This study investigated the effects of calcium carbide slag (CCS) (0–12 wt%) incorporation on the workability, electrochemical properties, durability, and microstructure evolution of sulfoaluminate cement (SAC) mortar. Results showed that increasing CCS content reduced mortar fluidity and shortened setting time, indicating that CCS accelerates early hydration. A 9% CCS content was determined to be the optimal dosage; at 28 days, compared to the control group, this dosage group exhibited a 6.53% increase in compressive strength, a 22.47% decrease in drying shrinkage, and a 0.279% decrease in mass loss. These performance improvements stemmed from CCS’s ability to inhibit pore connectivity and limit moisture migration. Electrochemical analysis further revealed that the 9% CCS dosage group had the highest charge transfer resistance and resistivity (30.00% higher than the control group), reflecting a denser matrix and greater ion transport resistance. Consequently, chloride ion permeability was significantly reduced, with electrical flux and diffusion coefficient decreasing by 39.98% and 28.89%, respectively. Microstructural observations confirmed that CCS promotes the formation and densification of hydration products, effectively improving the internal pore structure. While 9% CCS can serve as an effective functional supplementary material, its long-term durability and sustainability still face practical application challenges. Future research should focus on establishing predictive models for chloride ion permeation lifetime and conducting quantitative sustainability assessments of CCS-SAC composites, particularly evaluating material cost, energy consumption, and carbon dioxide emissions.

Coal gangue (CG) and iron tailings (ITs) are major industrial solid wastes, and their high-value reuse is crucial for sustainable construction materials. This study explores the feasibility of fabricating sintered porous bricks using CG and ITs as primary constituents, with shale as an auxiliary component. To evaluate durability in cold regions, laboratory freeze–thaw (F-T) cycling experiments were conducted. A degradation assessment framework based on the Wiener stochastic process was developed to predict frost-resistance service life by integrating experimental data with regional climatic conditions. Results show that the fabricated bricks exhibit satisfactory initial properties, with a compressive strength of 10.6 MPa and water absorption of 13.3%. With increasing F-T cycles, compressive strength decreases significantly, accompanied by increased mass loss and water absorption. Stress–strain analysis reveals progressive stiffness reduction and a transition from brittle to ductile failure. Microstructural observations confirm degradation of the glassy phase, pore expansion, and enhanced interconnectivity. The Wiener process-based model effectively describes the stochastic accumulation of F-T damage. By establishing equivalence between laboratory and natural F-T cycles, the long-term service life of coal gangue–iron tailing sintered porous bricks (CG-IT SPBs) in cold regions is theoretically evaluated. This work provides an integrated understanding of F-T damage behavior and establishes a scientific foundation for durability-oriented design and application of such bricks in extremely cold environments.

Ground-penetrating radar (GPR) is among the primary techniques for detecting concealed road cavities; however, radargram interpretation is highly dependent on manual expertise, which may result in delayed or incorrect judgments and hinder timely structural and safety risk mitigation. This paper proposes RacNet, a lightweight model that integrates finite-difference methods with deep learning for rapid and automated road cavity detection. The model establishes a morphology-based theoretical formulation derived from real road cavity characteristics and constructs a multi-source cavity radargram dataset including engineering, experimental, and simulated data. An adaptive feature extraction and efficient feature fusion strategy is adopted for improved detection performance while reducing computational complexity. Additionally, a diversified batch simulation system was developed to encode and quantify real data characteristics, enabling rapid and diverse road cavity radargram generation. Experimental results show that RacNet achieves a detection accuracy of 98.4%, reduces computational complexity by 29.3%, and increases inference speed by 34.1%. Furthermore, RacNet outperformed baseline models and was successfully applied in real-world road inspections. The detected cavities were validated via borehole imaging, confirming the effectiveness and generalization capability of the proposed method. These findings support the application of deep learning in engineering measurement and provide technical support for risk assessment and safety management in road infrastructure.