Malan loess is widely distributed on the Chinese Loess Plateau and poses great challenges to geotechnical, ecological, and agricultural practices due to its unique structure and collapsibility. It is essential to understand the evolution of these properties with depth to assess soil stability and reduce engineering risks in the area. This study investigates the mechanical properties and microstructural evolution of Malan loess with depth and employs multivariate statistical methods to explore their complex interrelationships. Oedometer-collapse tests reveal a 94.2 % reduction in collapsibility coefficient (delta s) from 0.0722 at 1 m to 0.0042 at 9 m, indicating a significant reduction in collapsibility with increasing depth. According to the results of the direct shear test, it showed that the shear strength initially decreases and then increases due to the combined effect of the water content and dry density. Scanning electron microscopy (SEM) images reveal the densification of the loess structure, with changes in particle contact from point to face contact and the evolution from macropores to mesopores and small pores as depth increases. Quantitative analysis by Avzio showed a decrease of 61.5 % in macropores area and an increase of 62.5 % in small pores area. The results obtained by Pearson's correlation analysis and random forest model showed that among these microstructural characteristics, the total pore area (%IncMSE = 22.77 %) is the most important factor influencing the collapsibility properties of loess and water content (%IncMSE = 17.72 %) acts a key role in controlling shear strength. Additionally, compared to traditional methods, the random forest model offers a more insightful understanding of nonlinear relationships and multifactorial coupling effects. These findings provide scientific guidance for geotechnical engineering in loess regions, aiding in risk mitigation and promoting sustainable construction.

Landslides, which are a type of process-based geological hazard, exhibit stagewise characteristics that serve as important guidance for the prevention and mitigation of slope engineering disasters. The cross-correlation and randomness of soil parameters can influence the evolution of landslide characteristics. This paper, based on the spatial variability of slope soil parameters, combines copula theory and the material point method (MPM) to establish a Monte Carlo-random material point method considering the cross-correlation of soil parameters. This resulting method is called copula-RMPM. It investigates the probability distributions of slope instability and landslide large deformation characteristics, such as sliding distance, landslide thickness, collapse range, and volume of sliding mass. The results indicated that in the study of soil parameter characteristics, failure probability increases with increased correlation coefficient. Also, failure probability showed a positive correlation with the variability coefficient of cohesion and internal friction angle, with failure probability being more sensitive to the variability coefficient of the internal friction angle. The landslide large deformation characteristics generally follow the normal distribution; they exhibit significant fluctuations in sliding distance and sliding mass area despite the relatively small variability coefficient. Compared with the results of random field simulation of soil parameters, the probability of landslide large deformation characteristics obtained by deterministic soil parameters is often lower. Therefore, the probability distribution of landslide large deformation characteristics obtained by the Monte Carlo-random material point method considering the cross-correlation of soil parameters is more meaningful for engineering guidance.

Waste red layers have the potential to be used as supplementary cementitious materials after calcination, but frequent and long-term dry-wet cycling leads to deterioration of their properties, limiting their large-scale application. In this study, the feasibility of using calcined red layers as cement replacement materials under dry-wet cycling conditions was analyzed. The damage evolution and performance degradation of calcined red layer-cement composites (RCC) were systematically evaluated via the digital image correlation (DIC) technique, scanning electron microscopy (SEM) analysis and damage evolution mode. The results show that the calcined red layer replacement ratio and number of dry-wet cycles affect the hydration and pozzolanic reactions of the materials and subsequently affect their mechanical properties. Based on the experimental data, a multiple regression model was developed to quantify the combined effects of the number of dry-wet cycles and the replacement ratio of the calcined red layer on the uniaxial compressive strength. As the number of dry-wet cycles increases, microcracks propagate, the porosity increases, and damage accumulation intensifies. In particular, at a high substitution ratio, the material properties deteriorate further. The global strain evolution process of a material can be accurately tracked via DIC technology. The damage degree index is defined based on strain distribution law, and a damage evolution model was constructed. At lower dry-wet cycles, the hydration reaction has a compensatory effect on damage. The pozzolanic reaction of the calcined red layer resulted in an increase in the number of dry-wet cycles. The RCC samples with high replacement ratios show significant damage accumulation with fast damage growth rates at lower stress levels. The model reveals the nonlinear effects of dry-wet cycling and the calcined red layer replacement ratio on damage accumulation in RCC. The study findings establish a scientific foundation for the resource utilization of abandoned red layers and serve as a significant reference for the durability design of materials in practical engineering applications.

This study introduces a novel, interdisciplinary method that merges fundamental geomechanics with computer vision to develop an advanced hybrid feature-aided Digital Volume Correlation (DVC) technique. This technique is specifically engineered to measure and compute the full-field strain distribution in fine-grained soil mixtures. A clay-sand mixture specimen composed of quartz sand particles and kaolinite was created. Its mechanical properties and deformation behaviour were then tested using a mini-triaxial apparatus, combined with micro-focus X-ray Computed Tomography (mu CT). The CT slices underwent image processing for denoising, segmentation of distinct phases, reconstruction of sand particles, and feature extraction within the soil specimen. The proposed approach incorporated a two-step particle tracking method, which initially uses particle volume and surface area features to establish a preliminary matching list for a reference particle and then use the Iterative Closest Point (ICP) method for precise target particle matching. The soil specimen's initial displacement field was then mapped onto the DVC method's grid, and further refined through subvoxel registration via a three-dimensional inverse compositional Gauss-Newton algorithm. The proposed method's effectiveness and efficiency were validated by accurately calculating the displacement and strain fields of the soil mixture sample, and comparing the results with those from a traditional DVC method. Given the soil's compositional and microstructural characteristics, these image-matching techniques can be integrated to create a versatile, efficient, and robust DVC system, suitable for a variety of soil mixture types.

Despite significant advances in laboratory testing in recent decades, geotechnical designs that incorporate data from in-situ testing remain predominant worldwide. One of the most commonly employed techniques for correlating soil mechanical properties is the standard penetration test. However, while this test provides valuable information for identifying soil strata and offering general descriptions of soil characteristics, its correlation with shear strength parameters has several limitations that are often overlooked. In this article, we aim to i) present a critical literature review concerning the applicability of correlations between the undrained shear strength of fine-grained soils and standard penetration test data; ii) estimate the uncertainties associated with the adoption of these empirical correlations, which are frequently disregarded in engineering practice; iii) present simulation results from typical slope stability analyses, taking into account the uncertainties associated with the estimation of the undrained shear strength. The findings of our study suggest that geotechnical engineers should exercise caution when using such simplified equations, as they often lead to underestimations or overestimations of the stability of geotechnical structures.

Soil liquefaction is a major contributor to earthquake damage. Evaluating the potential for liquefaction by conventional experimental or empirical methods is both time-intensive and laborious. Utilizing a machine learning model capable of precisely forecasting liquefaction potential might diminish the time, effort, and expenses involved. This research introduces an innovative predictive model created in three phases. Initially, correlation analysis determines essential elements affecting liquefaction. Secondly, predictions are produced using Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN), verified by K-fold cross-validation to guarantee resilience. Third, Ant Colony Optimization (ACO) improves outcomes by increasing convergence efficiency and circumventing local minima. The suggested EC + ACO model substantially surpassed leading approaches, such as SVM-GWO, RF-GWO, and Ensemble Classifier-GA, attaining a very low False Negative Rate (FNR) of 2.00 % when trained on 90 % of the data. A thorough performance evaluation shown that the model achieved a cost value of 1.133 % by the 40th iteration, exceeding the performance of other models such SVMGWO (1.412 %), RF-GWO (1.305 %), and Biogeography Optimized-Based ANFIS (1.7439 %). The model exhibited significant improvements in convergence behavior, with a steady decline in cost values, especially between the 20th and 50th iterations. Additional validation using empirical data from the Tohoku-oki, Great East Japan earthquake substantiated the EC + ACO model's enhanced accuracy and dependability in mirroring observed results. These findings underscore the model's resilience and efficacy, providing a dependable method for forecasting soil liquefaction and mitigating its seismic effects.

Acid contamination has a notable influence on the geotechnical properties of soil and this influence is strongly dependent on contamination concentration (pH) and contamination duration. To fully investigate the effect of acid contamination on the microscopic and strength properties of natural clay, a series of micro- and macrolaboratory tests were performed in this study, and the mechanism of this effect was comprehensively revealed. Microscopic analysis indicates that acid contamination could lead to some mineral transformations in clay, such as illite-smectite transforming into chlorite and illite transforming into kaolinite. Besides, more large pores and a looser structure can be observed in the clay due to the erosional effects of acid contamination, which could effectively weaken the strength properties of natural clay. The experimental results also indicated that, when subjected to acid contamination, the lower contamination pH could lead to a notable decrease in clay's shear strength, while the clay's shear strength increased initially and then decreased as contamination duration increased. In addition, gray correlation analysis results demonstrated that calcite has a significant effect on cohesion, while also indicating a strong correlation between illite and the internal friction angle.

Blueberries are the most popular small berries, in order to solve the problem of unbalanced blueberry resources in different regions of China. In this study, 18 blueberries were analyzed by chromatography and mass spectrometry for 9 soil elements, 6 anthocyanins, 7 phenolic acids, 9 organic acids, and 12 flavonoids. The result showed that blueberry physico-chemical indicators were significantly variable across production regions by Wenn and volcano maps, chlorogenic acid, ascorbic acid, citric acid, catechin were the main antioxidant active components, soil pH was significantly correlated with low content of anthocyanins and organic acids, soil elements were not significantly correlated with fruits antioxidant activity by the network correlation analysis. Cluster analysis and principal component analysis classified the antioxidant activity and fruit quality: represented by YNorthland, SNorthland, JSharpblue. It provides theoretical support for screening high quality blueberries and promoting the development of blueberry industry.

Slope failure, as a natural disaster, can cause extensive human suffering and financial losses worldwide. This paper introduces a new soil moisture extended cohesive damage element (SMECDE) method to predict railway slope failure under heavy rainfall. A correlation between rainfall intensity and soil moisture content is first established to create an equivalence between the two. Considering slope failure mechanisms dominated by the loss of soil or the cohesion of slope materials due to heavy rainfall infiltration, the soil moisture decohesion model (SMDM) is developed using previous experimental data to express how soil cohesion varies with different soil moistures and depths. The SMDM is incorporated into the extended cohesive damage element (ECDE) method to fundamentally study slope failure mechanisms under varying soil moisture levels and depths. The proposed SMECDE approach is used to predict the failure propagation of a selected railway embankment slope at the critical soil moisture or rainfall intensity. This SMECDE failure prediction is validated using relevant data from previous fieldwork and meteorological reports on the critical rainfall intensity at the site. Additionally, the corresponding slope damage scale prediction is validated with a large plastic deformation analysis using the commercial FEM package ABAQUS.

Pisha sandstone (PS) rapidly collapses in water and its performance deteriorates seriously, and its special engineering properties have always been the focus of researchers. In areas where fillers are scarce, it is of great significance to use PS as roadbed fillers to slow down soil erosion and green environmental protection. However, the long-term deformation characteristics after construction need further study. To reveal the long-term dynamic characteristics of Pisha sandstone fillers (PSF) under vehicle load, this study conducted the cyclic loading test of PSF by using the GDS triaxial test system. The deformation characteristics of PSF under different cyclic stress ratios (zeta) and load frequencies (f) were studied. The grey correlation analysis method was used to obtain the correlation degree of each influencing factor to the cumulative plastic strain (CPS) of the PSF. Finally, the grey GM (1,1) model is used to predict the CPS data of PSF. Based on this, the classical semi-logarithmic strain model is modified, and the CPS prediction model of PSF is established. The results reveal that the zeta and f will promote the development of axial deformation of PSF. The axial elastic deformation (epsilon(e)) and CPS of PSF increase with the increase of zeta, and the zeta has a great influence on the CPS. The influence of f on epsilon(e) is more significant at high stress levels and less significant at low stress levels. The influence of f on CPS is opposite to that of epsilon(e), that is, the influence of high stress level is small, and the influence of low stress level is large. According to the degree of correlation, the factors are sorted according to the degree of influence: static strength (sigma(f)) > confining pressure (sigma(3)) > dynamic static stress ratio (eta) > load frequency (f) > cyclic stress ratio (zeta). The GM (1,1) model has high accuracy and reliability for the quantitative description and prediction of the CPS of PSF. At the same time, according to the test and GM (1,1) model prediction results, the CPS prediction model of PSF was established. The research can provide insights and references for the establishment of cumulative deformation and prediction model of PSF under cyclic loading.