AUTHOR=Dong Leizhi , Wang Qingsong , Zhang Weiguo , Zhang Yongjun , Li Xiaoshuang , Liu Fei 

TITLE=Risk assessment of tunnel water inrush based on Delphi method and machine learning

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1555493

DOI=10.3389/feart.2025.1555493

ISSN=2296-6463

ABSTRACT=The water inrush is one of the most catastrophic emergencies in metro tunnels. To avoid the potential water inrush, this paper proposes a risk assessment model for the metro tunnel based on Delphi survey method and machine learning. The proposed model consists of two parts, the risk assessment index system and the risk level prediction model. Firstly, by using the Delphi survey method, appropriate risk factors are assembled into the water inrush risk assessment index system. To guarantee the accuracy of prediction results, only the correctly selected risk factors, validated by Grey Relational Analysis (GRA), are recognized as assessment indexes. Then, the Radial Basis Function (RBF) network, improved by the Locally Linear Embedding (LLE) algorithm and the Particle Swarm Optimization (PSO), is applied to predict the risk level. Training and test sample sets are constructed using engineering data from Qingdao metro tunnel construction. In the comparison with baseline models, the proposed model demonstrates the best accuracy and mean square error, which are 92.5% and 0.015, respectively. The LLE-PSO-RBF model is applied to the Qingdao Metro Line 4 tunnel project. Three tunnels are predicted by invoking the trained model, and the risk level of water inrush is I, III and IV, respectively.