TY - JOUR ID - 139405 TI - Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Dahiya, Nitin AU - Saini, Babita AU - Chalak, H. D. AD - Ph.D. Student, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, India AD - Associate Professor, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, India AD - Assistant Professor, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, India Y1 - 2021 PY - 2021 VL - 5 IS - 3 SP - 88 EP - 100 KW - Deep Neural Network KW - modelling KW - Storey drift KW - Machine-learning KW - Computer Programming DO - 10.22115/scce.2021.289034.1329 N2 - 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 as training data and the remaining 91 were used as test data. 0.9598 correlation coefficient was observed for DNN Keras as compared to 0.8905 by resilient back-propagation neural networks (BPNN), indicating that DNN Keras has about 8 per cent improved efficiency in predicting storey drift. Wilcoxon signed-rank test (non-parametric test) was used to compare and validate the performance of DNN Keras and resilient BPNN algorithms. The positive results of this study point to the need for further research into the use of DNN Keras in structural and civil engineering. UR - https://www.jsoftcivil.com/article_139405.html L1 - https://www.jsoftcivil.com/article_139405_95df3188de9b0fd1727ae74f13214977.pdf ER -