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Creep modeling of composite materials based on improved gene expression programming.

Hua Tan1,2, Shilin Yan1,2, Sirong Zhu3,4

  • 1Department of Engineering Structure and Mechanics, School of Science, Wuhan University of Technology, Wuhan, 430070, China.

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A new intelligent computing method accurately predicts composite material creep performance over time and temperature. This improved gene expression programming (IGEP) model surpasses classical methods, offering reliable long-term predictions with minimal error.

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Area of Science:

  • Materials Science
  • Computational Mechanics
  • Polymer Engineering

Background:

  • Traditional creep models like the Findley power-law are limited to one-dimensional, low-stress conditions.
  • Predicting long-term creep behavior in composite materials requires advanced modeling techniques.

Purpose of the Study:

  • To develop a novel, intelligent computing method for accurate creep modeling and performance prediction of composite materials.
  • To establish a bivariate creep model incorporating time and temperature dependencies.

Main Methods:

  • Utilized an intelligent computing approach to derive temperature-dependent sub-functions for a creep model.
  • Developed an improved gene expression programming (IGEP) algorithm with enhanced initialization and selection strategies.
  • Validated the model using statistical metrics (R², RMSE, MAE, RRSE) against experimental data and classical models.

Main Results:

  • The developed bivariate creep model demonstrated high accuracy, with an R-square value above 0.98.
  • The IGEP model successfully predicted long-term creep values (1000 h) with relative errors within 5.2%.
  • Time-temperature superposition using the Arrhenius equation and a creep master curve were effectively implemented.

Conclusions:

  • The proposed IGEP-based method provides a robust and accurate approach for predicting the long-term creep performance of composite materials.
  • This intelligent computing method offers significant advantages over traditional models in terms of accuracy and predictive capability.