The changing Arctic climate is affecting groundwater flow and storage in supra-permafrost aquifers due to groundwater recharge changes and thaw-driven alterations to aquifer properties and connectivity. Changes to shallow subsurface hydrological processes can drive extensive ecological and biogeochemical changes in addition to potential surface hydrologic regime shifts. This study uses a pan-Arctic geospatial approach to classify shallow, unconfined Arctic aquifers (supra-permafrost aquifers) as topography-limited (TL) (characterized by low permeability, wet climate, and/or low slopes) or recharge-limited (high permeability, dry climate and/or steep slopes) based on the water table ratio framework. Under current conditions, the continuous and discontinuous permafrost zones were determined to be predominantly (65%) TL, with an average net decrease of 5.6% by the year 2100 under RCP2.6 and RCP8.5 conditions. This apparent stability masks local-scale heterogeneity, with change in aquifer function projected at dispersed locations throughout the Arctic, and in clustered hot spots in Siberia and the central Canadian Arctic. Coastal zones around the Arctic are more TL (94%) compared to the overall average, leaving them especially vulnerable to ocean-driven impacts on groundwater such as subsurface seawater intrusion or groundwater flooding. Arctic coasts in Siberia and eastern Canada are also particularly susceptible to water table rise due to high relative sea-level rise which may exceed the active layer thickness and result in substantive changes to saturation. Classification results are sensitive to input values, particularly hydraulic conductivity, which remains a source of uncertainty in the analysis. Despite the sparsity of Arctic data, the available open-source datasets provide valuable insight into broad spatiotemporal trends in aquifer function and highlight particularly vulnerable regions and geographic areas where uncertainty should drive additional data collection and study. These results provide new context for conceptualizing changes to shallow Arctic aquifers as the climate evolves in the 21st century.

Aim Alaska's boreal forest is experiencing increasingly severe fires, droughts, and pest attacks that may destabilize carbon sequestration. Our aim was to understand boreal forest resilience to changing wildfire regimes using remote-sensed datasets validated with ground-truthing (GT).Location Five recently burned boreal forest sites (2010-2019) near Fairbanks, Alaska.Methods We used four AVIRIS-NG hyperspectral image datasets (425 spectral bands at 5-nm intervals; 3.5 x 43 km average swath) imaged by NASA in 2017-2018 during the Arctic-Boreal Vulnerability Experiment (ABoVE). Spectral analysis included fire fuel loads and random forest (RF) models constructed from key bands to describe common pre- and postburned vegetation classes. Models were validated with 89 GT plots inside the AVIRIS scenes. GT included tree stem densities, understory cover, soil characteristics, radial growth of 51 spruce trees from cores, and visual damage assays of 668 conifers and deciduous trees.Results Spectral evidence of high fuel loads in 2017 pre-dated a 2019 wildfire. Post-GT local models described vegetation more accurately than pre-GT, but accuracy decreased when spectral rulesets were broadened to increase overall classification. Soil temperature, basal area, slope, elevation, and tree density varied widely; thaw depth, soil moisture, moss cover, and canopy height varied mainly by vegetation class. Invasive species and thermokarst were insignificant. Deciduous seedlings were abundant in postburned sites; however, conifer seedling densities were similar to unburned forest. Upland spruce radial growth showed earlier drought sensitivity than lowland spruce.Conclusion Spectral analysis revealed fire vulnerability in some areas; however, local and temporal spectral variation presented challenges to accurately classify vegetation in AVIRIS scenes. GT suggests that recovering forests near Fairbanks may lack sufficient conifer recruitment to replace existing stands. Sites with stable seasonal thaw may offset drought stress under global warming.

As a potential source of damage, earthquake-induced liquefaction is a major concern for embankment safety and serviceability. Densification has been a popular method for improving the performance of liquefiable soils. Understanding embankment settlement mechanisms plays a fundamental role in determining densification remediation. In this work, nonlinear dynamic analysis of embankments on liquefiable soils is conducted by the finite-difference program FLAC3D (version 6.0) with the simple anisotropic sand constitutive model. Numerical models are validated via dynamic centrifuge test results reported in the literature. The effects of densification countermeasures on the mean and differential settlements are explored in this study. Furthermore, the effects of the densification spacing and width are investigated to optimize the geometry of the densified regions. The development of pore pressure and the movement of the surrounding loose soil are discussed. The results show that both the mean settlement and differential settlement should be simultaneously utilized to comprehensively assess the overall effectiveness of densification treatment. The mean settlement is influenced by the densification spacing and width, but the differential settlement is highly associated with the inner edge of the densified region. This study provides insight for improving the design of the location and lateral extent of densification regions to prevent excessive embankment settlement.

This study quantifies the seismic fragility assessment of shallow-founded buildings in liquefiable and treated soils, enhanced by drainage and densification, considering both short-and long-term behaviors. A conceptual framework is proposed for developing seismic fragility curves based on engineering demand parameters (EDPs) of buildings subjected to various earthquake magnitudes. The framework for establishing seismic fragility curves involves three essential steps. First, nonlinear dynamic analyses of soil-building systems are performed to assess both the short-term response, which occurs immediately following an earthquake, and the longterm response, when excess pore water pressure completely dissipates, and generate a dataset of building settlements. The seismic responses are compared in terms of excess pore water pressure buildup, immediate and residual ground deformation, and building settlement to explore the dynamic mechanisms of soil-building systems and evaluate the performance of enhanced drainage and densification over short-and long-term periods. Second, 38 commonly used and newly proposed intensity measures (IMs) of ground motions (GMs) are comprehensively evaluated using five statistical measures, such as correlation, efficiency, practicality, proficiency, and sufficiency, to identify optimal IMs of GMs. Third, fragility curves are developed to quantify probability of exceeding various capacity limit states, based on structural damage observed in Taiwan, for both liquefaction-induced immediate and residual settlements of buildings under different levels of IMs. Overall, this study proposes a rapid and straightforward probabilistic assessment approach for buildings in liquefiable soils, along with remedial countermeasures to enhance seismic resilience.

After sand liquefaction, buried underground structures may float, leading to structural damage. Therefore, implementing effective reinforcement measures to control sand liquefaction and soil deformation is crucial. Stone columns are widely used to reinforce liquefiable sites, enhancing their resistance to liquefaction. In this study, we investigated the mitigation effect of stone columns on the uplift of a shield tunnel induced by soil liquefaction using a high-fidelity numerical method. The liquefiable sand was modeled using a plastic model for large postliquefaction shear deformation of sand (CycLiq). A dynamic centrifuge model test on stone column-improved liquefiable ground was simulated using this model. The results demonstrate that the constitutive model and analysis method effectively reproduce the liquefaction behavior of stone column-reinforced ground under seismic loading, accurately reflecting the time histories of excess pore pressure ratio and acceleration. Subsequently, numerical simulations were employed to analyze the liquefaction resistance of saturated sand strata and the response of a shield tunnel before and after reinforcement with stone columns. Additionally, the effects of densification and drainage of the stone columns were separately studied. The results show that, after installing stone columns, the excess pore pressure ratio at each measurement point significantly decreased, eliminating liquefaction and mitigating the uplift of the tunnel. The drainage effect of the stone columns emerged as the primary mechanism for dissipating excess pore pressure and reducing tunnel uplift. Furthermore, the densification effect of stone columns effectively reduces soil settlement, particularly pronounced around the stone columns, i.e., at a distance of three times the diameter of the stone column.

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.

This study investigates the application of machine learning (ML) algorithms for seismic damage classification of bridges supported by helical pile foundations in cohesive soils. While ML techniques have shown strong potential in seismic risk modeling, most prior research has focused on regression tasks or damage classification of overall bridge systems. The unique seismic behavior of foundation elements, particularly helical piles, remains unexplored. In this study, numerical data derived from finite element simulations are used to classify damage states for three key metrics: piers' drift, piles' ductility factor, and piles' settlement ratio. Several ML algorithms, including CatBoost, LightGBM, Random Forest, and traditional classifiers, are evaluated under original, oversampled, and undersampled datasets. Results show that CatBoost and LightGBM outperform other methods in accuracy and robustness, particularly under imbalanced data conditions. Oversampling improves classification for specific targets but introduces overfitting risks in others, while undersampling generally degrades model performance. This work addresses a significant gap in bridge risk assessment by combining advanced ML methods with a specialized foundation type, contributing to improved post-earthquake damage evaluation and infrastructure resilience.

The properties of soils are highly complex, and therefore, the classification system should be based on multiple perspectives of soil properties to ensure effective classification in geotechnical engineering. The current study of research demonstrates a lack of correlation between classification systems based on soil plasticity and those based on in-situ mechanical properties of soils. A CPTu-based plasticity classification system is proposed using the soil behaviour type index (Ic), with its reliability and limitations discussed. The results indicate that (1) Ic has the capacity to predict the stratigraphic distribution from the in-situ mechanical properties of soils. It showed a significant linear correlation with wL, which soil classification zone was similar to that of clay factor (CF); (2) A CPTu-plasticity classification system is proposed to characterize both plasticity and in-situ mechanical properties of soils. This system allows for the initial classification of soils solely based on CPTu data. Furthermore, it has been established that Ic = 2.95 can delineate the boundary between high- and low-compressibility soils. (3) The error is only 25.2% relative to the Moreno-Maroto classification chart, and the system tends to classify soils of intermediate nature as clay or silt. The distance between the data points and both the C-line and the new C-line (Delta Ip, Delta IpIc) showed a significant positive correlation. Only one data point was misclassified, considering human error in measuring Ip. (4) The new classification chart has been found to be more applicable to offshore and marine soils.

Background and AimsSoil salinization is a major cause of land degradation and ecological damage. Traditional soil salinity monitoring techniques are limited in coverage and scalability, while remote sensing offers broader applicability and efficiency. This study addresses spatiotemporal variations in soil salt content (SSC) inversion across crop types in Tongliao City, Inner Mongolia, China, using an innovative integration of multi-temporal data and crop cover types, improving remote sensing monitoring accuracy.MethodsField sampling data and Sentinel-2 images from June to September in 2021 and 2022 were utilized. The deep learning U-net model classified key crops, including sunflower (33%), beet (12%), and maize (55%), and analyzed the effects of crop coverage on SSC across multiple time series. Six spectral variables were selected using the SVR-RFE model (R2 = 0.994, MAE = 0.016). SSC prediction models were developed using three machine learning methods (DBO-RF, PSO-SVM, BO-BP) and a deep learning method (Transformer).ResultsConsidering crop coverage variations improved the sensitivity of spectral variables to SSC response, enhancing predictive accuracy and model stability. Crop classification showed that the salinity index (SIs) correlated more strongly with SSC than the vegetation index (VIs), with SI6 having the highest correlation coefficient of 0.50. The Transformer model, using multi-time series data, outperformed other algorithms, achieving an average R2 of 0.71. The SSC inversion map from the Transformer model closely matched field survey trends.ConclusionThis research provides a novel approach to soil salinity prediction using satellite remote sensing, offering a scalable solution for monitoring salinization and valuable insights for environmental management and agricultural planning.

The substantial development of desiccation cracks profoundly impacts the mechanical and hydraulic properties of clayey soils, potentially leading to various engineering challenges such as slope failures. Therefore, identifying the soil's cracking potential is crucial for guiding engineering design and construction processes. The aim of this study was to propose a method for cracking potential classification for clayey soils. To this end, standard cyclic wet-dry tests, capable of maximizing the soil's cracking potential, were proposed based on an analysis of the cracking behavior of lateritic soils under different wet-dry conditions. Subsequently, the cracking characteristics of several typical clayey soils (i.e., lateritic soil, kaolinite, bentonite, and attapulgite) were examined by standard cyclic wet-dry tests. Finally, a novel method for cracking potential classification of clayey soils was proposed incorporating the entropy weighting method. The results show that the most significant degree of cracking in lateritic soil is observed under vacuum saturation and 60 degrees C oven-drying, which is identified as the standard wet-dry condition. When the crack development stabilizes after multiple standard wet-dry cycles, the cracking potential of the soil is characterized by parameters such as the total crack length, maximum crack width, surface crack rate and the fractal dimension of the cracks. On this basis, a classification method is proposed to categorize the cracking potential of clayey soils into five levels: extremely weak, weak, medium, strong, and extremely strong. The cracking potential of different clayey soils was evaluated using this method, revealing that bentonite exhibited the highest cracking potential, classified as extremely strong.