Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map

Nikoo, Mehdi; Sadowski, Łukasz; Nikoo, Mohammad Reza

doi:10.3390/coatings7100160

Cited by 18 publications

(7 citation statements)

References 49 publications

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“…One of the few devices available on the market for the simultaneous measurement of three electrical quantities stationary potential of reinforcement (E st ), concrete cover resistivity (Θ), and the corrosion current density (i cor ) is the GP-5000 GalvaPulse TM set (Figure 2a) [30][31][32][33]. The advantage of using the GalvaPulse TM device (compared to devices used in half-cell potential measurements) is the relatively short measurement time, which is usually no more than a few seconds at one point.…”

Section: Criteria For Assessing the Degree Of Reinforcement Corrosionmentioning

confidence: 99%

Manufacturing Errors of Concrete Cover as a Reason of Reinforcement Corrosion in a Precast Element—Case Study

et al. 2019

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The article concerns the assessment of technical condition of the precast loggia wall in a large panel building after 25 years of use as well as the cause of its damage. As a result of the study, cracks and losses of the concrete cover were found. Corrosion products were visible on exposed reinforcing rods. The reinforcement distribution and concrete cover thickness in loggia wall were estimated using a rebar detector. The corrosion assessment of reinforcement was performed using a semi non-destructive galvanostatic pulse method that allows the location of areas of corrosion and estimate the reinforcement corrosion activity. The phase composition of the concrete cover was analyzed. The test results showed an insufficient thickness of the concrete cover as the main cause of loggia wall damage. The research indicated that manufacturing errors made in the prefabrication plants affect the technical condition of precast elements and may lead to the damage of the structure well before the expected of its service life. In the case of manufacturing errors causing the implementation of an element with a concrete cover that does not meet the standard requirements for thickness and tightness, it is recommended to use protective coatings to increase the element’s durability to the designed level.

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Section: Criteria For Assessing the Degree Of Reinforcement Corrosionmentioning

confidence: 99%

Manufacturing Errors of Concrete Cover as a Reason of Reinforcement Corrosion in a Precast Element—Case Study

et al. 2019

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“…The corrosion process of steel bars embedded in porous concrete is presented in Figure 1 . Surface chloride concentrations affect the corrosion of steel and the performance of entire buildings and have a devastating effect in civil engineering [ 6 , 7 , 8 ]. The durability of a structure is an important parameter in RC buildings for maintaining an adequate service life of concrete structures [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

et al. 2021

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Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

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“…It is especially important in the case of unusual load, such as high temperature caused by fire [ 34 ]. In recent years, neural networks have been used for diagnostics of structural elements of buildings [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostics of Concrete and Steel in Elements of an Historic Reinforced Concrete Structure

et al. 2021

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Existing buildings, especially historical buildings, require periodic or situational diagnostic tests. If a building is in use, advanced non-destructive or semi-destructive methods should be used. In the diagnosis of reinforced concrete structures, tests allowing to assess the condition of the reinforcement and concrete cover are particularly important. The article presents non-destructive and semi-destructive research methods that are used for such tests, as well as the results of tests performed for selected elements of a historic water tower structure. The assessment of the corrosion risk of the reinforcement was carried out with the use of a semi-destructive galvanostatic pulse method. The protective properties of the concrete cover were checked by the carbonation test and the phase analysis of the concrete. X-ray diffractometry and thermal analysis methods were used for this. In order to determine the position of the reinforcement and to estimate the concrete cover thickness distribution, a ferromagnetic detection system was used. The comprehensive application of several test methods allowed mutual verification of the results and the drawing of reliable conclusions. The results indicated a very poor state of the reinforcement, loss in the depth of cover and sulphate corrosion.

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Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map

Cited by 18 publications

References 49 publications

Manufacturing Errors of Concrete Cover as a Reason of Reinforcement Corrosion in a Precast Element—Case Study

Manufacturing Errors of Concrete Cover as a Reason of Reinforcement Corrosion in a Precast Element—Case Study

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

Diagnostics of Concrete and Steel in Elements of an Historic Reinforced Concrete Structure

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