Tunnel boring machine (TBM) is widely used in tunnel construction. The boring efficiency is mainly affected by the cutter arrangement and construction parameters, in which the cutter spacing is designed to be reasonable and whether the penetration degree is set to be appropriate are the most important two aspects impacting the efficiency of rock breaking. As a direct product of rock breaking by cutter, rock ballast can provide comprehensive and intuitive feedback on the rock-breaking efficiency of TBM. The correlation mechanism between ballast particle size distribution and rock breaking efficiency is revealed by analyzing the rock chip morphology of granite cutting test. Based on the theory of characteristic particle size to predict the optimal penetration, it is verified by multiple working conditions that: The optimal penetration is 2.0 mm for cutter spacing of 60~70 mm, 2.5 mm for cutter spacing of 80 mm, and 3.0 mm for cutter spacing of 90~100 mm, and the characteristic particle size of the rock chips reaches the peak when the ratio of the cutter spacing to the penetration is in the range of 30~40, corresponding to the optimal efficiency of rock-breaking. It can dynamically optimize and adjust the combination of boring parameters according to the real-time monitoring of the characteristics of the rock chip particle size, and realize the scientific matching of the knife spacing and penetration.

Image analysis of rock chips is an important approach for TBM intelligent tunneling, and it can be used for real-time evaluation of rock-breaking efficiency and optimization of tunneling parameters. In order to investigate the relationships between TBM tunneling parameters and rock chip characteristics, a multi-step field tunneling test was carried out in an intact granite formation at a TBM construction section of Qingdao Metro Line 6. A rock chip image acquisition system was installed on the TBM to real-time collect rock chip images. Then, the relationship between the tunneling parameters and the size and shape characteristics of rock chips obtained through image processing was analyzed. The results showed that the median particle size d₅₀, maximum particle size d_max and roughness index CI all present a positive correlation with thrust, torque and penetration, while a good negative correlation with FPI and SE. When the thrust exceeds a critical value for rock-breaking, the increase in thrust will significantly increase d₅₀ and CI, while d_max increases significantly only when the thrust exceeds a larger value. Besides, as the thrust increases, the shape of the large rock chips gradually becomes flattened and the aspect ratio increases. However, after the thrust reaches a certain level, the aspect ratio will remain stable. The results can provide a basis for real-time optimization of TBM tunneling parameters based on rock chip information.