面板堆石坝瞬时变形和流变变形参数的联合反演

发布时间：2018-01-22 19:20

本文关键词： 堆石流变参数反演 RBF神经网络多种群遗传算法　出处：《水利学报》2016年01期 　论文类型：期刊论文

【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。
[Abstract]:The deformation of rockfill dam includes instantaneous elastic-plastic deformation and time-dependent rheological deformation. It is difficult to distinguish the two kinds of deformation accurately from the actual deformation monitoring. In this paper, the inversion problem of instantaneous and rheological deformation parameters is transformed into a combinatorial optimization problem. The intelligent optimization algorithm is used to find the best deformation parameters of rockfill. In the study, a variety of deformation parameter samples are drawn up, and the finite element method is used to calculate the deformation of the dam body. Then the radial basis function neural network is used to train the above samples and the mapping relationship between the rockfill deformation parameters and the dam deformation is established. Finally, according to the actual deformation measurement value of the dam body, the instantaneous and rheological deformation parameters of the dam body are optimized by multi-population genetic algorithm. The radial basis function neural network can be used to replace the finite element method to save the calculation time and improve the calculation efficiency. The multi-population genetic optimization algorithm can avoid the precocious problem of traditional genetic algorithm. The calculated settlement of Shuibuya dam by inversion parameters is in good agreement with the measured value.
【作者单位】：大连理工大学水利工程学院;
【基金】：国家自然科学基金项目(51179024;51379029)
【分类号】：TV641.43
【正文快照】： 1研究背景面板堆石坝的流变变形恶化了面板防渗体的受力性状,可导致面板发生脱空、挤压破碎以及拉伸裂缝等,严重影响面板坝的后期运行效益[1-2],但由于堆石流变变形的模型参数较难确定,使得流变变形的分析却难如人意。试验室内流变试验的规律与现场不同,室内流变试验变形稳定

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