Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN

Precast Seismic Structural Systems (PRESSS) provided an iterative procedure for obtaining optimum design of unbonded post-tensioned coupled precast concrete wall systems. Although PRESSS procedure is effective, however, it is lengthy and laborious. The purpose of this research is to employ Artificia...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Abdalla, Jamal (author)
مؤلفون آخرون: Elias, Saqan (author), Hawileh, Rami (author)
التنسيق: article
منشور في: 2014
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/8548
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author Abdalla, Jamal
author2 Elias, Saqan
Hawileh, Rami
author2_role author
author
author_facet Abdalla, Jamal
Elias, Saqan
Hawileh, Rami
author_role author
dc.creator.none.fl_str_mv Abdalla, Jamal
Elias, Saqan
Hawileh, Rami
dc.date.none.fl_str_mv 2014-04
2016-10-19T07:17:21Z
2016-10-19T07:17:21Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv Abdalla, Jamal A., Elias Saqan, and Rami Hawileh. "Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN." Computers and Concrete, An Int'l Journal 13, no. 4 (2014): 547-567
1943-541X
http://hdl.handle.net/11073/8548
10.12989/cac.2014.13.4.547
dc.language.none.fl_str_mv en_US
dc.relation.none.fl_str_mv Computers and Concrete
https://dx.doi.org/10.12989/cac.2014.13.4.547
dc.subject.none.fl_str_mv seismic design
precast concrete wall
unbonded post-tensioned
neural network
PRESSS
dc.title.none.fl_str_mv Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description Precast Seismic Structural Systems (PRESSS) provided an iterative procedure for obtaining optimum design of unbonded post-tensioned coupled precast concrete wall systems. Although PRESSS procedure is effective, however, it is lengthy and laborious. The purpose of this research is to employ Artificial Neural Network (ANN) to predict the optimum design parameters for such wall systems while avoiding the demanding iterative process. The developed ANN model is very accurate in predicting the non-dimensional optimum design parameters related to post-tensioning reinforcement area, yield force of shear connectors and ratio of moment resisted by shear connectors to the design moment. The Mean Absolute Percent Error (MAPE) for the test data for these design parameters is around %1 and the correlation coefficient is almost equal to 1.0. The developed ANN model is then used to study the effect of different design parameters on wall behavior. It is observed that the design moment and the concrete strength have the most influence on the wall behavior as compared to other parameters. Several design examples were presented to demonstrate the accuracy and effectiveness of the ANN model.
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identifier_str_mv Abdalla, Jamal A., Elias Saqan, and Rami Hawileh. "Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN." Computers and Concrete, An Int'l Journal 13, no. 4 (2014): 547-567
1943-541X
10.12989/cac.2014.13.4.547
language_invalid_str_mv en_US
network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/8548
publishDate 2014
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling Optimum seismic design of unbonded post-tensioned precast concrete walls using ANNAbdalla, JamalElias, SaqanHawileh, Ramiseismic designprecast concrete wallunbonded post-tensionedneural networkPRESSSPrecast Seismic Structural Systems (PRESSS) provided an iterative procedure for obtaining optimum design of unbonded post-tensioned coupled precast concrete wall systems. Although PRESSS procedure is effective, however, it is lengthy and laborious. The purpose of this research is to employ Artificial Neural Network (ANN) to predict the optimum design parameters for such wall systems while avoiding the demanding iterative process. The developed ANN model is very accurate in predicting the non-dimensional optimum design parameters related to post-tensioning reinforcement area, yield force of shear connectors and ratio of moment resisted by shear connectors to the design moment. The Mean Absolute Percent Error (MAPE) for the test data for these design parameters is around %1 and the correlation coefficient is almost equal to 1.0. The developed ANN model is then used to study the effect of different design parameters on wall behavior. It is observed that the design moment and the concrete strength have the most influence on the wall behavior as compared to other parameters. Several design examples were presented to demonstrate the accuracy and effectiveness of the ANN model.2016-10-19T07:17:21Z2016-10-19T07:17:21Z2014-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAbdalla, Jamal A., Elias Saqan, and Rami Hawileh. "Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN." Computers and Concrete, An Int'l Journal 13, no. 4 (2014): 547-5671943-541Xhttp://hdl.handle.net/11073/854810.12989/cac.2014.13.4.547en_USComputers and Concretehttps://dx.doi.org/10.12989/cac.2014.13.4.547oai:repository.aus.edu:11073/85482024-08-22T12:16:14Z
spellingShingle Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
Abdalla, Jamal
seismic design
precast concrete wall
unbonded post-tensioned
neural network
PRESSS
status_str publishedVersion
title Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
title_full Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
title_fullStr Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
title_full_unstemmed Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
title_short Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
title_sort Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
topic seismic design
precast concrete wall
unbonded post-tensioned
neural network
PRESSS
url http://hdl.handle.net/11073/8548