Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio

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...

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Autores principales:	Nitin Dahiya, Babita Saini, H. Chalak
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	deep neural network modelling storey drift machine-learning computer programming Technology T
Acceso en línea:	https://doaj.org/article/094e422d64534310bdb359e84b8e86c6
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id	oai:doaj.org-article:094e422d64534310bdb359e84b8e86c6
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spelling	oai:doaj.org-article:094e422d64534310bdb359e84b8e86c62021-11-11T11:41:52ZDeep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio2588-287210.22115/scce.2021.289034.1329https://doaj.org/article/094e422d64534310bdb359e84b8e86c62021-07-01T00:00:00Zhttp://www.jsoftcivil.com/article_139405_95df3188de9b0fd1727ae74f13214977.pdfhttps://doaj.org/toc/2588-2872In 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.Nitin DahiyaBabita SainiH. ChalakPouyan Pressarticledeep neural networkmodellingstorey driftmachine-learningcomputer programmingTechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 3, Pp 88-100 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep neural network modelling storey drift machine-learning computer programming Technology T
spellingShingle	deep neural network modelling storey drift machine-learning computer programming Technology T Nitin Dahiya Babita Saini H. Chalak Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
description	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.
format	article
author	Nitin Dahiya Babita Saini H. Chalak
author_facet	Nitin Dahiya Babita Saini H. Chalak
author_sort	Nitin Dahiya
title	Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
title_short	Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
title_full	Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
title_fullStr	Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
title_full_unstemmed	Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio
title_sort	deep neural network-based storey drift modelling of precast concrete structures using rstudio
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/094e422d64534310bdb359e84b8e86c6
work_keys_str_mv	AT nitindahiya deepneuralnetworkbasedstoreydriftmodellingofprecastconcretestructuresusingrstudio AT babitasaini deepneuralnetworkbasedstoreydriftmodellingofprecastconcretestructuresusingrstudio AT hchalak deepneuralnetworkbasedstoreydriftmodellingofprecastconcretestructuresusingrstudio
_version_	1718439099936800768

Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio

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