The tunnel face stability in composite strata, commonly referred to as the soft upper and hard lower condition, is a critical challenge in tunnel construction. The soft–hard ratio (SA) strongly influences the limit support pressure as well as the failure mechanism experienced by a tunnel face. This study focused on the Xingye Tunnel project in the Xiangzhou District of Zhuhai City. By conducting numerical simulations, the impact of different SAs on the limit support pressure was investigated. Furthermore, a limit equilibrium model was established on the basis of the analysis of the results of numerical simulation. The findings were then compared and analyzed alongside those of relevant theoretical models. In the event of tunnel face instability of composite strata, the deformation tends to be concentrated mainly in the soft soil layer, with less noticeable deformation in the hard rock layer. The investigation of different SAs revealed a linear decrease in the limit support pressure ratio of the tunnel face in composite strata as SA decreases. The self-stability of the tunnel face was observed when SA ≤ 0.125. Moreover, the limit support pressure ratio predicted by the truncated log-spiral model (TLSM) exhibited a higher degree of agreement with the results of numerical simulation than those of other relevant models. The superiority of TLSM was mainly demonstrated in the range of SA = 0.25 to 1.0.

This study investigates the micro-mechanical behavior of mortar under uniaxial compression using a three-phase model in PFC^3D. By simulating mortar as a composite of cement, sand, and the interfacial transition zone (ITZ), the research examines the impact of particle size on stress–strain behavior, crack propagation, porosity distribution, contact forces, and energy transformation. The simulations reveal that reducing sand particle size from 1–2 mm to 0.25–0.5 mm leads to a significant increase in uniaxial compressive strength, with peak strength values rising from 65.3 MPa to 89.6 MPa. The elastic modulus similarly improves by approximately 20% as particle size decreases. The study also finds that tensile cracks dominate failure, accounting for over 95% of total cracks, with their onset occurring at lower strains as the particle size is reduced. Porosity analysis shows that smaller particles result in a more uniform distribution, with the final porosity at peak strength ranging between 0.26 and 0.29, compared to 0.22 to 0.31 for larger particles. Additionally, energy dissipation patterns reveal that as particle size decreases, the boundary energy transformation into strain energy becomes more efficient, with a 15% increase in strain energy storage observed. These findings provide critical insights into optimizing mortar microstructure for enhanced mechanical performance in construction applications.

Coral sand is widely used as an aggregate in concrete to reduce costs and improve efficiency in marine engineering. However, it increases the porosity and brittleness of concrete, leading to steel corrosion. This study introduced basalt fibers to enhance the strength and corrosion resistance of concrete. The hydration process and pore structure of basalt fiber-reinforced coral sand concrete (BFRCSC) were analyzed through nuclear magnetic resonance techniques. During a 96 h hydration period, the intensity of total, gel, and small capillary water exhibited fluctuations, whereas large capillary and free water dropped sharply within the initial 10 h. The pore size distribution was analyzed using fractal theory. The analysis revealed that total fractal dimensions D, which corresponds to a combination of harmful and very harmful pores in the specimens after curing for 7 days, 14 days and 28 days, ranged from 2.84 to 2.923. Compressive strength decreased as porosity increased, but BF–0.1, which had a 0.1% basalt fiber content, achieved the highest strength due to its lower proportion of very harmful pores and higher total fractal dimension. This study emphasizes the importance of very harmful pores in determining the compression strength of BFRCSC, as analyzed using the fractal dimension.

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.

The rapid expansion of land reclamation necessitates a fundamental understanding of the strain rate effects on structured clays. While the rate effect has been widely studied in various soils, the interplay between bond structure and strain rate sensitivity remains unclear. This study investigates these mechanisms using artificially cemented kaolin (ACK) with controlled curing periods (2 and 30 days) to simulate naturally bonded clays. A series of undrained triaxial tests was conducted under low (100 kPa) and high (600 kPa) confining stresses, employing constant strain rates (0.01–5%/h) pre-peak and stepwise rate changes post-peak. The results reveal that the strain rate effects are governed by the bond structure maturity and drainage mechanisms. For the 30-day curing ACK, the pre-peak strength under low confining stress shows minimal rate sensitivity due to the rigid bond, while high confining stress induces a “negative” rate effect attributed to localised drainage along shear planes. The post-peak behaviour consistently exhibits a positive isotach-type rate effect (+3%/log-cycle) driven by viscous sliding. In contrast, the 2-day curing ACK displays negative rate effects pre-peak influenced by ongoing curing and post-peak strength reductions (−8%/log-cycle) linked to stick-slip dynamics. These findings establish a framework for predicting rate-dependent behaviour in structured clays, offering insights into land reclamation and subsequent construction work.

Proper soil conditioning is critical for earth pressure balance (EPB) shield tunneling to mitigate problems such as mud cake formation and cutter headwear by enhancing muck flow plasticity, particularly in sandy pebble soils. This research investigates the effect of conditioning agents—bentonite slurry and foam—on sandy pebble soils with varying fines contents, considering both concentration and injection ratio. Slump tests were conducted to evaluate the workability of conditioned soil. As fines content increases from 10 to 80%, the slump value of unconditioned soil decreases from 238 to 47 mm because of poor grading. Bentonite-conditioned soils with 10%–20% fines content decrease in slump value as slurry injection ratio (SIR) rises to 8% and 6%. This increase is attributed to improved grading and interlocking from the clay particles. As SIR and fines content increase, clay particles and free water form a film around the coarse particles and act as a lubricant, increasing slump value. In soils with 60% and 80% fines contents, foam significantly enhances workability, with slump value rising to 200 mm as FIR increases. To predict slump values and evaluate workability for improved engineering efficiency, this study employs a deep learning algorithm, considering the nonlinear relationship between slump values under different conditions. This model, with an accuracy exceeding 95%, contributes to developing rapid and intelligent solutions for soil conditioning in urban tunnel projects.