Predictive Models for Bond Strength of Reinforced Concrete with the Application of ANN

Abstract

Abstract The bond strength of grip b/w steel and concrete can be defined as the resistant to separating concrete or mortar from the reinforced bar. This bond strength is the most critical characteristic of reinforced-cement concrete. Structural performance depends upon this characteristic, especially in the failure phase. Bond strength is primarily dependable on many variables that affect this attribute. These variables include the diameter of the reinforced steel bar, bond extent, length to diameter ratio, cube compressive strength, concrete cover, cover to dimeter ratio, volume fraction and most importantly, different temperatures. Up to 150°C, there is no such change in bond strength of reinforcement concrete, but when the temperature rises beyond 150°C, it starts to decreasegradually. We have collected experimental data from the internationally published record. This study will see the change in bond strength at these temperature variations i.e., 200°C, 400°C, and 600°C. This observational study will represent a soft computing tool, i.e., an Artificial Neural network (ANN), to predict and measure the grip strength between concrete and steel bar at elevated temperatures. The bond strength of reinforced concrete has been predicted by using ANN Models. Data set based upon the different factor that affects the bond strength has been used as input for generating ANN model & ultimate bond strength of reinforced concrete has been used as output during the development of the ANN model. This model was then prepared to predict bond strength and affected by many input features and recorded a linear regression analysis. The predicted result then confirmed the accuracy and high estimation capability of the model.