Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks
The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1...
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| Language: | English |
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MDPI AG
2017-05-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/7/6/563 |
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| author | Omar Payán-Serrano Edén Bojórquez Juan Bojórquez Robespierre Chávez Alfredo Reyes-Salazar Manuel Barraza Arturo López-Barraza Héctor Rodríguez-Lozoya Edgar Corona |
| author_facet | Omar Payán-Serrano Edén Bojórquez Juan Bojórquez Robespierre Chávez Alfredo Reyes-Salazar Manuel Barraza Arturo López-Barraza Héctor Rodríguez-Lozoya Edgar Corona |
| author_sort | Omar Payán-Serrano |
| collection | DOAJ |
| description | The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%. |
| first_indexed | 2024-12-13T16:52:23Z |
| format | Article |
| id | doaj.art-e7adaa4e6ae64639a781fd9da134b33c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T16:52:23Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e7adaa4e6ae64639a781fd9da134b33c2022-12-21T23:38:01ZengMDPI AGApplied Sciences2076-34172017-05-017656310.3390/app7060563app7060563Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural NetworksOmar Payán-Serrano0Edén Bojórquez1Juan Bojórquez2Robespierre Chávez3Alfredo Reyes-Salazar4Manuel Barraza5Arturo López-Barraza6Héctor Rodríguez-Lozoya7Edgar Corona8Facultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa 80040, MexicoThe aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.http://www.mdpi.com/2076-3417/7/6/563dynamic analysiswind simulationartificial neural networksparallel computing |
| spellingShingle | Omar Payán-Serrano Edén Bojórquez Juan Bojórquez Robespierre Chávez Alfredo Reyes-Salazar Manuel Barraza Arturo López-Barraza Héctor Rodríguez-Lozoya Edgar Corona Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks Applied Sciences dynamic analysis wind simulation artificial neural networks parallel computing |
| title | Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks |
| title_full | Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks |
| title_fullStr | Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks |
| title_full_unstemmed | Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks |
| title_short | Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks |
| title_sort | prediction of maximum story drift of mdof structures under simulated wind loads using artificial neural networks |
| topic | dynamic analysis wind simulation artificial neural networks parallel computing |
| url | http://www.mdpi.com/2076-3417/7/6/563 |
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