Intelligent optimization method for slurry balance shield tunneling parameters based on ground settlement control

YIN Yulin; TAO Bowen; LI Jian; LI Qiming; WANG Gan

doi:10.11860/j.issn.1673-0291.20240028

Abstract：To address the issue of excessive ground settlement caused by improper design of tunneling parameters in slurry shield machines,this study proposes an intelligent optimization method for shield tunneling parameters that integrates machine learning algorithms.First,a five-step optimization frame-work is established,comprising geological information encoding,engineering data processing,shield response parameter prediction,ground settlement prediction,and control parameter optimization.Then,a data preprocessing pipeline tailored to the characteristics of shield tunneling data is developed to construct a sample database.Next,solution algorithms are respectively formulated for shield response prediction,ground settlement prediction,and control parameter optimization by applying Long Short-Term Memory(LSTM)neural networks and the Particle Swarm Optimization(PSO)algorithm.Finally,the proposed method is validated using a case study of the large-diameter slurry shield tunnel section between the Jingha Expressway and Luyuan North Street in the Beijing East Sixth Ring Road underground renovation project.The results indicate that geological encoding is an effective means of incorporating unstructured geological information into machine learning models.The shield response prediction model and ground settlement prediction model,both incorporating geological encoding as input,achieve an R2 of 0.92 on the test data,demonstrating strong predictive performance.Under the settlement control standard of-10 to 5 mm,the excavation parameters gen-erated by the intelligent optimization method reduce the average ground settlement by 29%compared to the measured data,offering valuable guidance for practical engineering applications.