Analysis of effects of mechanical parameters on stability of cavern group using parallel evolutionary neural network FEM

AN Hong-gang; FENG Xia-ting; LI Shao-jun

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Analysis of effects of mechanical parameters on stability of cavern group using parallel evolutionary neural network FEM

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- Analysis of effects of mechanical parameters on stability of cavern group using parallel evolutionary neural network FEM
- Vol. 25, Issue 4期　刊出日, (2004)
- 作者机构：
  
  1. 同济大学地下建筑与工程系,上海,200092
  2. 中国科学院武汉岩土力学研究所 IRSM重点实验室,湖北,武汉,430071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU457
- Online First：17 July 2014，
  
  Published：2004
- 稿件说明：
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AN Hong-gang, FENG Xia-ting, LI Shao-jun. Analysis of effects of mechanical parameters on stability of cavern group using parallel evolutionary neural network FEM[J]. 2004, 25(4期　刊出日).
DOI：

AN Hong-gang, FENG Xia-ting, LI Shao-jun. Analysis of effects of mechanical parameters on stability of cavern group using parallel evolutionary neural network FEM[J]. 2004, 25(4期　刊出日). DOI：

摘要

将洞室群视为一特定系统，采用并行进化神经网络有限元（ENN-FEM）方法建立该系统与岩体力学参数之间的相对应关系。采用遗传算法分析得到对围岩稳定性最不利的参数组合，对组合中任一参数可通过参数敏感度函数分析其敏感度，并由此综合评估各软岩力学参数对洞室群稳定性的影响，确定出关键岩层以给设计和施工提供指导性建议。实例计算表明该方法是合理的，且具有智能化和综合分析的优点。

Abstract

Treating the cavern group as a special system

the corresponding relationship between the system and the mechanical parameters of the rock mass is obtained by using the parallel evolutionary neural network FEM. The worst combination of parameters related to the stability of surrounding rock mass is searched out by genetic algorithms (GA). Through sensibility analysis

the synthetical evaluation of the effects of mechanical parameters of soft rock mass on the stability of cavern group is given. Moreover

the key rock mass could be determined to provide the design and construction with further instructive suggestions. The application shows that the method is reasonable and it has the advantages of intelligent and synthetical analysis.

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Related Institution

State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences

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