Research on Deformation Resistance Prediction of Oil Shale Fly Ash Improved Soil Roadbed Based on IPSO-Bi-LSTM
Oil shale fly ash improved soil(OFMS)roadbed filler has good frost resistance.In order to explore the change rule of strength and deformation resistance during service in seasonal freezing area,this study is based on the backfill project of OFMS filler in the municipal road of Changchun Metro Line 6.In combination with the results of OFMS dynamic rebound modulus tests,and taking into account the coupling effects of different conditions and factors,a dynamic rebound modulus prediction model based on IPSO-Bi-LSTM(improved particle swarm optimization-bidirectional long-short term memory neural network)was established,and compared with PSO-Bi-LSTM and PSO-SVR for prediction.The results show that:OFMS dynamic resilience modulus meets the specification requirements of soil roadbed.The average relative errors of the three model test sets are5.477%,6.097%and 10.209%,respectively,showing good performance in the prediction of OFMS dynamic resilience modulus.Among them,IPSO-Bi-LSTM test set MAE is 5.057,RMSE is 6.008,and R2 is 0.984,which is relatively excellent.In the study,water content,compaction degree,confining pressure and stress conditions are more reasonable as influencing parameters,which can effectively reflect the changes of complex environmental influences.In conclusion,IPSO-Bi-LSTM can predict the dynamic resilience modulus of OFMS with high accuracy,and can provide a reliable basis for predicting and evaluating the life of OFMS roadbed,and expand its engineering application.
万方数据
维普
