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<p>Accurately simulating the post-peak behavior of brittle rocks remains a challenge for statistical damage constitutive models. This study investigates the sensitivity of post-peak simulation to model components by evaluating combinations of three strength criteria and three micro-element strength distributions against triaxial test data from five rock types. Results indicate that the probability distribution of micro-element strength governs simulation accuracy, while the strength criterion exerts minimal influence. Based on these findings, a relative brittleness index-based method for selecting the optimal distribution is proposed, along with a dual-parameter collaborative correction method. Validation shows that this approach significantly enhances the fit to experimental post-peak curves, eliminates criterion-induced interference in parameter adjustment, and remains effective across multiple distribution modes. The proposed method offers a solution for improving the reliability of damage models in predicting brittle rocks failure.</p>

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Post-peak dependence analysis in statistical damage constitutive modeling of brittle rocks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月26日

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  • Yixiao Shen,
  • Peidong Zhu,
  • Chao Zhang,
  • Hao Zhou,
  • Dongping Zhu,
  • Yongyi Li

摘要

Accurately simulating the post-peak behavior of brittle rocks remains a challenge for statistical damage constitutive models. This study investigates the sensitivity of post-peak simulation to model components by evaluating combinations of three strength criteria and three micro-element strength distributions against triaxial test data from five rock types. Results indicate that the probability distribution of micro-element strength governs simulation accuracy, while the strength criterion exerts minimal influence. Based on these findings, a relative brittleness index-based method for selecting the optimal distribution is proposed, along with a dual-parameter collaborative correction method. Validation shows that this approach significantly enhances the fit to experimental post-peak curves, eliminates criterion-induced interference in parameter adjustment, and remains effective across multiple distribution modes. The proposed method offers a solution for improving the reliability of damage models in predicting brittle rocks failure.