Long-Term Performance of Corrosion-Damaged Reinforced Concrete Beams

doi:10.14359/14660

Cited by 34 publications

(12 citation statements)

References 21 publications

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“…[4][5][6][7][8] Many studies have been conducted to investigate the effects of corrosion on the flexural performances of RC members. [4][5][6][7][8][9][10][11][12][13] In particular, Wang and Liu, 11,12 Maaddawy et al 10 and Han et al 4 proposed theoretical analysis models, which can quantitatively estimate the flexural responses of corroded RC members. In contrast, only a few studies have been conducted on the shear performance of RC members with corroded longitudinal reinforcement, and the experimental results on the shear behavior of corroded RC members are also very limited.…”

Section: Introductionmentioning

confidence: 99%

Experimental shear tests of reinforced concrete beams with corroded longitudinal reinforcement

Han

Lee

et al. 2020

Structural Concrete

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In this study, shear tests were conducted to investigate the effect of corrosion damage induced in longitudinal reinforcement on the shear resistance mechanisms of reinforced concrete (RC) members without transverse reinforcement. A total of eight RC beam specimens were carefully designed and fabricated, and accelerated corrosion and typical shear tests were conducted considering various anchorage conditions and corrosion rates that were introduced in the longitudinal tension reinforcement as the key test variables. Test results showed that when the tensile reinforcement was not sufficiently anchored at the end regions of the RC members, the shear strengths of the test specimens decreased as the corrosion rate increased. In contrast, when the longitudinal reinforcement was properly anchored using the hook detail, the shear strengths showed a clear trend of increase, even when the longitudinal reinforcement was severely corroded.

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Section: Introductionmentioning

confidence: 99%

Experimental shear tests of reinforced concrete beams with corroded longitudinal reinforcement

Han

Lee

et al. 2020

Structural Concrete

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“…Thus, this study aims to evaluate the efficiency of various ML models in predicting the residual flexural capacity of corroded RC beams. A comprehensive dataset of the flexural behavior of corroded RC beams was collected from previous studies [8,12,13,[23][24][25][26][27]. The dataset was then randomly split into 80% training data and 20% test data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool

2023

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Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams.

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“…The aging of concrete structures is nowadays still actual, and the theme of concrete durability is now increasing its importance also from the point of view of eco-sustainability, in terms of raw materials quarrying savings. One of the most important issues that is far from being solved is surely carbon steel reinforcements' corrosion [1][2][3][4][5][6]. Well compacted and low porosity concretes manufactured with ordinary Portland cement protect carbon steel reinforcement against general corrosion due to the formation of the passive film on the steel surface that occurs naturally in alkaline environments.…”

Section: Introductionmentioning

confidence: 99%

Inhibition Effect of Tartrate Ions on the Localized Corrosion of Steel in Pore Solution at Different Chloride Concentrations

et al. 2020

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The aim of this work is the evaluation of the inhibition effect of tartrate ions with respect to the localized corrosion of steel reinforcements in alkaline solution as a function of the concentration of chlorides ions. Weight loss tests and electrochemical tests were carried out in saturated Ca(OH)2 solution with NaOH at pH 12.7 and 13.2. The results only evidence a slight inhibition effect at pH 12.7, whereas at pH 13.2 the pitting onset is inhibited also for chloride concentration up to 3 M. Tartaric acid is a dicarboxylic acid with nucleophile substituents, which can act as a chelating agent both adsorbing on the surface of the passive film and forming a soluble complex with ferrous and ferric ions. Tartrate causes an increase in the passive current density but it prevents the depassivation of carbon steel due to the action of chlorides, thus preventing pitting initiation due to the competitive adsorption on metal surface.

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Long-Term Performance of Corrosion-Damaged Reinforced Concrete Beams

Cited by 34 publications

References 21 publications

Experimental shear tests of reinforced concrete beams with corroded longitudinal reinforcement

Experimental shear tests of reinforced concrete beams with corroded longitudinal reinforcement

Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool

Inhibition Effect of Tartrate Ions on the Localized Corrosion of Steel in Pore Solution at Different Chloride Concentrations

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