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Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization
This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced for...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651162/ https://www.ncbi.nlm.nih.gov/pubmed/31269761 http://dx.doi.org/10.3390/ma12132133 |
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author | Ficarella, Elisa Lamberti, Luciano Degertekin, Sadik Ozgur |
author_facet | Ficarella, Elisa Lamberti, Luciano Degertekin, Sadik Ozgur |
author_sort | Ficarella, Elisa |
collection | PubMed |
description | This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines. |
format | Online Article Text |
id | pubmed-6651162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66511622019-08-07 Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization Ficarella, Elisa Lamberti, Luciano Degertekin, Sadik Ozgur Materials (Basel) Article This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines. MDPI 2019-07-02 /pmc/articles/PMC6651162/ /pubmed/31269761 http://dx.doi.org/10.3390/ma12132133 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ficarella, Elisa Lamberti, Luciano Degertekin, Sadik Ozgur Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization |
title | Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization |
title_full | Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization |
title_fullStr | Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization |
title_full_unstemmed | Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization |
title_short | Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization |
title_sort | mechanical identification of materials and structures with optical methods and metaheuristic optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651162/ https://www.ncbi.nlm.nih.gov/pubmed/31269761 http://dx.doi.org/10.3390/ma12132133 |
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