Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression

In reinforced concrete structures, the fiber-reinforced polymer (FRP) as reinforcing rebars have been widely used. The use of GFRP (glass fiber-reinforced polymer) bars to solve the steel reinforcement corrosion problem in various concrete structures is now well documented in many research studies....

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Main Authors: Isleem, H.F., Tayeh, B.A., Alaloul, W.S., Musarat, M.A., Raza, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29625 /
Published: MDPI 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120041847&doi=10.3390%2fma14237172&partnerID=40&md5=5e5a4895df4cb6c9774aff09f0115cac
http://eprints.utp.edu.my/29625/
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spelling utp-eprints.296252022-03-29T05:20:41Z Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression Isleem, H.F. Tayeh, B.A. Alaloul, W.S. Musarat, M.A. Raza, A. In reinforced concrete structures, the fiber-reinforced polymer (FRP) as reinforcing rebars have been widely used. The use of GFRP (glass fiber-reinforced polymer) bars to solve the steel reinforcement corrosion problem in various concrete structures is now well documented in many research studies. Hollow concrete-core columns (HCCs) are used to make a lightweight structure and reduce its cost. However, the use of FRP bars in HCCs has not yet gained an adequate level of confidence due to the lack of laboratory tests and standard design guidelines. Therefore, the present paper numerically and empirically explores the axial compressive behavior of GFRP-reinforced hollow concrete-core columns (HCCs). A total of 60 HCCs were simulated in the current version of Finite Element Analysis (FEA) ABAQUS. The reference finite element model (FEM) was built for a wide range of test variables of HCCs based on 17 specimens experimentally tested by the same group of researchers. All columns of 250 mm outer diameter, 0, 40, 45, 65, 90, 120 mm circular inner-hole diameter, and a height of 1000 mm were built and simulated. The effects of other parameters cover unconfined concrete strength from 21.2 to 44 MPa, the internal confinement (center to center spiral spacing = 50, 100, and 150 mm), and the amount of longitudinal GFRP bars (�v = 1.78�4.02). The complex column response was defined by the concrete damaged plastic model (CDPM) and the behavior of the GFRP reinforcement was modeled as a linear-elastic behavior up to failure. The proposed FEM showed an excellent agreement with the tested load-strain responses. Based on the database obtained from the ABAQUS and the laboratory test, different empirical formulas and artificial neural network (ANN) models were further proposed for predicting the softening and hardening behavior of GFRP-RC HCCs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120041847&doi=10.3390%2fma14237172&partnerID=40&md5=5e5a4895df4cb6c9774aff09f0115cac Isleem, H.F. and Tayeh, B.A. and Alaloul, W.S. and Musarat, M.A. and Raza, A. (2021) Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression. Materials, 14 (23). http://eprints.utp.edu.my/29625/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In reinforced concrete structures, the fiber-reinforced polymer (FRP) as reinforcing rebars have been widely used. The use of GFRP (glass fiber-reinforced polymer) bars to solve the steel reinforcement corrosion problem in various concrete structures is now well documented in many research studies. Hollow concrete-core columns (HCCs) are used to make a lightweight structure and reduce its cost. However, the use of FRP bars in HCCs has not yet gained an adequate level of confidence due to the lack of laboratory tests and standard design guidelines. Therefore, the present paper numerically and empirically explores the axial compressive behavior of GFRP-reinforced hollow concrete-core columns (HCCs). A total of 60 HCCs were simulated in the current version of Finite Element Analysis (FEA) ABAQUS. The reference finite element model (FEM) was built for a wide range of test variables of HCCs based on 17 specimens experimentally tested by the same group of researchers. All columns of 250 mm outer diameter, 0, 40, 45, 65, 90, 120 mm circular inner-hole diameter, and a height of 1000 mm were built and simulated. The effects of other parameters cover unconfined concrete strength from 21.2 to 44 MPa, the internal confinement (center to center spiral spacing = 50, 100, and 150 mm), and the amount of longitudinal GFRP bars (�v = 1.78�4.02). The complex column response was defined by the concrete damaged plastic model (CDPM) and the behavior of the GFRP reinforcement was modeled as a linear-elastic behavior up to failure. The proposed FEM showed an excellent agreement with the tested load-strain responses. Based on the database obtained from the ABAQUS and the laboratory test, different empirical formulas and artificial neural network (ANN) models were further proposed for predicting the softening and hardening behavior of GFRP-RC HCCs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Isleem, H.F.
Tayeh, B.A.
Alaloul, W.S.
Musarat, M.A.
Raza, A.
spellingShingle Isleem, H.F.
Tayeh, B.A.
Alaloul, W.S.
Musarat, M.A.
Raza, A.
Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
author_sort Isleem, H.F.
title Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
title_short Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
title_full Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
title_fullStr Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
title_full_unstemmed Artificial neural network (Ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
title_sort artificial neural network (ann) and finite element (fem) models for gfrp-reinforced concrete columns under axial compression
publisher MDPI
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120041847&doi=10.3390%2fma14237172&partnerID=40&md5=5e5a4895df4cb6c9774aff09f0115cac
http://eprints.utp.edu.my/29625/
_version_ 1741197273695518720
score 11.62408

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