高心墙堆石坝瞬变-流变参数解耦反分析方法及变形预测

高心墙堆石坝材料分区较多,且静力模型和流变模型参数均不相同。为了避免二者同时反演时巨大的计算量,并进一步提高反演参数的准确性,更好地分析堆石坝应力变形、进行变形预测,考虑堆石体瞬变和流变参数的解耦关系,对瞬变和流变参数进行解耦反演分析。以瀑布沟心墙堆石坝为例,在参数敏感性分析的基础上,利用堆石坝施工期、第一次满蓄水期、第二次满蓄水期的变形监测资料,以敏感性较强的参数为待反演参数,采用基于基因片段差异度的遗传算法和径向基函数神经网络(RBF)构建反演平台,对瞬变-流变模型参数进行了解耦反演分析。反演结果表明,计算值与实测值在数值和变化规律上总体符合较好,反演结果较为合理可靠。

Decoupling Inversion of Instantaneous and Rheological Parameters andDeformation Prediction of High Core-wall Rockfill Dam

High core-wall rockfill dam is featured with many partitions of different materials with varied instantaneous and rheological parameters. The inversion of instantaneous and rheological parameters simultaneously is accompanied with ponderous calculation. To improve the accuracy of material parameters in calculation model and deformation prediction, a decoupling inversion of instantaneous and rheological parameters was conducted in consideration of the decoupling relationship between instantaneous and rheological parameters. With Pubugou high core-wall rockfill dam as an example, the sensitivity of parameters to the deformation of rockfill dam was examined,and highly sensitive parameters were selected for inversion. Furthermore, the decoupling inversion of instantaneous and rheological parameters was conducted by adopting modified genetic algorithm and radial basis function neural network (RBF) according to the monitoring data of deformation during dam construction, first impoundment, and second impoundment. The inversion result proved that the calculated settlements agreed well in general with the measured data in terms of values and change rules.