Settlement prediction of high-speed railway subgrade based on MIDBO-BP-Adaboost

HE Quanpeng; SI Yongbo; LI Shaoyuan

doi:10.11860/j.issn.1673-0291.20240065

Abstract：To address the issue of high-speed railway subgrade settlement influenced by factors such as temperature and humidity,this study proposes a combined prediction model integrating an improved Dung Beetle Optimization(My Improved Dung Beetle Optimization,MIDBO)algorithm,Back Propagation(BP)neural network,and Adaptive Boosting(Adaboost).First,to overcome the limita-tions of the conventional Dung Beetle Optimization(DBO)algorithm,specifically its tendency to con-verge to local optima and poor performance in complex engineering applications,a novel MIDBO algo-rithm is developed.This algorithm integrates composite chaotic mapping,simulated annealing,and a nonlinear exponential dynamic weighting strategy.Then,the MIDBO algorithm is used to optimize the BP neural network,which is then combined with the Adaboost algorithm to establish the MIDBO-BP-Adaboost model.Finally,the proposed model and comparative models are applied to predict sub-grade settlement on the Lanzhou-Urumqi High-Speed Railway.Experimental results demonstrate that the MIDBO algorithm effectively optimizes the BP neural network,significantly improving prediction accuracy of the model.The Adaboost algorithm further enhances the model's robustness and general-ization capability.Compared with the BP model,the MIDBO-BP-Adaboost model reduces the mean absolute error,root mean square error,and mean absolute percentage error by 63.81％,63.84％,and 62.26％,respectively,while increasing the coeffi-cient of determination by 18.82％.These findings offer a valuable reference for predicting subgrade settlement in Lanzhou-Urumqi High-speed Railway.