doi:10.16186/j.cnki.1673-9787.2020.5.20

Received:2019/11/31

Revised:2020/02/09

Published:2020/09/15

Prediction of TBM performance based on optimized BP neural network

ZHAO Guangzu1, WANG Yaxu1, LI Yao1, XU Shoutian2, CHEN Shuai2

1.Geotechnical and Structural Engineering Research Center， Shandong University， Jinan  250061 ， Shandong， China;2.China Railway Engineering Equipment Group Co. ，Ltd. ，Zhengzhou  450016 ，Henan， China

Abstract:Due to TBM penetration velocity having complex nonlinear relationship with machine parameters and rock mass parameters， it is difficult to predict the TBM performances accurately. In order to construct a reliable TBM performance prediction model， the main influencing factors of TBM penetration velocity were discussed， and TBM performance prediction models were proposed based on BP neural network optimized by simulated annealing algorithm and genetic algorithm， and GA-BP model and the SA-BP model were trained and tested based on the TBM database of Jilin Songhua River Water Supply Project. Compared with the traditional BP neural network， prediction of the optimized model had better generalization and significantly improved accuracy. The results showed that the BP neural network optimized by simulated annealing and genetic algorithm could overcome the drawback that were easy to fall into local optimum to some extent， and it had a good performance on TBM performance prediction.

Key words:tunnel boring machine;penetrate velocity;rock mass parameter;neural network

基于优化BP神经网络的TBM性能预测_赵光祖.pdf