Multi‑parameter Calibration Method and Application of Constitutive Model Based on Improved Dung Beetle Optimization Algorithm

LU Pin; YU Yang; LIU Songlin; WANG Tianlong; XU Zihan

doi:10.13409/j.cnki.jdpme.20250224001

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Multi‑parameter Calibration Method and Application of Constitutive Model Based on Improved Dung Beetle Optimization Algorithm

更新时间：2025-12-12

- Multi‑parameter Calibration Method and Application of Constitutive Model Based on Improved Dung Beetle Optimization Algorithm
- Journal of Disaster Prevention and Mitigation Engineering Vol. 45, Issue 5, Pages: 1014-1023(2025)
- 作者机构：
  
  1.浙江大学海洋学院，浙江舟山 316000
  2.浙江大学海南研究院，海南三亚 572025
  3.南洋理工大学土木与环境工程学院，新加坡 618798
- 作者简介：
- 基金信息：
- DOI：10.13409/j.cnki.jdpme.20250224001
  CLC： TU43
- Received：24 February 2025，
  
  Revised：2025-04-07，
  
  Published：28 October 2025
- 稿件说明：
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卢玭,于洋,刘松林等.基于改进蜣螂优化算法的本构模型多参数标定方法及应用[J].防灾减灾工程学报,2025,45(05):1014-1023.

LU Pin,YU Yang,LIU Songlin,et al.Multi‑parameter Calibration Method and Application of Constitutive Model Based on Improved Dung Beetle Optimization Algorithm[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(05):1014-1023.
卢玭,于洋,刘松林等.基于改进蜣螂优化算法的本构模型多参数标定方法及应用[J].防灾减灾工程学报,2025,45(05):1014-1023. DOI： 10.13409/j.cnki.jdpme.20250224001.

LU Pin,YU Yang,LIU Songlin,et al.Multi‑parameter Calibration Method and Application of Constitutive Model Based on Improved Dung Beetle Optimization Algorithm[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(05):1014-1023. DOI： 10.13409/j.cnki.jdpme.20250224001.

摘要

多参数本构模型的标定是其准确刻画岩土体复杂应力应变关系的关键和难点。针对复杂本构模型参数多、标定难的问题，本研究通过融合动态范围调整机制、T分布变异策略、Levy飞行全局搜索和多策略种群更新机制，改进蜣螂优化算法。通过优化典型标准函数，并与蜣螂优化算法（DBO）、灰狼优化算法（GWO）、麻雀搜索算法（SSA）、鲸鱼优化算法（WOA）和北鹰优化算法（NGO）的优化结果进行对比，验证了改进蜣螂优化算法在全局优化能力和参数标定效率方面均表现出显著的提升。基于三维多重机构边界面模型，构建算法‑本构模型双向数据交互机制程序，结合砂土三轴试验数据开展参数标定，对比改进蜣螂算法和传统蜣螂算法参数标定结果可知：改进蜣螂优化算法最优适应度值显著降低，平均无效运行率由改进前的8.75%降至4.17%，有效克服了早熟收敛问题。参数标定后获得的应力-应变曲线，在峰值偏应力、残余应力等关键点及整体趋势上与试验数据具有良好的一致性，为复杂本构模型多参数标定提供了方法支撑。

Abstract

The calibration of multi-parameter constitutive models is both the key and the challenge for accurately characterizing the complex stress-strain relationships of geotechnical materials. To address the issues of numerous parameters and difficult calibration in complex constitutive models

this study improved the dung beetle optimization algorithm by integrating dynamic range adjustment mechanisms

T-distribution variation strategies

Levy flight global search

and multi-strategy population update mechanisms. By optimizing typical benchmark functions and comparing the optimization results with those of the dung beetle optimization (DBO)

gray wolf optimization (GWO)

sparrow search algorithm (SSA)

whale optimization algorithm (WOA)

and northern goshawk optimization (NGO)

the improved dung beetle optimization algorithm was verified to achieve significant improvements in both global optimization capability and parameter calibration efficiency. Based on a three-dimensional multi-mechanism bounding surface model

a bidirectional data interaction mechanism program between the optimization algorithm and the constitutive model was established. Parameter calibration was performed using triaxial test data of sandy soil. The comparison of the parameter calibration results between the improved dung beetle algorithm and the traditional dung beetle algorithm demonstrated that the optimal fitness value of the improved algorithm was significantly reduced

and the average invalid operation rate decreased from 8.75% to 4.17%

effectively overcoming premature convergence. The stress-strain curve obtained after parameter calibration showed good agreement with the experimental data in key aspects such as peak deviatoric stress

residual stress

and overall trends

providing methodological support for the multi-parameter calibration of complex constitutive models.

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references

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