A data-driven approach for predicting interface bond strength between corroded reinforcement and concrete

Huang, Tao; Liu, Tingbin; Xu, Ning; Yue, Kangle; Li, Yunxia; Liu, Xing; Liu, Shiyang; Ou, Jiaxiang

doi:10.1016/j.istruc.2023.105122

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…With the application of machine learning algorithms, research on the FRP-to-concrete interface's performance is no longer limited to experimentation. 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].…”

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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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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“…The results demonstrated that the convolution-based integrated stacking model produces excellent fine-grained predictions with coefficient of determination, a20 index, and mean square error values of 0.84, 0.75, and 0.022, respectively. Huang et al 41 trained an ANN model by using a total of 166 samples to estimate the bond strength of corrosion reinforcements and concrete, which produced well.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to estimate the bond strength of the corroded steel bar‐concrete

Wang,

Li,

et al. 2023

Structural Concrete

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Steel bar corrosion is prone to serious degeneration of the bond strength (τBS) of reinforced concrete, which leads to structural failure. There are many factors affecting the τBS; thus, the assessment of the τBS of corroded steel bar‐concrete becomes very complicated. In this research, a τBS evolution database of 430 samples containing 14 important factors, such as concrete compressive strength, yield strength and corrosion rate of steel bar, was established. Six machine learning algorithms were employed for comparative study to achieve optimal prediction results. The results showed that random forest and extreme random trees models have the best performance with coefficient of determination (R2) of 0.83 and 0.82, mean square error (MSE) of 13.59 and 13.98, root mean square error of 3.68 and 3.74, mean absolute error values of 2.35 and 2.31, and a20 values of 0.62 and 0.70, respectively. In addition, both models demonstrated shorter training times compared to the others. The running times are 589.75 and 418.31 ms, respectively. The superior feasibility and reliability of the machine learning algorithm were validated by contrasting with the computation of the empirical formulas. The empirical formulas exhibited higher error values, with the majority of MSE indicators exceeding 50 and the lowest R2 value even presenting negative values. Furthermore, feature importance calculations were used to understand the ability of the machine learning models to perceive each influencing factor. Among them, the corrosion rate, the yield strength of the longitudinal bar, and the stirrup emerged as the three most important influential factors.

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Study the effect of ANN splitting ratios and training functions on the prediction of corroded steel-to-concrete bond strength

Bhushan,

Arora,

Kumar

et al. 2024

Multiscale and Multidiscip. Model. Exp. and Des.

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A data-driven approach for predicting interface bond strength between corroded reinforcement and concrete

Cited by 6 publications

References 38 publications

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

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

Machine learning to estimate the bond strength of the corroded steel bar‐concrete

Study the effect of ANN splitting ratios and training functions on the prediction of corroded steel-to-concrete bond strength

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