基于遗传和声算法的考虑实际压实质量的土石坝沉降预测模型

鉴于通常采用统计方法预测土石坝沉降时往往假定同一分区的坝料为均质同密度的，但却忽略了实际压实质量差异导致的坝料密度空间分布的不均性问题，利用数字大坝系统实时采集的各坝料分区的压实密度和填筑进度，建立了考虑各坝料分区实际压实质量的土石坝施工期沉降预测回归模型，并利用遗传和声算法对模型的回归系数进行寻优重估，以提高模型的精度。实例应用结果表明，基于遗传和声算法的考虑实际压实质量的土石坝沉降预测模型的计算结果与实际情况更为接近，为土石坝施工期沉降预测提供了一种新途径。

Genetic Harmony Algorithm-Based Earth-Rock Dam Settlement Forecasting Model with Consideration of Actual Compaction Quality

Dam materials are generally considered to be homogeneous in the same zone for forecasting earth-rock dam settlement using statistical models in the most previous studies, which may neglect the spatial difference of material density under actual compaction quality. With timely acquiring the compacted density and construction appearance of each dam material zone based on the digital dam system, a regression model for earth-rock dam settlement with consideration of actual compaction quality was established. To improve the accuracy of the model, a genetic harmony algorithm was applied to optimize the regression coefficients of the model. A case study shows that the result of the proposed method is in better agreement with the actual measurements than that of the previous method without considering the actual compaction quality. Therefore, it provides a potential way for forecasting earth-rock dam settlement in construction period.