Prediction of maximum relative out-of- plane displacement of infill wall exposed to shake table by using artificial neural network models

This paper aims to present forecasting of relative out-of-plane displacement, failure mode and failure limit of reinforced concrete frame enclosure with infill wall by artificial neural network (ANN) models. For this purpose, two shake table experiment test results were evaluated. Model-I is unreinforced brick model and Model-II is composed of infill wall with bed joint reinforcement. After a certain acceleration level, accelerometers are detached from the cracked wall surface in order not to be damaged. These instrument detachments cause data missing. Missing relative maximum out-of-plane displacements were predicted by ANN models. Moreover, failure of infill wall through out-of-plane direction is also predicted at 0,79g acceleration level for both models (Model-I and Model-II). This study proves that ANN based procedure predict robust results to estimate missing experimental data