The widely acknowledged Mononobe-Okabe (MO) model is not recommended for seismic passive earth pressures analysis because it yields nonconservative results when the ratio of the wall’s to the soil’s frictional angle is equal to or greater than 0.5. A more advanced analytical approach, termed the log-spiral-Rankine (LSR) method, was recently introduced and is capable of producing more conservative solutions for the aforementioned scenario by considering soil cohesion, a curvilinear failure surface, and so on. Implementation of the LSR method, however, is quite complicated, as it requires several iterations to obtain the final converged solution. This study proposes an alternate way to approximate the more accurate LSR solutions from the simple MO equation for evaluation of seismic passive earth pressures in cohesive backfills. The approach uses two modification factors to account for the effects of soil cohesion and curvilinear failure surface, respectively. The overall error of the proposed equation is approximately 2%. More importantly, the new equation is easy to implement and thus suitable for routine design practice.

With the rapid development of underground space, structural crack monitoring has become increasingly critical. This study proposes a unified framework integrating image preprocessing, feature extraction, model training, and safety assessment for crack analysis. An improved OTSU threshold segmentation algorithm based on sliding windows and local statistical analysis is developed to enhance noise suppression and detail preservation under complex backgrounds and varying resolutions. For crack identification and orientation classification, SVM, CNN, ResNet-18, and K-means clustering are systematically compared. The results show that the improved OTSU method outperforms the classical approach in both high- and low-resolution images. In classification tasks, SVM achieves the best performance under limited data conditions, with accuracy exceeding 96% and reaching 97% after outlier removal, outperforming CNN, K-means, and ResNet-18. Although ResNet-18 demonstrates strong overall performance with high prediction confidence across crack categories, it remains slightly inferior to SVM when training data are limited. Experimental validation using full-scale loading tests of metro shield tunnel segments further confirms the robustness of the proposed approach, with SVM achieving an accuracy of 95.45% in real-world conditions. This study provides an efficient and reliable solution for automated crack detection and classification in metro tunnel infrastructure and similar underground segment-based systems.

Water–bentonite slurry is widely used in slurry shield tunneling due to its ability to stabilize the tunnel face and transport excavated soils. As the shield advances, natural soil is eaten by the cutting teeth and inevitably mixes with fresh water–bentonite slurry, among which silt clay-sized soil particles are difficult to remove, impeding reuse of the slurry and posing challenges for tunneling safety and efficiency. Therefore, it is crucial to evaluate the performance and adaptability of slurry containing silt clay soil. In this study, non-swelling silt clay (kaolin) was added to water–bentonite slurries of varying bentonite concentrations, creating water–bentonite–kaolin slurries with densities ranging from 1019 to 1200 kg/m3. The rheological properties of these slurries were evaluated using a concentric cylinder rheometer, while the hydraulic characteristics of the resulting filter cakes were examined via modified fluid loss tests. The results showed that due to the addition of kaolin, higher-density slurries displayed increased rheological parameters. Slurries with higher bentonite concentrations showed pronounced non-linear rheological profiles, attributed to bentonite's strong thixotropic properties. Both higher bentonite concentrations and slurry densities produced thicker filter cakes with extremely low hydraulic conductivities (approximately 10−9 m/s). Pure bentonite slurries formed filter cakes with the highest void ratios and greater water retention, whereas denser slurries exhibited reduced pore space. Furthermore, pressure gradients under laminar flow conditions in a specific pipe were quantified, and a reference chart was developed to preliminarily determine the slurry's capacity to suspend soil particles. The implications for slurry shield tunneling practices were discussed.