Predicting the Ultimate and Relative Bond Strength of Corroded Bars and Surrounding Concrete by Considering the Effect of Transverse Rebar Using Machine Learning

Mousavi, Seyed Mohammad; Peyma, Abdolhamid Bahr; Mousavi, Seyed Roohollah; Moodi, Yaser

doi:10.1007/s40996-022-00909-7

Cited by 10 publications

(9 citation statements)

References 95 publications

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“…The R-value of the proposed ANN model was 1.31% higher than the model of Mousavi et al [ 7 ]. The RMSE of the ANN model was 72.17% lower than Mousavi et al [ 7 ] model; this means that the developed ANN model has more accuracy than the existing ML models.…”

Section: Resultsmentioning

confidence: 54%

“…Mousavi et al [ 7 ] used MLP, RBFNN, and SVR algorithms to estimate the steel-to-concrete BS. The SVR model outperformed the other ML models.…”

Section: Resultsmentioning

confidence: 99%

“…The results illustrated that the correlation coefficient (R) value of the ANN model was 0.927. Mousavi et al [ 7 ] estimated BS with three ML models, namely MLP, RBFNN, and SVR, with 482 experimental datasets, while the BS range was 0.19 to 91.42 MPa. The considered input parameters were eight, and the analyzed results displayed that the precision of the SVR (R = 0.977) model was higher compared with other ML models.…”

Section: Application Of ML In Concrete Technologymentioning

confidence: 99%

“…Vapnik and Chervonenkis created the SVM algorithm in 1963, and it was originally presented by Boser, Guyon, and Vapnik at the Computational Learning Theory conference in 1992 [ 62 ]. SVM is a type of ML algorithm that comes under the category of supervised learning and is used to analyze data for classification, prediction, and regression analysis [ 7 ]. However, mostly, it is used to analyze data for classification problems in ML.…”

Section: MLmentioning

confidence: 99%

“…It is crucial to evaluate the loss of steel-to-concrete BS for the calculation of the remaining residual capacity of RC elements impacted by corrosion of reinforcement bar. Once the structure’s remaining bond capacity is determined, the residual service life might be found in the corrosion-affected RC structures [ 6 , 7 ]. The general formula to find BS is given below:

where P F is the pullout force (kN), d is the diameter of the reinforcement bar (mm), and L b is the bond length (mm).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

et al. 2022

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The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.

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Section: Resultsmentioning

confidence: 54%

“…Mousavi et al [ 7 ] used MLP, RBFNN, and SVR algorithms to estimate the steel-to-concrete BS. The SVR model outperformed the other ML models.…”

Section: Resultsmentioning

confidence: 99%

Section: Application Of ML In Concrete Technologymentioning

confidence: 99%

Section: MLmentioning

confidence: 99%

where P F is the pullout force (kN), d is the diameter of the reinforcement bar (mm), and L b is the bond length (mm).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

et al. 2022

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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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Predicting the Bond Strength Between Concrete and Glass Fiber-Reinforced Polymer Bars Using Soft Computing Models

Shahri

Mousavi

2023

Iran J Sci Technol Trans Civ Eng

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Predicting the Ultimate and Relative Bond Strength of Corroded Bars and Surrounding Concrete by Considering the Effect of Transverse Rebar Using Machine Learning

Cited by 10 publications

References 95 publications

Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

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

Predicting the Bond Strength Between Concrete and Glass Fiber-Reinforced Polymer Bars Using Soft Computing Models

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