Durability assessment of FRP-to-concrete bonded connections under moisture condition using data-driven machine learning-based approaches

Baghaei, Keyvan Aghabalaei; Hadigheh, S. Ali

doi:10.1016/j.compstruct.2021.114576

Cited by 15 publications

(4 citation statements)

References 26 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%

“…It was concluded that GMDH outperformed existing empirical equations in terms of accuracy and safety. In addition, ANN was used along with MLR and ANFIS to determine the shear bond strength of FRP-concrete connections under water immersion conditions [94]. The ANN approach was superior to the other models in computing the interfacial bond strength of FRP-strengthened joints under water immersion conditions.…”

Section: Bond Strengthmentioning

confidence: 99%

“…Based on a dataset of 855 single-lap shear test results, it was proven that the CatBoost algorithm was able to effectively predict the bond strength between FRP and concrete and was superior to the other ensemble models. The durability and failure modes of FRP and concrete connections under moisture conditions were measured using ANN, SVM, DT, and ensemble models [94]. The developed models were trained and tested using 429 durability test results collected from the literature.…”

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%

Section: Bond Strengthmentioning

confidence: 99%

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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“…Employing data-driven methodologies to address these complex prediction challenges is a pragmatic approach [14,15]. Researchers have advocated for the utilization of artificial neural network (ANN) models to forecast the strength of interfacial bonds, yielding superior predictive outcomes [16][17][18]. Additionally, a study has proposed an equation for interfacial bond strength that utilizes an ANN model based on artificial bee colony optimization [19].…”

Section: Introductionmentioning

confidence: 99%

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

Wang,

Ye,

Liu

et al. 2024

Applied Sciences

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Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

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A Machine Learning Approach to Modelling the Bond Strength of Adhesively Bonded Joints Under Water Immersion Condition

Baghaei

Hadigheh

2021

Lecture Notes in Civil Engineering

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Durability assessment of FRP-to-concrete bonded connections under moisture condition using data-driven machine learning-based approaches

Cited by 15 publications

References 26 publications

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

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

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

A Machine Learning Approach to Modelling the Bond Strength of Adhesively Bonded Joints Under Water Immersion Condition

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