A barrier wall is a vertical engineered layer designed to block contaminated soil, thereby controlling pollution sources, preventing pollutant migration to groundwater, and limiting pollution spread. Cement–bentonite barrier walls, widely adopted for their seepage control capability, structural strength, and cost-effectiveness, face sustainability challenges due to high cement consumption. This study systematically investigates the coupled effects of steel slag substitution rate and bentonite dosage on the mechanical–permeability of barrier materials for the first time and proposes steel slag (containing dicalcium silicate (C₂S) and tricalcium silicate (C₃S) phases similar to cement clinker) as a partial cement substitute in steel slag–cement–bentonite barrier materials, aiming to reduce cement usage and utilize industrial waste. Through unconfined compressive strength tests, direct shear tests, and variable head permeability tests, the effects of steel slag substitution rates (0~50%) and bentonite dosages (46~54%) on material performance were systematically investigated. Key findings include (1) unconfined compressive strength decreases linearly with increasing steel slag substitution but grows exponentially with bentonite dosage; (2) cohesion exhibits a negative exponential relationship with steel slag substitution and a linear positive correlation with bentonite content—the unconfined compressive strength of the materials with bentonite dosage of 50% and 54% were 1.51 and 2.84 times higher than those with bentonite dosage of 46%, respectively; (3) cohesion and unconfined compressive strength conform to c = (0.23~0.39)q_u; (4) permeability decreases with higher steel slag substitution and bentonite dosage, achieving controlled low permeability (<1 × 10⁻⁷ cm/s). This research provides a sustainable solution for barrier wall construction by integrating waste recycling and performance optimization.

Vertical containment barriers—critical for intercepting contaminant transport in subsurface environments—demand materials that balance low permeability with adequate strength, particularly in stress-sensitive mountainous terrain. Plastic concrete, as a key barrier material, provides essential properties, including exceptional stress relaxation, to suppress fracture development under compressive loads, coupled with effective seepage control. This study examines its strength performance through experiments on varied mixing techniques (dry, wet, and 24 h hydration), unconfined compression under uncontaminated conditions (water–binder ratios: 1.3–2.1, bentonite content: 20–30%, ages: 14–90 days), barium ion immersion (1–5 g/L, pH 7–11), and dry–wet cycling (10 cycles). Key findings demonstrate that (1) the strength of samples prepared by dry mixing and wet mixing is lower than that of samples mixed for 24 h, and all specimens met the target design strength following 28 days of curing; (2) under pollution-free conditions, strength decreases with higher water–binder ratios and bentonite content, showing a linear relationship. Strength increases exponentially with age; (3) in the presence of Ba²⁺, strength gradually decreases as Ba²⁺ concentration and pH increase, particularly notably at 3 g/L Ba²⁺ and pH 11. Strength increases with age, following a power relationship; (4) under dry–wet cycles, ion concentration has minimal impact on sample quality and surface state but significantly affects strength, with higher ion concentrations leading to greater strength loss and susceptibility to cycles; (5) during solution immersion, higher ion concentrations and pHs result in greater strength loss and worse erosion resistance.

Establishing a correlation between the Cone Penetration Test (CPT) and the Standard Penetration Test (SPT) is of significant importance for geotechnical engineering practice. A novel correlation between CPT-q_t and SPT-N₆₀ based on a genetic algorithm (GA) and linear regression was proposed in this study. Based on the soil behavior type index (I_c), a GA was first applied to divide the dataset into different I_c intervals. Subsequently, linear regression was performed separately for the data in each interval to establish a correlation between CPT-q_t and SPT-N₆₀. Concurrently, the Segmented Information Criterion (SIC) was introduced to perform dual-objective optimization of complexity and prediction accuracy. The results indicate that the proposed model achieved an R² of 0.60 and an RMSE of merely 8.10. Specifically, the R² values improved by 33% and 5% compared to the traditional models and the AI models, respectively. On the validation dataset, the proposed model achieved an R² of 0.67 and an RMSE of 4.33, demonstrating higher accuracy compared to the traditional models. In summary, a method for investigating the CPT-SPT correlation is proposed in this study, characterized by simplicity, efficiency, and enhanced reliability. Additionally, a novel criterion (SIC) for mitigating overfitting is introduced. These two research findings can provide more reliable input parameters for SPT-based design, thereby supporting geotechnical engineering applications, and offer a valuable reference for relevant studies in other regions.