Estimating the Bond Strength of FRP Bars Using a Hybrid Machine Learning Model

Li, Ran; Liu, Lulu; Cheng, Ming

doi:10.3390/buildings12101654

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…The results showed that 22% of the reviewed studies discussed the application of different ML techniques for FRP-concrete bond strength. Within this scope, many ML models have been created to compare the feasibility of various algorithms [88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103].…”

Section: Bond Strengthmentioning

confidence: 99%

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The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Alhusban,

Alkhawaldeh

2023

Sustainability

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Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.

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Section: Bond Strengthmentioning

confidence: 99%

“…On the other hand, the ELM was applied to study the bond behavior at the interface between concrete and FRP bars [101]. In this study, a new optimized model, namely a particle swarm optimization-based ELM model, was proposed.…”

Section: Bond Strengthmentioning

confidence: 99%