To solve the influence of uncertainty on the prediction accuracy of high-speed railway slope displacement,an interval prediction theory is introduced to quantify the uncertainty in displacement prediction,and a Bootstrap-GRU-BP(BGB)hybrid interval prediction model is established.BGB model firstly uses the Bootstrap-based Gated Recurrent Unit(GRU)algorithm to measure the predicted mean value of displacement and the variance of cognitive error,uses the BP algorithm to measure the variance of random error,and then combines the predicted mean of displacement,the variance of cognitive error and random error to quantify the prediction intervals under a certain confidence level.Finally,based on the monitoring data of the slopes along the Hangzhou-Shaoxing-Taizhou High-Speed Railway,the response characteristics of the cognitive uncertainty of the BGB model are explored,and the superiority of the BGB model is verified by comparing multiple interval prediction models.The results show that the BGB model not only constructs clear and reliable prediction intervals but also provides highly accurate point prediction results;changing model input features and prediction algorithm leads to the change of cognitive uncertainty,while the prediction intervals constructed by the BGB model can correctly respond to the changes in uncertainty.Compared to the interval prediction models centered on Extreme Learning Machine(ELM)and Support Vector Regression(SVR),the BGB model has better performance in both interval and point predictions.The research results can provide reliable prediction results for the development of high-speed railway slope displacement,and further provide the theoretical basis for the reliability analysis of high-speed railway slope.
维普
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