Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Biological soil crusts (BSCs; biocrusts) are well developed in drylands, which are crucial to the stability and resilience of dryland ecosystems. In the southeastern Gurbantunggut Desert, a typical sandy desert in the middle part of central Asia, engineering development has an increasing negative impact on ecosystems. Fortunately, ecological restoration measures are being implemented, but the exact effect on soil quality is still unclear. In artificial sand-fixing sites on reshaped dunes along the west-east desert road, a total of 80 quadrats (1 m x 1 m) of reed checkerboards after the implementation of sand-fixing measures for 10 years were investigated to determine the BSC development status and soil properties. The algal and lichen crusts accounted for 48.75 % and 26.25 % of the total quadrat number, respectively, indicating an obvious recovery effect of BSC (only 25 % for bare sand). The developmental level of BSC gradually increased from the top to the bottom of the dunes (Li 0 -> Li 6),which was consistent with the distribution pattern of BSCs on natural dunes. Compared with bare sand, the soil organic carbon (13.85 % and 23.07 % increases), total nitrogen (12.55 % and 23.95 % increases), total potassium (9.30 % and 8.24 % increases), and available nitrogen (23.97 % and 61.41 % increases) contents of algal and lichen crusts were significantly increased, and lichen crusts had markedly higher increase effect than algal crusts. The BSC development markedly reduced soil pH (0.49 % and 0.50 % decreased) and increased electrical conductivity(11.99 % and 10.68 % increases), resulting in improved soil microenvironment. Soil properties showed significant linear relationships with BSC development level, and an optimal fitting (R2 = 0.770 or 0.780) was detected for the soil fertility index. Based on the soil property matrix, the bare sands, algal, and lichen crusts were markedly separated along the first axis in the PCA biplot, which once again confirmed the significant positive effect of BSC recovery on soil fertility improvement. Consequently, in the early stage of sand-fixation (e.g., < = 10 years) by reed checkerboards on the damaged desert surface, BSC recovery can well promote and predict soil fertility in this area. The results provide a reliable theoretical basis for the restoration technology and scientific management of degraded sandy desert ecosystems.

This computational study focuses on the thermo-hydro-mechanical simulations of the behaviors of freezing soils used for artificial ground freezing (AGF) in a metro project. Leveraging the experimental and field data available in the literature, we simulate the sequential freezing and excavation of a twin tunneling that occurred in months during the actual construction of the tunnel. A thermo-hydro-mechanical model is developed to capture the multi-physical rate-dependent behaviors triggered by phase transitions, as well as the creeping and secondary consolidation of the soil skeleton and the ice crystals. We then calibrate the material models and establish the THM finite element model coupled with the rate-dependent multi-physical models, which may accurately predict the surface heave induced by ground freezing throughout the project. To showcase the potential of using simulations to guide the AGF, we simulate the scenario where a simultaneous freezing scheme is employed as an alternative to the actual sequential scheme design. We then compared the simulated performance with the recorded results obtained from the sequential scheme. Finally, parametric studies on the effect of ground temperature, the porosity of the frozen soil, and the intrinsic elastic modulus of the solid skeleton are conducted. The maximum surface heave is inferred from finite element simulations to quantify the sensitivity and the impact on the safety of AGF operations.

Freeze-thaw cycles pose a serious threat to the protection and preservation of earthen sites. To investigate the effects of freeze-thaw cycles on the shear strength and permeability of site soil, this study took artificially prepared site soil as the research object. Through triaxial shear tests and permeability tests, the strength and permeability characteristics of site soil under different sticky rice slurry content, sticky rice slurry density and freeze-thaw cycles were analyzed. In addition, the mineral composition, chemical structure, and microstructural characteristics of the samples were investigated by combining X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) tests. The results showed that the addition of sticky rice slurry could increase the shear strength and decrease the permeability coefficient of the soil, while the opposite effect was exhibited after freeze-thaw cycle. The optimum ratio of loess to sticky rice slurry was 95:5, and the optimum density of sticky rice slurry was 1.04 g/cm3. The addition of sticky rice slurry and the increase in the number of freeze-thaw cycles did not significantly change the mineral composition of the soil. The SEM results showed that the morphology and arrangement of soil particles became complex after freeze-thaw cycle, the inter-particle connections became loose, and the pore morphology became irregular. The surface porosity of the soil increased, and the proportion of large and medium pores increased. The directionality of the pores was enhanced, and the complexity of the pores increased. The pore arrangement became relatively stable after 15 freeze-thaw cycles. These findings can provide a reference for the restoration of ancient sites in loess areas.

True triaxial tests were conducted on artificially frozen sand. The effects of the intermediate principal stress coefficient, temperature and confining pressure on the strength of frozen sand were studied. The stress-strain curves under different initial conditions indicated a strain hardening. In response to increases of either the intermediate principal stress coefficient or the confining pressure or to a decrease of temperature, the strength typically increased. Furthermore, a new strength criterion was proposed to describe the strength of artificially frozen sand under a constant b-value stress path, combining the strength function in the p-q and pi planes. Considering the low confining pressure, the strength criterion in the p-q plane fitted the linear relationship in the parabolic strength criterion well. The strength criterion in the pi plane was combined with stress invariants, and a new strength criterion was established. This criterion considers unequal tension and compression strength, and integrates temperature. Test results indicated its validity. All parameters of the strength criterion could be easily determined from the triaxial compression and triaxial tensile tests.

Tensile cracks play a pivotal role in the formation and evolution of reservoir landslides. To investigate how tensile cracks affect the deformation and failure mechanism of reservoir landslides, a novel artificial tension cracking device based on magnetic suction was designed to establish a physical model of landslides and record the process of landslide deformation and damage by multifield monitoring. Two scenarios were analyzed: crack closure and crack development. The results indicate that under crack closure, secondary cracks still form, leading to retrogressive damage. In contrast, under crack development conditions, the failure mode changes to composite failure with overall displacement. The release of tensile stresses and compression of the rear soil are the main driving forces for this movement. Hydraulic erosion also plays a secondary role in changing landslide morphology. The results of multifield monitoring reveal the effects of tensile cracking on reservoir landslides from multiple perspectives and provide new insights into the mechanism of landslide tensile-shear coupled damage.

Artificial ground freezing (AGF) is an effective technique for ground stabilization in projects such as tunneling and shaft mining. This study examines the impacts of freeze-thaw processes, soil type, and compaction levels on the strength characteristics of sandy and clayey soils and evaluates AGF performance through laboratory-scale physical modeling using liquid nitrogen as the cooling agent. Results indicate that freezing significantly enhances soil strength, but thawing leads to notable reductions. Sandy soils compacted to 95% experienced a 50% decrease in unconfined compressive strength (UCS) after brief exposure to thawing, while clayey soils exhibited a smaller reduction of 30%. Compaction emerged as a critical factor in strength retention, with UCS in sandy soils decreasing by 50% when compaction dropped from 95 to 85%, compared to a 25% reduction in clayey soils. The results also demonstrated that sandy soils froze more rapidly and efficiently, achieving a frozen diameter of approximately 25 cm around a single freezing pipe within 4 h, compared to 15 cm in clayey soils over 8 h. Furthermore, sandy soils required less liquid nitrogen to achieve the same frozen column compared to clay soils, owing to their higher thermal conductivity and lower water retention. These findings highlight the superior efficiency of AGF in sandy soils under controlled conditions, particularly when water seepage is absent, and underscore the importance of optimizing compaction levels and freeze-thaw parameters to enhance the cost-effectiveness of soil stabilization. The study provides valuable insights into soil behavior during AGF, particularly the impact of thawing, supporting its broader application in various geotechnical projects.

Understanding slope stability is crucial for effective risk management and prevention of slides. Some deterministic approaches based on limit-equilibrium and numerical methods have been proposed for the assessment of the safety factor (SF) for a given soil slope. However, for risk analyses of slides of earth dams, a range of SFs is required due to uncertainties associated with soil strength properties as well as slope geometry. Recently, several studies have demonstrated the efficiency of artificial neural network (ANN) models in predicting the SF of natural and artificial slopes. Nevertheless, such techniques operate as black-box models, prioritizing predictive accuracy without suitable interpretability. Alternatively, multivariate polynomial regression (MVR) models offer a pragmatic interpretability strategy by combining the analysis of variance with a response surface methodology. This approach overcomes the difficulties associated with the interpretability of the black-box models, but results in limited accuracy when the relationship between independent and dependent variables is highly nonlinear. In this study, two models for a quick assessment of slope SF in earth dams are proposed considering the MVR and the ANN models. Initially, a synthetic dataset was generated considering different soil properties and slope geometries. Then, both models were evaluated and compared using unseen data. The results are also discussed from a geotechnical point of view, showing the impact of each input parameter on the assessment of the SF. Finally, the accuracy of both models was measured and compared using a real-case database. The obtained accuracy was 78% for the ANN model and 72% for the MVR one, demonstrating a great performance for both proposed models. The efficacy of the ANN model was also observed through its capacity to reduce false negatives (a stable prediction when it is not), resulting in a model more favorable to safety assessment.

Artificial ground freezing (AGF), widely employed in subway tunnel construction, significantly alters the microstructure of surrounding soils through freeze-thaw processes. These changes become critical under subway operation, where traffic-induced dynamic loading can lead to progressive soil deformation. Understanding the dynamic behavior of freeze-thaw-affected soils is therefore essential for predicting and mitigating deformation risks. This study investigates the microstructural evolution of soil subjected to a single freeze-thaw cycle-representative of AGF practice-and subsequent dynamic loading. Dynamic triaxial tests were conducted under a fixed dynamic stress amplitude of 10 kPa and loading frequencies of 0.5 Hz, 1.5 Hz, and 2.5 Hz, simulating typical subway traffic conditions. Microstructural analyses were performed using mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM). Results show that the freeze-thaw cycle leads to a denser yet more disordered particle arrangement, with sharper and more angular particles, as reflected by increased probability entropy and reductions in surface porosity, form factor, and uniformity coefficient. Dynamic loading further causes particles to flatten and align in a more directional manner, accompanied by decreased surface porosity and form factor, and an increased uniformity coefficient. Pore structures become more uniform and less complex. Among various microstructural indicators, total intrusion volume from MIP displays a strong correlation with cumulative plastic strain, suggesting its potential as a micro-scale predictor of soil deformation. These findings enhance our understanding of the coupled effects of freeze-thaw and dynamic loading on soil behavior and offer valuable insights for improving the safety and durability of subway tunnel systems constructed using AGF.

This study utilizes a combined approach of Finite Element Method (FEM) simulation and Artificial Neural Network (ANN) modeling to analyze and predict the load-displacement relationship of bored piles in clayey sand. FEM is applied to simulate the nonlinear relationship between load and vertical displacement, with input parameters including load and the mechanical properties of the soil. The results obtained from FEM are used as input data for the ANN model, enabling accurate predictions of vertical displacement based on these parameters. The findings of this study show that the predicted ultimate bearing capacity of the bored piles is highly accurate, with negligible error when compared to field experiments. The ANN model achieved a high level of accuracy, as reflected by an R2 value of 0.9992, demonstrating the feasibility of applying machine learning in pile load analysis. This research provides a novel, efficient, and feasible approach for analyzing and predicting the bearing capacity of bored piles, while also paving the way for the application of machine learning in geotechnical engineering and foundation design. The integration of FEM and ANN not only minimizes errors compared to traditional methods but also significantly reduces time and costs when compared to field experiments.