Application of combined prediction model based on FrFT in settlement monitoring of subway foundation pit
In view of the difficult parameter selection of the extreme learning machine (ELM) model in the field of subway foundation pit settlement prediction,this paper introduced the particle swarm optimization (PSO) algorithm to optimize the key parameters of the ELM model. Combined with fractional Fourier transform (FrFT),the paper proposed a combined prediction model of subway foundation pit settlement based on FrFT and PSO-ELM models. Firstly,FrFT was used to decompose the settlement data of the subway foundation pit on multiple scales,and several subsequences with simple structures were obtained. Secondly,the PSO algorithm was introduced to optimize the ELM model globally and improve the prediction performance of ELM. For each sub-sequence,the proposed PSO-ELM model was used for modeling and prediction,respectively. Finally,the results of superimposing each subsequence were considered predicted results. The settlement monitoring data of a subway foundation pit was used for the prediction experiment,and the experimental results of the GM(1,1) model,ELM network model,long-short term memory (LSTM) model,and the combined prediction model in this paper were compared. The results show that the combined prediction model proposed in this paper has significantly improved the prediction accuracy compared with other models,verifying that the model in this paper can effectively mine the regularity and trend information in the data,solve the problem of difficult parameter selection,and provide a reference for the monitoring and prediction of relevant structural deformation.
fractional Fourier transform (FrFT)particle swarm optimization (PSO)extreme learning machine (ELM)combined modelprediction of foundation pit settlement
万方数据
维普
