| 迟世春,朱叶.面板堆石坝瞬时变形和流变变形参数的联合反演[J].水利学报,2016,47(1):18-27 |
| 面板堆石坝瞬时变形和流变变形参数的联合反演 |
| Back-analysis of instantaneous and rheological deformation parameters for concrete faced rockfill dams |
| 投稿时间：2015-01-07 |
| DOI：10.13243/j.cnki.slxb.20150031 |
| 中文关键词: 堆石流变 参数反演 RBF 神经网络 多种群遗传算法 |
| 英文关键词: rockfill dams creep parameter inversion RBF neural network multiple population genetic algorithm |
| 基金项目:国家自然科学基金项目(51179024,51379029) |
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| 中文摘要: |
| 堆石坝变形包括瞬时弹塑性变形及时间相关的流变变形,从实际的监测变形中精确区分这两种变形有一定技术难度。本文将瞬时及流变变形参数的反演问题转化为一个组合优化问题,采用智能优化算法寻找最佳的堆石变形参数。研究中,首先拟定了多种变形参数样本,采用有限元法计算坝体变形;然后采用径向基神经网络训练上述样本,建立堆石变形参数与坝体变形之间的映射关系;最后根据坝体实际变形测量值,采用多种群遗传算法优化得到坝体瞬时及流变变形参数。采用径向基神经网络替代有限元可节省计算时间,提高计算效率;而多种群遗传优化算法可避免传统遗传算法早熟问题。用反演参数再次计算得到的水布垭坝体沉降与实测值吻合较好。 |
| 英文摘要: |
| Settlement of rockfill dams include instantaneous elastic-plastic deformation during construction and time-dependent rheological deformation after water impoundment, but it is difficult to distinguish these two kinds of deformation accurately from actual monitoring deformation. In this paper,a back analysis method was proposed to obtain the instantaneous and rheological deformation parameters successively by the combinatorial intelligent optimization algorithm. Firstly, dam deformation was calculated by the finite element method using some prepared parameter samples. Then a RBF neural network has been trained using these samples to establish a mapping relationship between the parameters and dam deformation. Thirdly, the instantaneous and rheological deformation parameters of the dam have been determined by multiple population genetic optimization algorithm according to the actual dam deformation measurements. The computing time of dam deformation has been saved greatly by RBF neural network instead of finite element method, and the precocious problem can be avoided by the multiple population genetic algorithm. The recalculated settlement values of Shuibuya concrete faced rockfill dam using the inversion parameters are well agreed with the actual measured values. |
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