基于BO-GRU的混凝土坝变形预测模型

针对混凝土坝变形具有较强的非线性特点、目前大坝变形预测模型出现参数过多及易陷入局部最优等问题,提出了一种深度学习中的门控制循环单元(GRU)模型,并结合贝叶斯优化算法(BO)对门控制循环单元的超参数进行优化,建立BO-GRU模型应用于混凝土坝变形预测。为检验模型的可行性,以实测变形监测数据为基础,并与极限学习机、相关向量机和基于遗传算法优化的支持向量机等模型预测结果进行对比。结果表明:该模型的泛化能力强、运行效率高,能有效运用于混凝土坝的变形预测。

Deformation prediction model of concrete dams based on BO-GRU model

Because the deformation of concrete dams has strong nonlinear characteristics, too many parameters are involved when using current prediction models, and yet these models are prone to local optimum. Here, a gated recurrent unit (GRU) model in deep learning was combined with Bayesian optimization (BO) to optimize the hyperparameters of the gated recurrent units, based on which the BO-GRU model was established to predict the deformation of concrete dams. In order to test the feasibility of the model, its prediction result was then compared with that of the extreme learning machine, the correlation vector machine and the support vector machine optimized by the genetic algorithm, based on the measured deformation monitoring data. The comparison result shows that the BO-GRU model has strong generalization ability and high operating efficiency, and it is suitable for the deformation prediction of concrete dams.