Displacement determination of concrete reinforcement building using data-driven models

Abstract

Abstract Decision making on buildings after the earthquake have always been a great concern of scientists. Safety concerns, possibility of using the building, repairing the building, and the rate of damage are some of the most vital factors that needs to be paid attention in immediate decision makings of the buildings. In order to determine the level of damage in the buildings, the maximum displacement of stories is one of the most important parameter that needs to be investigated. In this paper, a concrete frame with shear wall containing 4-stories and 4-bays has been designed for acceleration records of 0.1 g to 1.5 g and the rate of damage is determined. The total of 450 data with 6 input variables and one output variable is produced. The input parameters are defined as frequency, Vs, Richter, the distance from the earthquake epicentre (DEE), PGA, and acceleration, and the output parameter is defined as drift. With respect to this data set, three different data-driven models, i.e. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression Model (MLR) are used to predict the displacements. Results indicate that Both the ANN and ANFIS model show great accuracies in estimating the displacements in concrete frame with shear wall. On the other hand, MLR model did not show acceptable accuracy in the same estimation purposes. Finally, the sensitivity analysis was performed on the data set and it was observed that the accuracy of the predictions highly depends on the number of input parameters. In other words, increasing the number of input parameters would result in the increase in the accuracy of the final prediction results.