Seismic Performance Assessment of Reinforced Concrete Building Stock Using Artificial Neural Network and Linear Regression Analysis

Abstract

In the last half century, Turkish earthquake codes for designing building under earthquake loads went through many modifications and editions (TEC1975, TEC1997, TEC2007 and TBEC2018). Hence, there are many buildings existing that has been built in accordance with old regulations since improvements in the recent earthquake code. Therefore, the need of a quick assessment method to identify the building seismic performance level in accordance with the latest seismic code is extremely vital. For this purpose, this research aimed to prepare a database for the quick estimation of building seismic performance by constructing an artificial neural network model and linear regression analysis. In order to meet these objectives, 540 reinforced concrete building models with various parameters such as building material properties, geometry, designed standard, site class, and peak ground acceleration were modeled with respect to TEC1975, TEC1997, TEC2007, TBEC2018 and seismic performance obtained from the pushover analysis in accordance with TBEC2018. Data obtained were used to perform multiple linear regression analysis (MVLRA). Also, data obtained from the pushover analysis were used to train and validate the constructed artificial neural network (ANN) model with several training algorithms performed with various number of hidden layers in order to figure out the optimum number of hidden layers and best train method which gives the highest accuracy of prediction for the performance assessment of the buildings as well. Results indicate that ANN can be a very profound technique in predicting the seismic performance levels. In addition, validity of the created model was checked by the application through the existing buildings as a case study with various parameters within the range of considerations according to the existing study. Furthermore, identification of the significance of the predictor variables according to their effect on seismic assessment have been done with several methods which are widely used in literature as well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.