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Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration
A vehicle-bridge tuned mass damper (TMD) coupled dynamic analysis and vibration-control model was established to optimize TMD damping effects on a steel-box girder bridge bearing vehicle loads. It was also used to investigate optimization efficiency of different algorithms in TMD design parameters....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478324/ https://www.ncbi.nlm.nih.gov/pubmed/31013304 http://dx.doi.org/10.1371/journal.pone.0215773 |
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author | Liu, Jianwei Li, Dejian Yu, Peng |
author_facet | Liu, Jianwei Li, Dejian Yu, Peng |
author_sort | Liu, Jianwei |
collection | PubMed |
description | A vehicle-bridge tuned mass damper (TMD) coupled dynamic analysis and vibration-control model was established to optimize TMD damping effects on a steel-box girder bridge bearing vehicle loads. It was also used to investigate optimization efficiency of different algorithms in TMD design parameters. This model simulated bridges and vehicles with the use of a 7 degrees of freedom curved-beam element model and a 7 degrees of freedom vehicle model, respectively. The TMD system was simulated with the use of multiple rigid-body systems linked with springs and dampers. Road surface condition, as a vibration source, was simulated with the use of a frequency equivalent method based on a power spectrum. A variably-accelerated pattern search algorithm was proposed in line with the initial TMD parameters calculated by Den Hartog formula. Visual software was compiled by Fortran and used for an optimization study of vibration reduction. A three-span, curved, continuous steel-box girder bridge was used as the numerical example. Optimized effects and computational efficiency of vibration reduction under different methods were compared. The comparison included a single variable optimization based on Den Hartog formula, an ergodic search method, an integer programming method, a traditional genetic algorithm, a traditional pattern search algorithm, and a variably-accelerated pattern search algorithm. The results indicate that variably-accelerated pattern search algorithm is more efficient at improving TMD optimal parameter design. Final TMD parameter optimization values obtained by different methods are quite close to each other and tends verify the reliability of the optimization results. |
format | Online Article Text |
id | pubmed-6478324 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64783242019-05-07 Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration Liu, Jianwei Li, Dejian Yu, Peng PLoS One Research Article A vehicle-bridge tuned mass damper (TMD) coupled dynamic analysis and vibration-control model was established to optimize TMD damping effects on a steel-box girder bridge bearing vehicle loads. It was also used to investigate optimization efficiency of different algorithms in TMD design parameters. This model simulated bridges and vehicles with the use of a 7 degrees of freedom curved-beam element model and a 7 degrees of freedom vehicle model, respectively. The TMD system was simulated with the use of multiple rigid-body systems linked with springs and dampers. Road surface condition, as a vibration source, was simulated with the use of a frequency equivalent method based on a power spectrum. A variably-accelerated pattern search algorithm was proposed in line with the initial TMD parameters calculated by Den Hartog formula. Visual software was compiled by Fortran and used for an optimization study of vibration reduction. A three-span, curved, continuous steel-box girder bridge was used as the numerical example. Optimized effects and computational efficiency of vibration reduction under different methods were compared. The comparison included a single variable optimization based on Den Hartog formula, an ergodic search method, an integer programming method, a traditional genetic algorithm, a traditional pattern search algorithm, and a variably-accelerated pattern search algorithm. The results indicate that variably-accelerated pattern search algorithm is more efficient at improving TMD optimal parameter design. Final TMD parameter optimization values obtained by different methods are quite close to each other and tends verify the reliability of the optimization results. Public Library of Science 2019-04-23 /pmc/articles/PMC6478324/ /pubmed/31013304 http://dx.doi.org/10.1371/journal.pone.0215773 Text en © 2019 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liu, Jianwei Li, Dejian Yu, Peng Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
title | Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
title_full | Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
title_fullStr | Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
title_full_unstemmed | Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
title_short | Study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
title_sort | study on optimization algorithm of tuned mass damper parameters to reduce vehicle-bridge coupled vibration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478324/ https://www.ncbi.nlm.nih.gov/pubmed/31013304 http://dx.doi.org/10.1371/journal.pone.0215773 |
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