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Computers and Concrete
  Volume 29, Number 3, March 2022 , pages 145-159
DOI: https://doi.org/10.12989/cac.2022.29.3.145
 


Predicting bond strength of corroded reinforcement by deep learning
Harun Tanyildizi

 
Abstract
    In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.
 
Key Words
    anova analysis; bond strength; concrete; corroded reinforcement; deep learning; extreme learning machine
 
Address
Harun Tanyildizi: Department of Civil Engineering, Faculty of Technology, Firat University, Elazig, Turkey
 

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