Intelligent optimization of TBM cutter spacing and FDEM-based investigation of rock breakage considering brittleness

Abstract

Cutter spacing is a key factor influencing the efficiency of TBM operations. Meanwhile, rock brittleness, as a critical indicator of rock fracture, significantly influences fragmentation behavior and rock-breaking efficiency. This study investigates the influence of rock brittleness on rock-breaking through numerical penetration experiments based on the hybrid finite-discrete element method (FDEM) and proposes four intelligent hybrid models to optimize cutter spacing. The results show that as the rock brittleness index (BI) increases from 4.731 to 32.588, the count, depth, width, and proportion of tensile cracks increase, and crack propagation shifts from horizontal to oblique orientations. Moderate cutter spacing (90-110 mm) is optimal for generating tensile cracks. The rock-breaking force increases significantly with higher BI; for instance, at 80 mm spacing, the maximum force for rock with a BI of 13.134 is 5.51 times that for rock with a BI of 4.731. The influence of BI on cutter work and specific energy (SE) is more substantial than the effect of cutter spacing. As BI increases, both cutter work and SE rise considerably. Among the proposed models, the particle swarm optimization and extreme gradient boosting (PSO-XGBoost) model demonstrates the highest performance, achieving an R² of 0.994, VAF of 99.418%, RMSE of 0.987, and MAPE of 5.217% on the test datasets. An optimization method for cutter spacing is proposed based on this model.

Keywords

Rock brittleness / Rock-breaking efficiency / Cutter spacing / PSO-XGBoost / FDEM

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Youlin Qin: Writing - review & editing, Writing - original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Li Yu: Writing - original draft, Supervision. Mingnian Wang: Supervision, Funding acquisition. Zhaohui Chen: Visualization. Hong Jin: Writing - review & editing. Mingyang Yu: Data curation. Songshen Wang: Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 51578458).

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