Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

Geoscience Frontiers

Ning Zhang¹  Annan Zhou²  Song-Shun Lin³  Shui-Long Shen⁴ 

[1] Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria 3001, Australia;Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;MOE Key Laboratory of Intelligent Manufacturing Technology, College of Engineering, Shantou University, Shantou, Guangdong 515063, China;

关键词: EPB shield machine;    Advancing speed prediction;    Intelligent models;    Empirical analysis;    Tunnel excavation;   

DOI  :  

来源: DOAJ

【 摘 要 】

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance (EPB) shield tunnelling. Five artificial intelligence (AI) models based on machine and deep learning techniques—back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), long-short term memory (LSTM), and gated recurrent unit (GRU)—are used. Five geological and nine operational parameters that influence the advancing speed are considered. A field case of shield tunnelling in Shenzhen City, China is analyzed using the developed models. A total of 1000 field datasets are adopted to establish intelligent models. The prediction performance of the five models is ranked as GRU > LSTM > SVM > ELM > BPNN. Moreover, the Pearson correlation coefficient (PCC) is adopted for sensitivity analysis. The results reveal that the main thrust (MT), penetration (P), foam volume (FV), and grouting volume (GV) have strong correlations with advancing speed (AS). An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets. Finally, the prediction performances of the intelligent models and the empirical method are compared. The results reveal that all the intelligent models perform better than the empirical method.

【 授权许可】

Unknown