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Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
2021
Journal of Soft Computing in Civil Engineering
In this research, storey drift has been determined using Deep neural networks (DNN Keras). DNN Keras has various hyper tuning parameters (hidden layer, drop out layer, epochs, batch size and activation function) that make it capable to model complex problems. Building height, number of bays, number of storeys, time period, storey displacement, and storey acceleration were the input parameters while storey drift was the output parameter. The dataset consists of 288 models, out of 197 were used
doi:10.22115/scce.2021.289034.1329
doaj:094e422d64534310bdb359e84b8e86c6
fatcat:mx65tgaudfcg3ba6dbukhf6rea