关键词: 高速铁路隧道, 变形预测, PSO-SVM模型, GA-BP网络模型, ARMA模型

Abstract:

Composite model of multiple evaluation system is put forward so as to overcome the shortcomings of traditional prediction models, such as single structure, poor prediction accuracy and poor stability. Firstly, a prediction system of railway tunnel construction deformation is established based on support vector machine (SVM), back propagation (BP) neural network, autoregressive moving average (ARMA) model. Then, the root mean square error, error sum of squares and mean absolute error are taken as evaluation indicators to establish error evaluation system of each predicting result, to solve weight contribution indicator of single prediction model and obtain optimal combination weights. And then, the prediction accuracy validation system is established based on after test, residual test and correlation test to check and evaluate the validity of the prediction model. Finally, the abovementioned model is applied to a superlarge crosssection railway tunnel. The results show that the relative error of the composite model of multiple evaluation system is less than 2%; the prediction accuracy is higher than that of single prediction model, and can meet the requirements of relevant tests.

Key words: highspeed railway tunnel, deformation prediction, PSOSVM model, GABP network model, ARMA model