袁克阔. 粒子群算法改进及内变量本构模型参数反演[J]. 煤田地质与勘探, 2017, 45(2): 112-117. DOI: 10.3969/j.issn.1001-1986.2017.02.020
引用本文: 袁克阔. 粒子群算法改进及内变量本构模型参数反演[J]. 煤田地质与勘探, 2017, 45(2): 112-117. DOI: 10.3969/j.issn.1001-1986.2017.02.020
YUAN Kekuo. Improved particle swarm optimization and parameter inversion in internal variable constitutive model[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(2): 112-117. DOI: 10.3969/j.issn.1001-1986.2017.02.020
Citation: YUAN Kekuo. Improved particle swarm optimization and parameter inversion in internal variable constitutive model[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(2): 112-117. DOI: 10.3969/j.issn.1001-1986.2017.02.020

粒子群算法改进及内变量本构模型参数反演

Improved particle swarm optimization and parameter inversion in internal variable constitutive model

  • 摘要: 为了研究深埋煤矿巷道通常存在长时间、大变形问题,拓展岩土工程反分析的手段,改善岩土工程反分析的效率和精度,首先基于自然选择、自适应变惯性权重、异步变化学习因子的策略改进了粒子群算法并完成了程序实现,通过Sphere和Rastrigrin两函数测试了改进算法的优越性;其次以Matlab软件为平台,联合大型有限元软件ABAQUS,编制了岩土反分析程序GeoPSOInverse.m;最后应用所编程序反演了以不可恢复应变为变量的、不显含时间的泥岩蠕变模型参数。结果证实:改进的粒子群算法在岩土工程参数反演计算中体现出了可靠的反演能力和很快的收敛速度,可进行复杂采矿工程的实践应用。

     

    Abstract: In order to extend back analysis means and improve the efficiency and accuracy, an improved particle swarm optimization(PSO) with variable inertia weight and synchronous changing learning factor was carried out and the program was completed, the advantage of the enhanced method was tested by Sphere and Rastrigrin functions. Then, a back analysis program GeoPSOInverse.m was developed by the software MATLAB, in which the finite element code of ABAQUS was embedded. At last, the parameters of creep constitutive model of a mudstone with irreversible strain as variable and implicit time in formula were back analysied. The results show that this improved PSO method is a very good inverse analysis method and its efficiency is quite good, and can be proceeded in practical application for complex engineering.

     

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