基于季节与趋势模式的基坑顶部变形预测

Deformation Prediction at the Top of Foundation Pits Based on Seasonal and Trend Patterns

提出一种通过粒子群算法(PSO)优化变分模态分解(VMD)控制参数,并结合回声状态网络(ESN)和自注意力机制的基坑顶部变形预测方法。首先,利用粒子群算法优化VMD的控制参数,将基坑顶部变形序列数据分解成不同的本征模态函数(IMF),根据不同频率特征,将变形序列分解成季节项、趋势项与随机项。将自注意力机制与回声状态网络结合,对重构后的长时间序列数据进行建模,并对比筛选不同时长的输入数据,以确定最佳输入时长,从而提高预测精度。以广州某基坑的变形监测数据为例,对所提方法进行验证。实验结果表明,在输入步长为3的情况下,模型的MSE(均方误差)为0.62,R2为0.986,有效提升了基坑顶部变形预测的准确性与稳定性。

A method for predicting top-of-foundation-pit deformation was proposed by optimizing the control parameters of variational mode decomposition(VMD)using particle swarm optimization(PSO)and combining echo state networks(ESN)with a self-attention mechanism.Firstly,the PSO was used to optimize the control parameters of VMD,decomposing the deformation sequence data of the foundation pit top into different intrinsic mode functions(IMF).According to the different frequency characteristics,the deformation sequence was decomposed into seasonal,trend,and random components.The self-attention mechanism was combined with ESN to model the reconstructed long-term sequence data,and different input data lengths are compared and selected to determine the optimal input length,thereby improving prediction accuracy.The proposed method was validated using deformation monitoring data from a foundation pit in Guangzhou.Experimental results show that with an input step of 3,the method achieves an MSE(mean square error)of 0.62 and an R 2 of 0.986,significantly improving the accuracy and stability of top-of-foundation-pit deformation prediction.