1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;
2. Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China;
3. Engineering Research Center of Urban Underground Development, Zhejiang University, Hangzhou 310058, China;
4. Shanghai tunnel Engineering Co., Ltd., Shanghai 200082, China
Prediction of maximum ground settlement induced by #br#
shield tunneling based on recurrent neural network
Li Luobin1,2,3 Gong Xiaonan1,2,3 Gan Xiaolu1,2,3 Cheng Kang1,2,3 Hou Yongmao4
Abstract：With the rapid development of computer technology, emerging algorithms such as machine learning are being increasingly used to predict the maximum ground settlement induced by tunneling. In the process of tunnel construction, the data collected by shield machines and ground monitoring points have strong serialization characteristics, and the traditional machine learning algorithm has certain limitations on the processing of such data. Recurrent neural network (RNN) has extremely strong ability to process time-series data, and has a wide range of applications in video recognition, speech translation and other fields. Taking geological parameters, geometric parameters and shield machine parameters as inputs, the prediction efficiency of two RNN models (LSTM, GRU) and a traditional BPNN model on the maximum ground settlement caused by the tunneling was investigated. The results show that the prediction result of RNN for tunnel settlement is better than that of traditional BPNN model, and the prediction result of RNN in continuous unknown sections is more stable than that of BPNN.
李洛宾 龚晓南 甘晓露 程康 侯永茂. 基于循环神经网络的盾构隧道#br#
引发地面最大沉降预测[J]. 土木工程学报, 2020, 53(S1): 13-19.
Li Luobin Gong Xiaonan Gan Xiaolu Cheng Kang Hou Yongmao. Prediction of maximum ground settlement induced by #br#
shield tunneling based on recurrent neural network. 土木工程学报, 2020, 53(S1): 13-19.
