Abstract
To address stress concentration issues during the assembly of tunnel boring machine (TBM) segments, particularly in the context of synchronous excavation and segment assembly (SESA) TBMs, this paper presents an optimization framework focused on hydraulic cylinder pressure redistribution. The paper develops a Bayesian optimization extreme gradient boosting (BO-XGBoost) model for cylinder status detection and devises an online Archimedean optimization algorithm (AOA) for cylinder pressure redistribution. Verification of the proposed approach's effectiveness is conducted using a SESA TBM experimental bench. The results indicate that: 1) The proposed hydraulic cylinder status detection model effectively distinguishes various cylinder operating statuses, achieving a high F1-score of 0.9891. 2) Across the six segment assembly scenarios, the stress concentration phenomenon can be improved by 16.03%, 14.29%, 8.84%, 31.10%, 15.47%, and 11.11%, respectively. 3) In comparison to the offline optimization algorithm, the online algorithm reduces the maximum pressure by 36.69% and decreases algorithmic error by 30.50%. A key contribution from this paper is the introduction of an active pressure control approach integrated with the online multi-objective optimization algorithm, which is helpful in effectively improving the stress concentration phenomenon in the SESA TBM.
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