The construction environment of deep rock tunnels is complex, and effectively enhancing tunnel boring machine (TBM) tunneling efficiency is paramount. Increasing rock-breaking efficiency and minimizing cutter consumption are essential strategies for improving TBM tunneling efficiency. Selecting suitable tunneling parameters is crucial for enhancing rock-breaking efficiency and reducing cutter consumption. Existing research on the optimization of the ratio of maximum cutter spacing to penetration (S_max/P) based on field-measured data is limited, and few studies compare and analyze the relationship between SE, CI, and the S_max/P ratio separately. Consequently, this study determined optimal tunneling parameters for various types of surrounding rock and construction environments, aiming to more accurately optimize TBM tunneling performance during construction processes based on on-site construction data. This study conducted a comparative analysis of specific energy (SE) and the coarseness index (CI). Under both working conditions, the quadratic fitting coefficients of the CI are 4.2% and 10.6% higher than those of the SE, respectively, with the CI selected to represent the particle size distribution of rock chips. Finally, taking into account both the correlations between the CI and the ratio of maximum cutter spacing to penetration (S_max/P), as well as cutter consumption and the S_max/P ratio, an optimization method for the TBM tunneling parameter was established under both dry and saturated conditions. The research findings indicate that cutter consumption exhibits an exponential increase with a higher rock Cerchar Abrasivity Index (CAI); it initially decreases as the S_max/P ratio increases and subsequently increases in both dry and saturated conditions. Instead, the CI demonstrates an initial increase and subsequent decrease as the S_max/P ratio increases. Maximizing rock-breaking efficiency and minimizing cutter consumption are crucial for improving tunneling performance. In saturated conditions, the corresponding optimal S_max/P ratio ranges are 7.055–8.319 for soft rock, 8.606–8.931 for medium–hard rock, and 13.50–14.00 for hard rock, and these optimal ranges under dry conditions are 8.495–9.457, 10.972–12.169, and 16.5–17.5 for the same rock types. This study provides optimal S_max/P ratio ranges for TBM tunneling, thereby significantly enhancing tunneling efficiency.

Triaxial rheological tests are performed on mudstones collected from the soft interlayer in the Three Gorges Reservoir Area. In the tests, both dry and saturated conditions are considered, and a complete rheological process is then observed. Based on such laboratory observations of stress‐strain, a four‐element rheological constitutive model is developed, which is composed of (i) the Hook element, (ii) the Kelvin element, (iii) the viscoelastic‐plastic body, and (iv) the nonlinear viscous body (HKCV model). The HKCV model adopts the one‐dimensional and three‐dimensional equations that are derived. The rheological parameters required are identified, allowing the successful development of the HKCV model. A comparison with the laboratory test observations and the existing model estimates shows that the estimates of the HKCV model are relatively consistent with the observations of the triaxial rheological test. The HKCV model better characterizes the rheological process than the three existing models. However, the HKCV model has the limitation of requiring more parameters than the existing models.

Lithology variations, which are recognized as rock type differences, significantly affect the physical properties of rock masses in red beds. In this paper, we introduce a statistical method for quantitatively evaluating the degree of lithology variations in layered red beds with depth. The core of this method is to use borehole logs as random variables for statistical analysis to calculate the percentage of a specified lithology at a given elevation, which involves seven steps of attitude calculation, reference point selection, distance calculation, elevation modification, data discretization, data statistics, and curve plotting. The Yellow River Guxian Dam is chosen as a field case study. We classify rock types of feldspar sandstone, fine sandstone, and conglomerate into hard rocks and that rock types of calcareous siltstone, argillaceous siltstone, and mudstone into soft rocks. Borehole logs recorded during the geological investigation are used to plot the percentage curve of hard rocks. We find that the degree of lithology variations for each lithology group differs greatly, the general behaviors of lithology variations on two sides of the Guxian Dam riverbed are quite similar but still with some differences, and that some thick lithology groups can be finely divided into several subgroups. On the basis of the hard rock percentage curve, we introduce a lithology variation index to quantitatively characterize the degree of lithology variations, which can be used as an important index to supplement the traditional methods when performing rock mass classifications in red beds. We also plot the trilithology percentage curve of sandstone, calcareous siltstone, and argillaceous siltstone, which serves for the determination of physical parameters of the dam foundation rock mass, the identification of the potential shear sliding surface, and the search for an impervious grouting bottom. Moreover, we find that the crest and trough, which are local high and low points in the hard rock percentage curve, can be used to show some characteristics of shear zones. The locations of shear zones are well represented in the form of troughs and that the development of shear zones has a good linear relationship with the hard rock percentage of the corresponding crest. The method proposed in this paper can be promoted and applied in similar projects or studies.