In view of the pollution of unpaved road dust in the current mines, this study demonstrated the excellent dust suppression performance of the dust suppressant by testing the dynamic viscosity, penetration depth and mechanical properties of the dust suppressant, and apply molecular dynamics simulations to reveal the interactions between substances. The results showed that the maximum dust suppression rate was 97.75 % with a dust suppressant formulation of 0.1 wt% SPI + 0.03 wt% Paas + NaOH. The addition of NaOH disrupts the hydrogen bonds between SPI molecules, which allows the SPN to better penetrate the soil particles and form effective bonding networks. The SPI molecules rapidly absorb onto the surface of soil particles through electrostatic interactions and hydrogen bonds. The crosslinking between SPI molecules connects multiple soil particles, forming larger agglomerates. The polar side chain groups in the SPN interact with soil particles through dipole-dipole interactions, further stabilizing the agglomerates and resulting in an enhanced dust suppression effect. Soil samples treated with SPN exhibited higher compressive strength values. This is primarily attributed to the stable network structure formed by the SPN dust suppressant within the soil. Additionally, the SPI molecules and sodium polyacrylate (Paas) molecules in SPN contain multiple active groups, which interact under the influence of NaOH, restricting the rotation and movement of molecular chains. From a microscopic perspective, the SPN dust suppressant further strengthens the interactions between soil particles through mechanisms such as liquid bridge forces, which contribute to the superior dust suppression effect at the macroscopic level.

The treatment of soil with biopolymers has demonstrated various benefits, including strength enhancement, reduction in the permeability coefficient, and promotion of vegetation. Consequently, numerous experiments have been conducted to evaluate the strength of biopolymer-treated soils. This study aims to evaluate the interparticle bonding strength attributed to the biopolymer network formed between soil particles, focusing on the strength characteristics at the particle scale. Agar gum, a thermo-gelling biopolymer, was selected to assess the strength of biopolymer solutions. Experiments were conducted at concentrations of 2 %, 4 %, and 6 % with varying drying times to account for the differences in water content. The bonding, tensile, and shear strengths of the agar gum polymer solutions were evaluated under different loading conditions. To compare the strengths and meaningful trends observed in the agar gum polymer solution under different conditions. The results demonstrated that for all strength conditions involving the agar gum solution, the strength increased with higher concentrations and lower water content. During the particle size test, the bonding strength was improved up to 160 kPa, and the tensile strength of the agar gum polymer itself was observed to be up to 351 kPa. Furthermore, the UCS test results of the silica sand mixed with agar gum showed an improvement up to 1419 kPa. Among the evaluated strengths, the tensile strength was the highest, whereas the shear strength was the lowest. A comparison between the adhesive strength tests, which evaluated the strength characteristics at the soil particle scale, and the UCS of silica sand mixed with an agar gum solution revealed a similar trend. The shear strength increased consistently with drying time across all concentration conditions, which was consistent with the trends observed in the UCS. These findings suggest that the strength characteristics of soils treated with agar gum solutions can be effectively predicted and utilized for ground improvement applications.

Soil in combination with cement, more commonly referred to as soilcrete, has gained great popularity within the construction sector. To this end, its mechanical properties have to be determined quickly and accurately. Unfortunately, the conventional methods for determining them include lab tests which are rather expensive and error prone. A better solution however comes in the form of machine learning (ML) algorithms which have tremendous potential. Hence, this study sought to analyze how efficient the three algorithms in predicting the uniaxial compressive strength (UCS) of soilcrete. 400 samples of soilcrete were manufactured and analyzed, using two types of soil including clay and limestone along with metakaolin which served as a mineral additive. A total of 80% of the dataset was made use of for training while the remaining 20% served a testing purpose, in addition to the 37 datasets which were specifically designed for evaluation purposes. A Stepwise procedure was completed and a total of 8 parameters were identified including metakaolin and soil type, super plasticizer content, water to binder ratio, shrinkage, binder density and finally ultrasonic velocity. Most of the algorithms were able to achieve satisfactory results however Gaussian process regression (GPR), support vector regression (SVR) and null-space SVR (NuSVR) were able to stand out due to their potential performance. Focusing on the trained models and lab tests that were done, this research managed to establish the proper superplasticizer constitution (1%), water-to-binder ratio (0.4) and metakaolin content (12%) with the goal of achieving the highest UCS value in the provided soilcrete specimens. Furthermore, a graphical user interface (GUI) was created based on the trained ML models. For the civil engineers and researchers who need to estimate the UCS of the soilcrete specimens, this GUI greatly simplifies the process.

Concrete structures located in environments such as oceans, salt soils, and salt lakes are not only subjected to the sustained action of loads, but also to the erosive attack of sulphate ions at the same time, leading to changes in their mechanical properties. This paper focuses on the development of the mechanical properties of fly ash concrete over time, targeting axially compressed fly ash concrete components in a sulfate erosion environment. Under a stress level of 20 %, the paper takes into account factors such as fly ash contents of 25 %, 50 % and 75 %, loading ages of 28d, 90d and 120d, and sulphate solution concentrations of 2 %, 6 % and 10 %, respectively, conducting experimental research on the evolution of mechanical properties after the coupling effects of sustained load and sulfate erosion. Subsequently, the mechanism and law of evolution of axial compressive strength and modulus of elasticity of fly ash concrete after sustained loading coupled with sulphate erosion are analyzed. By using the concrete Compressible Packing Model (CPM) and the Triple-Sphere Model (TSM), along with a durability analysis of fly ash concrete under sustained loading, the calculation models of axial compressive strength, as well as the elastic modulus of fly ash concrete after the coupled action of sustained loading and sulphate erosion are established respectively. Finally, the model established in this paper is evaluated through data analysis using deviation analysis, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) methods, comparing it with existing models and experimental results. The research results show that, in terms of deviation analysis, the model established in this paper has a deviation of less than 1.5 % compared to the test data for elasticity modulus, and a deviation of less than 2 % compared to the test data for compressive strength. In terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), the model's errors compared to the experimental results for elasticity modulus and compressive strength are within 0.5. The comparison shows that the calculation results of the mechanical properties model of fly ash concrete constructed in this paper are in good agreement with the test data. The significance of the research lies in its ability to provide a theoretical basis for understanding the long-term performance development law of fly ash concrete structures in sulphate erosion environment.

Soilcrete is a composite material of soil and cement that is highly valued in the construction industry. Accurate measurement of its mechanical properties is essential, but laboratory testing methods are expensive, timeconsuming, and include inaccuracies. Machine learning (ML) algorithms provide a more efficient alternative for this purpose, so after assessment with a statistical extraction method, ML algorithms including back-propagation neural network (BPNN), K-nearest neighbor (KNN), radial basis function (RBF), feed-forward neural networks (FFNN), and support vector regression (SVR) for predicting the uniaxial compressive strength (UCS) of soilcrete, were proposed in this study. The developed models in this study were optimized using an optimization technique, gradient descent (GD), throughout the analysis (direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters). After doing laboratory analysis, data pre-preprocessing, and data-processing analysis, a database including 600 soilcrete specimens was gathered, which includes two different soil types (clay and limestone) and metakaolin as a mineral additive. 80% of the database was used for the training set and 20% for testing, considering eight input parameters, including metakaolin content, soil type, superplasticizer content, water-to-binder ratio, shrinkage, binder, density, and ultrasonic velocity. The analysis showed that most algorithms performed well in the prediction, with BPNN, KNN, and RBF having higher accuracy compared to others (R2 = 0.95, 0.95, 0.92, respectively). Based on this evaluation, it was observed that all models show an acceptable accuracy rate in prediction (RMSE: BPNN = 0.11, FFNN = 0.24, KNN = 0.05, SVR = 0.06, RBF = 0.05, MAD: BPNN = 0.006, FFNN = 0.012, KNN = 0.008, SVR = 0.006, RBF = 0.009). The ML importance ranking-sensitivity analysis indicated that all input parameters influence the UCS of soilcrete, especially the water-to-binder ratio and density, which have the most impact.

Soil-rock mixture is a common geo-material found in natural deposit slopes and various constructions, such as tunnels, hydropower stations, and subgrades. The complex mechanical characteristics of soil-rock mixture arise from its multi-phase compositions and cooperative interactions. This paper investigated the mechanical properties of soil-rock mixture, focusing on the influence of rock content, and soil-rock interface strength was discussed. Specimens with varying rock contents were subjected to uniaxial compression tests. The results indicated that rock content, as a key structural parameter, significantly controls the crack propagation trends. As rock content increases, the initial structure of the soil matrix is damaged, leading to the formation of a weak-strength soil-rock interface. The failure mode transitions from longitudinal cracking to multiple shear fractures. To analyze the strength of the soil-rock interface from a mesoscopic perspective, simulations of soil-rock mixture specimens with irregular rock blocks were conducted using the particle discrete element method (PDEM). At the meso-scale, the specimen with 30% rock content exhibited a complex particle displacement distribution, with differences in the direction and magnitude of displacement between soil and rock particles being critical to the failure modes of the specimen. As the soil-rock interface strength increased from 0.1 to 0.9, the distribution of force chains within the specimen shifted from a centralized to a more uniform distribution, and the thickness of force chains became increasingly uniform. The strength responses of the soil-rock mixture under uniaxial compression condition were discussed, revealing that the uniaxial compression strength (UCS) of soil-rock mixture decreases exponentially with increasing rock content. An estimation formula was developed to characterize the UCS of soil-rock mixture in relation to rock content and interface strength. The findings from both the experiments and simulations can provide valuable insights for evaluating the stability of deposit slopes and other constructions involving soil-rock mixture.

The present investigation explores the potential of alkali-activated slag as a novel method for stabilizing and enhancing the mechanical properties of loose sandy soils. To achieve this, unconfined compression tests were performed on samples with varying slag content, activator solution parameters, and curing conditions. A predictive model was developed to estimate UCS based on these factors. The microstructural analyses using field emission scanning electron microscopy and energy-dispersive X-ray spectroscopy elucidated the development of gels contributing to improved mechanical properties of the treated samples. Additionally, UCS tests demonstrated that increased slag content, activator concentration, and curing time significantly increase strength, stiffness, and brittleness. Notably, the findings show that samples treated with alkali-activated slag achieved substantially higher strength than those treated with ordinary Portland cement. These findings highlight the superior efficiency of this method in soil stabilization.

The disposal of tailings has always been a focal point in the mining industry. Semi-dry tailings stockpiling, specifically high-concentration tailings stockpiling, has emerged as a potential solution. To enhance the stability of tailings stockpiling and minimize its costs, the incorporation of a low-cost curing agent into high-concentration tailings is essential. Therefore, this study focuses on the development of a curing agent for high-concentration unclassified tailings stockpiling. The composition of a low-cost curing agent system is determined based on theoretical analysis, and the curing reaction mechanisms of each composition are researched. Subsequently, an orthogonal experiment is designed, and the strength of the modified unclassified tailings solidified samples at different curing ages is measured. Furthermore, the rheological properties of the modified unclassified tailings slurries are tested, and the feasibility of industrial transportation of the unclassified tailings slurries modified with the optimized curing agent is analyzed. Lastly, the microscopic morphologies of each material and the modified unclassified tailings solidified samples are characterized, their chemical compositions are tested, and the action mechanism of the curing agent in the curing system is further investigated. The results show that the optimal proportions of each material in the curing agent are as follows: slag, 58%; quicklime, 15%; cement, 8%; gypsum, 9%; and bentonite, 10%. The dominance of industrial waste slag exceeding 50% reflects the low-cost nature of the curing agent. Under this condition, the modified unclassified tailings slurry with a mass concentration of 75% exhibited a yield stress of 43.62 Pa and a viscosity coefficient of 0.67 Pas, which is suitable for pipeline transportation. These findings lay a foundation for subsequent decisions regarding stockpiling processes and equipment selection.

This paper investigates the effectiveness of applying continuous high-compression pressure on the initial setting of fresh concrete to produce hardened concrete materials with excellent mechanical properties. A novel experimental apparatus was self-designed and used for the pre-setting pressure application. The utilization of the completely decomposed granite (CDG) soil as an alternative aggregate in concrete production was also explored. A total of twenty-eight specimens were fabricated using two types of fine aggregates, six mix ratios, two initial pressure values, and two distinct durations of the initial pressure application. The density and uniaxial compressive strength (UCS) of the specimens were examined to evaluate their mechanical qualities, while micro-CT tests with image analysis were used to quantify their porosity. The results indicated that the 10 MPa initial pre-setting pressurization can effectively eliminate the excess air and voids within the fresh concrete, therefore enhancing the mechanical properties of the hardened concrete specimens of various types. Compared with non-pressurized specimens, the porosity values of pressurized specimens were reduced by 73.11% to 86.53%, the density values were increased by 1.43% to 8.31%, and the UCS values were increased by 8.42% to 187.43%. These findings provide a reference for using a continuous high pre-setting compression pressure and using CDG soil as an aggregate in the fabrication of concrete materials with improved mechanical performance.

In regions with sandy soft soil strata, the subway foundation commonly undergoes freeze-thaw cycles during construction. This study focuses on analyzing the microstructural and fractal characteristics of frozen-thawed sandy soft soil to improve our understanding of its strength behavior and stability. Pore size distribution curves before and after freeze-thaw cycles were examined using nuclear magnetic resonance technology. Additionally, fractal theory was applied to illustrate the soil's fractal properties. The strength properties of frozen remolded clay under varying freezing temperatures and sand contents were investigated through uniaxial compression tests, indicating that soil strength is significantly influenced by fractal dimensions. The findings suggest that lower freezing temperatures lead to a more dispersed soil skeleton, resulting in a higher fractal dimension for the frozen-thawed soil. Likewise, an increase in sand content enlarges the soil pores and the fractal dimension of the frozen-thawed soil. Furthermore, an increase in fractal dimension caused by freezing temperatures results in increased soil strength, while an increase in fractal dimension due to changes in sand content leads to a decrease in soil strength.