Optimal pressure control for segment assembly in synchronous tunnel boring machines considering time delay
Year: 2025
Type: article
Abstract: To address the problems of stress concentration and response delay during the segment assembly process in synchronous tunnel boring machines (S-TBMs), this paper proposes an optimization framework for redistributing the assembly hydraulic cylinder pressure. Bayesian optimization Kolmogorov-Arnold networks (BO-KAN) is employed to predict the cylinders’ pressure, while generalized differential evolution 3 (GDE3) is applied for pressure redistribution of the cylinders. The effectiveness of the proposed method is verified through an S-TBM experimental platform. The results indicate that: 1) BO-KAN accurately predicts the pressures of 16 hydraulic cylinders, achieving a high coefficient of determination (R 2 ) values with an average of 0.998. 2) BO-KAN performs superior predictive capability against other models, achieving an R 2 score increase of 0.100 and a composite improvement rate of 1.012 %. 3) GDE3 exhibits superior efficiency during the 1 s delay effect scenario, attaining a 46.955 bar max difference of pressure reduction and a 28.117 % optimization enhancement. 4) GDE3 demonstrates a significant effect in pressure redistribution across different engineering conditions, with the average optimization degrees for the max pressure of the optimal pressure being 11.465 %. The innovation of this study lies in developing a pressure redistribution framework through the integration of BO-KAN and GDE3 taking into account time delay, which significantly mitigates pressure concentration problems and response hysteresis phenomena in S-TBM construction processes.
Authors: Limao Zhang, Benyinan Huang, Yongsheng Li, Xieqing Song, Maozhi Wu
Institution: Huazhong University of Science and Technology
Language: English
Topic: Tunneling and Rock Mechanics
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,820
OpenAlex ID: W6940710713