment-length-bar-diameter ratio (l/d). The investigation of the variable number of genetic parameters such as number of chromosomes, head size, and number of genes was undertaken such that 11 different models (M1–M11) were created. The results of accuracy evaluation parameters, namely coefficient of determination (R(2)), mean absolute error (MAE), and root mean square error (RMSE) imply that the M11 model outperforms other created models for the training and testing stages, with values of (0.925, 0.751, 1.08) and (0.9285, 0.802, 1.11), respectively. The values of R(2) and error indices showed that there is very close agreement between the experimental and predicted results. 30 number chromosomes, 9 head size, and 5 genes yielded the optimum model. The parametric analysis revealed that d(b), c/d, and l/d significantly affected the BS. The FRP rebar diameter size is greater than 10 mm, whereas a l/d ratio of more than 12 showed a considerable decrease in BS. In contrast, the rise in c/d ratio revealed second-degree increasing trend of BS.
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spelling pubmed-91827472022-06-10 Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model Amin, Muhammad Nasir Iqbal, Mudassir Salami, Babatunde Abiodun Jamal, Arshad Khan, Kaffayatullah Abu-Arab, Abdullah Mohammad Al-Ahmad, Qasem Mohammed Sultan Imran, Muhammad Polymers (Basel) Article Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result, determining the bond strength (BS) qualities of FRP rebars is critical. In this work, BS data for FRP rebars was investigated, utilizing non-linear capabilities of gene expression programming (GEP) on 273 samples. The BS of FRP and concrete was considered a function of bar surface (Bs), bar diameter (d(b)), concrete compressive strength (f(c)′), concrete-cover-bar-diameter ratio (c/d), and embedment-length-bar-diameter ratio (l/d). The investigation of the variable number of genetic parameters such as number of chromosomes, head size, and number of genes was undertaken such that 11 different models (M1–M11) were created. The results of accuracy evaluation parameters, namely coefficient of determination (R(2)), mean absolute error (MAE), and root mean square error (RMSE) imply that the M11 model outperforms other created models for the training and testing stages, with values of (0.925, 0.751, 1.08) and (0.9285, 0.802, 1.11), respectively. The values of R(2) and error indices showed that there is very close agreement between the experimental and predicted results. 30 number chromosomes, 9 head size, and 5 genes yielded the optimum model. The parametric analysis revealed that d(b), c/d, and l/d significantly affected the BS. The FRP rebar diameter size is greater than 10 mm, whereas a l/d ratio of more than 12 showed a considerable decrease in BS. In contrast, the rise in c/d ratio revealed second-degree increasing trend of BS. MDPI 2022-05-25 /pmc/articles/PMC9182747/ /pubmed/35683818 http://dx.doi.org/10.3390/polym14112145 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
Amin, Muhammad Nasir
Iqbal, Mudassir
Salami, Babatunde Abiodun
Jamal, Arshad
Khan, Kaffayatullah
Abu-Arab, Abdullah Mohammad
Al-Ahmad, Qasem Mohammed Sultan
Imran, Muhammad
Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model
title Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model
title_full Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model
title_fullStr Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model
title_full_unstemmed Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model
title_short Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model
title_sort predicting bond strength between frp rebars and concrete by deploying gene expression programming model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182747/
https://www.ncbi.nlm.nih.gov/pubmed/35683818
http://dx.doi.org/10.3390/polym14112145
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