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针对库水位和降雨对面板堆石坝渗流的滞后效应,结合深覆盖层面板堆石坝的筑坝特点,在综合考虑覆盖层厚度、筑坝材料等对大坝渗流影响的基础上,建立了考虑滞后效应的深覆盖层面板堆石坝渗流安全监控模型,并在模型的求解中采用了云自适应遗传算法。实例应用表明,该模型能较好地反映库水位、降雨对渗流的滞后影响,模型精度与预报效果优于一般渗流统计模型,且云自适应遗传算法较好地弥补了传统遗传算法执行效率不高、易陷入局部最优解等不足。
In view of the hysteresis effect of reservoir water level and rainfall on seepage flow of CFRD, combined with the damming characteristics of deep overburden rockfill dam, based on comprehensive consideration of the influence of covering thickness and dam material on seepage of dam, Considering the hysteresis effect of deep overburden CFRD seepage safety monitoring model, and in the model used in the cloud adaptive genetic algorithm. The example application shows that the model can better reflect the influence of reservoir water level and rainfall on the seepage lag. The model accuracy and forecasting effect are better than the general seepage statistical model. And the cloud adaptive genetic algorithm can make up the traditional GA implementation efficiency High, easy to fall into the local optimal solution and other deficiencies.