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Creep modeling of composite materials based on improved gene expression programming

In this article, a new method for creep modeling and performance prediction of composite materials is presented. Since Findley power-law model is usually suitable for studying one-dimensional time-dependent creep of materials under low stress, an intelligent computing method is utilized to derive th...

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Autores principales: Tan, Hua, Yan, Shilin, Zhu, Sirong, Wen, Pin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789067/
https://www.ncbi.nlm.nih.gov/pubmed/36564449
http://dx.doi.org/10.1038/s41598-022-26548-6
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author Tan, Hua
Yan, Shilin
Zhu, Sirong
Wen, Pin
author_facet Tan, Hua
Yan, Shilin
Zhu, Sirong
Wen, Pin
author_sort Tan, Hua
collection PubMed
description In this article, a new method for creep modeling and performance prediction of composite materials is presented. Since Findley power-law model is usually suitable for studying one-dimensional time-dependent creep of materials under low stress, an intelligent computing method is utilized to derive three temperature-related sub-functions, the creep model as a function of time and temperature is established. In order to accelerate convergence rate and improve solution accuracy, an improved gene expression programming (IGEP) algorithm is proposed by adopting the probability-based population initialization and semi-elite roulette selection strategy. Based on short-term creep data at seven temperatures, a bivariate creep model with certain physical significance is developed. At fixed temperature, the univariate creep model is acquired. R(2), RMSE, MAE, RRSE statistical metrics are used to verify the validity of the developed model by comparison with viscoelastic models. Shift factor is solved by Arrhenius equation. The creep master curve is derived from time–temperature superposition model, and evaluated by Burgers, Findley and HKK models. R-square of IGEP model is above 0.98 that is better than classical models. Moreover, the model is utilized to predict creep values at t = 1000 h. Compared with experimental values, the relative errors are within 5.2%. The results show that the improved algorithm can establish effective models that accurately predict the long-term creep performance of composites.
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spelling pubmed-97890672022-12-25 Creep modeling of composite materials based on improved gene expression programming Tan, Hua Yan, Shilin Zhu, Sirong Wen, Pin Sci Rep Article In this article, a new method for creep modeling and performance prediction of composite materials is presented. Since Findley power-law model is usually suitable for studying one-dimensional time-dependent creep of materials under low stress, an intelligent computing method is utilized to derive three temperature-related sub-functions, the creep model as a function of time and temperature is established. In order to accelerate convergence rate and improve solution accuracy, an improved gene expression programming (IGEP) algorithm is proposed by adopting the probability-based population initialization and semi-elite roulette selection strategy. Based on short-term creep data at seven temperatures, a bivariate creep model with certain physical significance is developed. At fixed temperature, the univariate creep model is acquired. R(2), RMSE, MAE, RRSE statistical metrics are used to verify the validity of the developed model by comparison with viscoelastic models. Shift factor is solved by Arrhenius equation. The creep master curve is derived from time–temperature superposition model, and evaluated by Burgers, Findley and HKK models. R-square of IGEP model is above 0.98 that is better than classical models. Moreover, the model is utilized to predict creep values at t = 1000 h. Compared with experimental values, the relative errors are within 5.2%. The results show that the improved algorithm can establish effective models that accurately predict the long-term creep performance of composites. Nature Publishing Group UK 2022-12-23 /pmc/articles/PMC9789067/ /pubmed/36564449 http://dx.doi.org/10.1038/s41598-022-26548-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tan, Hua
Yan, Shilin
Zhu, Sirong
Wen, Pin
Creep modeling of composite materials based on improved gene expression programming
title Creep modeling of composite materials based on improved gene expression programming
title_full Creep modeling of composite materials based on improved gene expression programming
title_fullStr Creep modeling of composite materials based on improved gene expression programming
title_full_unstemmed Creep modeling of composite materials based on improved gene expression programming
title_short Creep modeling of composite materials based on improved gene expression programming
title_sort creep modeling of composite materials based on improved gene expression programming
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789067/
https://www.ncbi.nlm.nih.gov/pubmed/36564449
http://dx.doi.org/10.1038/s41598-022-26548-6
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