Modeling of GFRP-Concrete Bond-Slip Behavior: Integrating Neural Networks with Finite Element Analysis

Rajeev Devaraj; Ayodele Olofinjana; Christophe Gerber

doi:10.3390/constrmater6010012

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Modeling of GFRP-Concrete Bond-Slip Behavior: Integrating Neural Networks with Finite Element Analysis

Journal article

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Modeling of GFRP-Concrete Bond-Slip Behavior: Integrating Neural Networks with Finite Element Analysis

Rajeev Devaraj, Ayodele Olofinjana and Christophe Gerber

Construction Materials, Vol.6(1), pp.1-20

2026

DOI: https://doi.org/10.3390/constrmater6010012

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Abstract

GFRP-steel RC beams

artifical neural networks (ANN)

finite element analysis (FEA)

GFRP-concrete bond behaviour

Glass fibre-reinforced polymer (GFRP) offers a durable, high-tensile strength alternative to steel rebar in reinforced concrete (RC). However, the inherent lack of ductility in GFRP limits its structural applications, which has led to the development of hybrid GFRP-steel RC systems. The composite nature of these systems requires an accurate understanding of the bond interaction between GFRP rebar and concrete. Existing bond models often fall short of accurately representing the distinct mechanical properties and surface characteristics of GFRP bars, particularly within finite element (FE) analysis environments. To address this gap, the present study proposes a computational method that employs a feedforward neural network (FFNN) trained on experimental data encompassing a specific range of parameters (bar diameters 8-16 mm, concrete strengths 18-50 MPa), including bar diameter, bond length, concrete strength, and cover thickness. Unlike conventional models that typically focus on peak bond strength, the developed FFNN accurately predicts the complete bond-slip relationship. The developed bond model is then integrated into the FE analysis. The simulation results demonstrate strong agreement with experimental data (average R2 = 0.93) and effectively capture key behavioral aspects such as crack initiation and propagation.

Details

Title: Modeling of GFRP-Concrete Bond-Slip Behavior: Integrating Neural Networks with Finite Element Analysis
Authors: Rajeev Devaraj (Corresponding Author) - University of the Sunshine Coast
Ayodele Olofinjana - University of the Sunshine Coast
Christophe Gerber - University of the Sunshine Coast
Publication details: Construction Materials, Vol.6(1), pp.1-20
Publisher: MDPI AG
Date published: 2026
DOI: 10.3390/constrmater6010012
ISSN: 2673-7108
Copyright note: © 2026 by the authors. 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.
Data Availability: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991214551502621
Output Type: Journal article

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