A genetic algorithm optimization framework for the characterization of hyper-viscoelastic materials: application to human articular cartilage.
| Autor: | Allen P; Physical Sciences for Health CDT, Department of Chemistry, University of Birmingham, Birmingham, UK., Cox SC; School of Chemical Engineering, University of Birmingham, Birmingham, UK., Jones S; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK., Espino DM; Department of Mechanical Engineering, University of Birmingham, Birmingham, UK. |
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| Jazyk: | angličtina |
| Zdroj: | Royal Society open science [R Soc Open Sci] 2024 Jun 26; Vol. 11 (6), pp. 240383. Date of Electronic Publication: 2024 Jun 26 (Print Publication: 2024). |
| DOI: | 10.1098/rsos.240383 |
| Abstrakt: | This study aims to develop an automated framework for the characterization of materials which are both hyper-elastic and viscoelastic. This has been evaluated using human articular cartilage (AC). AC (26 tissue samples from 5 femoral heads) underwent dynamic mechanical analysis with a frequency sweep from 1 to 90 Hz. The conversion from a frequency- to time-domain hyper-viscoelastic material model was approximated using a modular framework design where finite element analysis was automated, and a genetic algorithm and interior point technique were employed to solve and optimize the material approximations. Three orders of approximation for the Prony series were evaluated at N = 1, 3 and 5 for 20 and 50 iterations of a genetic cycle. This was repeated for 30 simulations of six combinations of the above all with randomly generated initialization points. There was a difference between N = 1 and N = 3/5 of approximately ~5% in terms of the error estimated. During unloading the opposite was seen with a 10% error difference between N = 5 and 1. A reduction of ~1% parameter error was found when the number of generations increased from 20 to 50. In conclusion, the framework has proved effective in characterizing human AC. Competing Interests: We declare we have no competing interests. (© 2024 The Authors.) |
| Databáze: | MEDLINE |
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