Prediction Method of Double Shield TBM Tunnelling Attitude Based on Tunnelling Physical Law and LSTM
The controlling of the TBM tunnelling attitude is the key to ensure the quality of tunnel construction.Establishing the relationship between tunnelling attitude and control parameters,and predicting the TBM excavation posture based on this is one of the key challenges that urgently needs to be addressed in this field.This paper introduces a TBM tunneling attitude prediction method.Using Long-Short Term Memory(LSTM)neural network as a bridge,the initial tunnel tunneling attitude and control parameters of each ring are used as inputs to predict the horizontal and vertical bias angles and distances of each ring.To overcome the inherent shortcomings of overfitting and error accumulation in LSTM,a physical law of TBM tunneling attitude was established based on the TBM movement principle,and it was introduced as a constraint into the conventional LSTM algorithm.Based on the Qingdao Metro Line 6 project,a total of 140 sets of data were collected to establish a mining attitude prediction model based on an improved LSTM method,in order to verify the prediction accuracy and generalization of the method.
TBMtunnelling attitudelong-short term memory neural networksequential prediction
万方数据
维普
