Predicting Displacement Data of Three-Dimensional Reinforced Concrete Frames with Different Strengthening Applications Using ANN

In this study, the artificial neural network (ANN) method was used to estimate unavailable displacement data of three-dimensional (3D) reinforced concrete (RC) frames with different strengthening applications. Four 3D-RC frames were produced two storeys and one bay in 1/6 geometric scale with the deficiencies commonly observed in residential buildings in Turkey. The first specimen was a bare frame containing no brick walls and no strengthening. The second specimen was all brick walls and no strengthening. The third specimen was strengthened with an internal steel panel. The fourth specimen was strengthened with an infilled RC shear wall. The specimens were tested under reverse cyclic lateral loading and constant vertical loading until failure. This study investigated the estimation of displacement data when the linear variable differential transformer of 104 numbers is corrupted and some hysteretic loop data are missing. Using the method proposed the unavailable or incorrect displacement data can be predicted by ANN without performing any additional experiments. Root mean squared error, coefficient determination, mean absolute error, mean squared error and normalised mean absolute error statistical values were used to compare experimental results with ANN model results. These statistical values usually exhibit very low error rate until a cycle of maximum load is reached.