Prediction of Land Subsidence Caused by Shield Tunnel Construction with Joint CNN-LSTM Neural Network

Abstract

Objective Metro shield tunnel construction may cause surrounding land subsidence, affecting the surrounding environment. Traditional land subsidence prediction models are difficult to comprehensively consider the influencing factors of land subsidence. Therefore, in order to improve the prediction accuracy of land subsidence, the CNN(convolutional neural network)-LSTM(long-short-term memory) joint neural network is used to predict the land subsidence caused by shield tunnel construction. Method With the monitored land subsidence data of a metro section as the research object, CNN is used to connect the influencing parameters (including compressive modulus, cohesion, internal friction angle, Poisson′s ratio, soil thickness, tunnel buried depth, and construction parameters) and monitored land subsidence data. The LSTM neural network is used to analyze the land subsidence, and a land subsidence prediction model based on the CNN-LSTM joint neural network is established. Simultaneous consideration of the multiple factor influence on land subsidence prediction is explored. Result & Conclusion Using CNN has a good effect on extracting the parameter features related to land subsidence. The prediction accuracy of the established CNN-LSTM model is 3% higher than that of the LSTM model alone, and 9% higher than that of the traditional BP (back propagation) neural network model. The prediction accuracy of the established CNN-LSTM model reaches 93% when predicting the short time land subsidence in single measuring point, and the predicted value is in good agreement with the monitored value.

Keywords

• shield tunnel construction
• land subsidence
• prediction
• convolutional neural network
• long-short-term memory neural network