Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks

Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensive RNN is trained to tackle these problems andprovide a simple tool for assessment. Aim of the study is to predict the non-linear drift demand along the heightof a structure by employing RNN for a given stiffness profile along the height, strength reduction coefficient, massdensity on a floor, number of storeys. In order to train the network, a large number of nonlinear time historyanalyses are conducted for synthetically created building models. It is shown that RNN is able to accurately predictnonlinear drift demand profile of a structure along height without conducting tedious time history analyses.Therefore, the trained RNN can serve as a drift demand estimation tool, significantly shortening the assessmentprocedure.


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Citation Formats
K. Kocamaz, B. Binici, and K. Tuncay, “Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks,” 2021, vol. 1, Accessed: 00, 2021. [Online]. Available: