作为建筑质量的关键保证和基石,深基坑变形预测的重要性不容忽视。然而,深基坑变形数据具有非线性和不稳定性的特点,这将增加变形预测的难度。针对这一特点和传统变形预测方法难以挖掘不同时间跨度数据之间的相关性,考虑了变分模式分解(VMD)在处理非平稳序列和门控周期单元(GRU)在处理复杂时间序列数据中的优势。一种结合粒子群优化(PSO)的预测模型,变分模式分解,提出了一种门控循环单元。首先,利用PSO算法优化的VMD对原始数据进行分解,得到Internet消息格式(IMF)。其次,采用粒子群优化的GRU模型对各IMF进行预测。最后,将各组分的预测值等权重求和,得到最终预测值。实例研究结果表明,PSO-GRU预测模型对原始序列的平均绝对误差,EMD分解,VMD分解数据为0.502mm,0.462mm,和0.127毫米,分别。与LSTM的预测均方误差相比,GRU,和PSO-LSTM预测模型,PSO-GRU对VMD分解的PTB0数据下降了62.76%,75.99%,和53.14%,分别。PTB04数据下降了70%,85.17%,和69.36%,分别。此外,与PSO-LSTM模型相比,按模型时间计算,下降了8.57%。当预测步长从三个阶段增加到五个阶段时,四个预测模型对原始数据的平均误差,EMD分解的数据,VMD分解数据增加了28.17%,3.44%,和14.24%,分别。VMD分解的数据更有利于模型预测,能有效提高模型预测精度。预测步长的增加将降低变形预测的准确性。所构建的PSO-VMD-GRU模型具有精度可靠、应用范围广等优点,能有效指导基坑工程的施工。
As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.