An efficient deep learning approach for ultimate bond strength estimations of corroded bar and concrete

Abstract The corrosion behavior of the reinforced concrete structure depends heavily on the interfacial bond behavior between steel and concrete. Over the years, the deterioration of integrated reinforced steel has weakened this bond and potentially led to structural problems. Conventional methods of bond strength evaluation, such as pullout and bond beam tests, is frequently intrusive and tedious. Therefore, there is a growing need for non-intrusive, effective, and reliable forecasting algorithms capable of assessing bond deterioration caused by corrosion. Traditional algorithms for predicting bond strength make it difficult to capture the complex nature of steel-concrete bonds. The present study proposes two different deep learning algorithms, i.e., convolutional neural networks (CNN) and long short-term memory (LSTM), for predicting maximum bond strength in the presence of corrosion. The predictive model is based on a comprehensive dataset comprising 218 datasets from previous studies encompassing diverse input and output variables for predicting the models. The models were trained and tested using the given data to improve early predictions of corrosion-induced bond degradations. The predictive model’s effectiveness was assessed by applying various performance metrics. From this study, the CNN model exhibits higher accuracy and efficiency with mean absolute error, root mean square error, and mean absolute percentage error of 0.25, 0.28, and 95.72, respectively, for predicting ultimate bond strength estimations. The findings of this study provide an accurate and robust prediction model to improve the reliability and safety of the concrete structure by enhancing the residual load-bearing capacity of the concrete structure that has undergone corrosion.