关键词: 地铁, 沉降预测, 小波去噪, 相空间重构, 动态特征, GSM-SVR模型

Abstract: To predict the settlement of surrounding surfaces during metro construction with high accuracy and reliability, the authors propose a combined model based on wavelet threshold denoising (WTD), phase space reconstruction (PSR), and a grid search method-optimized support vector machine regression model (GSM-SVR). The model is built using engineering data of a city′s Metro Line 1. First, the WTD method (db4 function) is applied to preprocess the raw monitoring data, effectively eliminating noise and singular values. This step raises the signal-to-noise ratio to 11.814 4 and reduces the root mean square error (E_RMSE) to 0.373 83. Second, PSR (embedding dimension=5 and delay time=1) is used to convert the static time series into multidimensional dynamic feature data, thereby enhancing the nonlinear representation capability of the data. Finally, the hyperparameters of the SVR model (c=50 and g=0.38) are optimized using the GSM to construct a GSM-SVR prediction model. Subsequently, six comparative models, including the traditional SVR, particle swarm optimization-SVR, and gray wolf optimizer-SVR models, are used for performance evaluation. Results indicate the following: (1) WTD-PSR effectively removes noise and singular values while enhancing the multidimensional dynamic characteristics of the data. (2) Compared with the other models, the GSM-SVR model reduces prediction errors (E_MAPE, E_{MAE, and} E_RMSE) by 8.44%－64.35% and improves the goodness of fit (R²) by 0.51%－22.08% while achieving an average error of only 3.68%. (3) Denoising remarkably enhances model performance, reducing prediction errors by 12.33%－19.70%. Thus, the proposed model effectively predicts the settlement during metro construction.

Key words: metro, settlement prediction, wavelet threshold denoising, phase space reconstruction, dynamic characteristics, grid search method-optimized support vector machine regression model