A novel MgO-mixing column was developed for deep soft soil improvement, utilizing in-situ deep mixing of MgO with soil followed by carbonation and solidification via captured CO2 injection. Its low carbon footprint and rapid reinforcement potential make it promising for ground improvement. However, a simple and cost-effective quality assessment method is lacking. This study evaluated the electrical properties of MgO-mixing columns using electrical resistivity measurements, exploring relationships between resistivity parameters and column properties such as saturation, strength, modulus, CO2 sequestration and uniformity. Microscopic analyses were conducted to elucidate the mechanisms underlying carbonation, solidification, and electrical property changes. The life cycle assessment (LCA) was performed to assess its carbon reduction benefits and energy consumption. The findings reveal that the electrical resistivity decreases rapidly with increasing test frequency, remaining constant at 100 kHz, with the average electrical resistivity being slightly higher in the upper compared to the lower section. Additionally, electrical resistivity follows a power-law decrease with increasing saturation. Both electrical resistivity and the average formation factor exhibit strong positive correlations with unconfined compressive strength (UCS) and deformation modulus, enabling predictive assessments. Furthermore, CO2 sequestration in MgO-mixing columns is positively correlated with electrical resistivity, and the average anisotropy coefficient of 0.96 indicates good column uniformity. Microstructural analyses identify nesquehonite, dypingite/hydromagnesite, and magnesite as significant contributors to strength enhancement. Depth-related changes in electrical resistivity parameters arise from variations in the amount and distribution of carbonation products, which differently impede current flow. LCA highlights the significant low-carbon advantages of MgOmixing columns

The cumulative plastic deformation and damage evolution of frozen soil-rock mixtures under cyclic loading was studied by a dynamic triaxial instrument with real-time resistivity measurement function. A series of low- temperature cyclic triaxial tests were conducted under varying confining pressures (200 kPa, 500 kPa, 800 kPa), block proportions (0, 30 %, 40 %, 50 %), and dynamic stress ratios (0.4, 0.6, 0.8). The results reveal that the cumulative plastic deformation process can be divided into three stages, such as microcrack closure as the initial stage, crack steady growth as the middle stage, and rapid crack propagation until it fails as the final stage. Under the same number of cycles, the greater the dynamic stress is, the greater the cumulative plastic deformation is. Furthermore, a strong correlation is identified between the resistivity and the cumulative plastic deformation. With the increase of the number of cycles, the cumulative plastic deformation leads to the accumulation of internal damage, and the resistivity gradually increases. Thus, a damage evolution model based on resistivity damage variables is proposed. The model demonstrated an average fitting accuracy of 97.36 % with the experimental data.

Research on conductive models of damaged soil that consider the effect of microcrack expansion (the degree of saturation and suction) is weak. By assuming an equivalent conductive path a unit series-parallel conductive model of damaged soil under environmental loads was proposed. This model shows the change in soil porosity and fractal dimension. To verify that, the soil was damaged by rainfall cycles (simulated natural drying and rainfall). Electrical measurements and X-ray microscopy tests were performed to obtain the damaged soil resistivity, porosity, and fractal dimension variation. The resistivity was calculated based on the conductive model, and the error was approximately 7.9% compared with that of the test. In addition, the soil damage variable related to soil porosity and fractal dimension was introduced, and it exhibited a logarithmic relationship with soil resistivity. Variations in soil damage during the rainfall cycles were observed. In the top layer, the soil porosity increased and the fractal dimension decreased owing to microcrack expansion, resulting in an increase in soil damage. In contrast, in the bottom layer, the soil porosity decreased and the fractal dimension increased, resulting in a decrease in soil damage due to particle migration from the top area and pore fill.

Earthquakes and rainfall both cause soil damage and strength degradation of cutting slopes, resulting in increased slope instability. However, few studies have been conducted on the failure mechanisms of cutting slopes under earthquakes and rainfall. In this study, field electrical measurements were conducted to evaluate the damage to a cutting slope hit by the Yangbi Earthquake (MS = 6.4) in Yunnan Province, China. After material segmentation using the resistivity probability density statistical method, we observed several damaged areas running along the slope depth direction, forming several potential sliding surfaces. Furthermore, considering the slope damage after the earthquake, a discrete element model of the slope was developed, and the dynamic process of the gravel-soil landslide under rainfall was analyzed. Compared with low cutting slope with thin overburden sliding along one sliding surface, the results indicate that the high cutting slope with thick overburden slides along several sliding surfaces that formed by the earthquake-step sliding mods. Slope sliding can be divided into four stages: First, the slope body at the bottom area slid and accelerated firstly, while several cracks appear on the top area due to tension (initial stage and acceleration stage). Thereafter, the upper slope body gradually slides along its respective sliding surface. The body at the bottom area of the slope was pushed by that at the upper area and slid at a high velocity along the sliding surfaces due to secondary acceleration (secondary acceleration stage). Finally, the sliding velocity of the slope gradually decreases, and an accumulation is formed, entering a stable stage (deceleration stage).

This study employed geo-electrostratigraphic and hydrogeological information to model and assess subsurface structure and hydrogeological properties within a major coastal environment in Nigeria's Niger Delta region, offering a high-resolution approach to groundwater resource management. The selection of the study area was predicated on its critical residential, agricultural, and economic significance, as well as its susceptibility to hydrogeological challenges arising from rapid urbanization and industrial activities. Unlike previous studies that utilized these methods independently, this research combined different geoelectrical technologies to enhance the accuracy of subsurface characterization. The results delineated four distinct geo-layers characterized by specific resistivity values, thicknesses, and depths, providing crucial insights into groundwater infiltration, storage potential, and contamination risks. The first geo-layer (motley topsoil) had resistivity values ranging from 95.2 to 1463.7 Qm. The second layer (sandy clay) exhibited resistivity values ranging from 8.8 to 2485.1 Qm. The third layer, identified as fine sand, exhibited resistivity values ranging from 72.5 to 1332.7 Qm. The fourth layer comprised coarse sands and it exhibited a mean resistivity of 525.98 Qm, indicating a well-drained permeable formation that could serve as an additional aquifer unit. A key innovation of this study was the quantitative assessment of hydrogeological parameters, including anisotropic coefficient, transverse resistance, longitudinal conductance, and groundwater yield potential index. The anisotropic coefficient ranged from 1.0 to 1.78 (mean: 1.17), revealing minimal sediment invasion and confirming the dominance of arenaceous sediments in the Benin Formation. The groundwater yield potential index varied from 3.14 x 102 to 8.1465 x 104 Qm2, highlighting areas of significant aquifer potential. The longitudinal conductance analysis revealed that 69 % of the study area has low aquifer protectivity, underscoring the region's vulnerability to contamination. Another novel contribution was the evaluation of soil corrosivity, which has direct implications for infrastructure longevity. Results indicate that 86 % of the study area is non-corrosive, making it suitable for long-term pipeline installation, a factor rarely integrated into groundwater assessments. The study alsoadvances understanding of the Benin Formation by linking resistivity variations to arenaceous-argillitic intercalations, and this significantly influences groundwater movement and contaminant transport. By synthesizing resistivity models, hydrogeological parameters, and contamination risk assessments, this research provides a more holistic framework for sustainable groundwater management. Furthermore, this research offers a robust framework for similar hydrogeophysical assessments in other regions with comparable geological and hydrological settings. (c) 2025 Guangzhou Institute of Geochemistry, CAS. Published by Elsevier BV. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Characterization of vegetation effect on soil response is essential for comprehending site-specific hydrological processes. Traditional research often relies on sensors or remote sensing data to examine the hydrological properties of vegetation zones, yet these methods are limited by either measurement sparsity or spatial inaccuracy. Therefore, this paper is the first to propose a data-driven approach that incorporates high-temporalresolution electrical resistivity tomography (ERT) to quantify soil hydrological response. Time-lapse ERT is deployed on a vegetated slope site in Foshan, China, during a discontinuous rainfall induced by Typhoon Haikui. A total of 97 ERT measurements were collected with an average time interval of 2.7 hours. The Gaussian Mixture Model (GMM) is applied to quantify the level of response and objectively classify impact zones based on features extracted directly from the ERT data. The resistivity-moisture content correlation is established based on on-site sensor data to characterize infiltration and evapotranspiration across wet-dry conditions. The findings are compared with the Normalized Difference Vegetation Index (NDVI), a common indicator for vegetation quantification, to reveal potential spatial errors in remote sensing data. In addition, this study provides discussions on the potential applications and future directions. This paper showcases significant spatio-temporal advantages over existing studies, providing a more detailed and accurate characterization of superficial soil hydrological response.

Electrical resistivity tests can potentially be applied in loess damage testing under combined freeze-thaw cycle (FTC) and earthquake conditions, which is crucial for preventing and controlling loess landslides. However, two challenges involving loess electrical resistivity measurements and damage characterization should be addressed. To achieve loess spatial resistivity measurements in extreme environments with low-uncertainty, a novel, multichannel, four-point method utilizing flexible electrodes is proposed. For loess damage characterization, a novel fusion algorithm is developed that integrates the electrical conductivity model with a data-driven process to eliminate the influence of moisture content and temperature on resistivity. To validate this approach, loess resistivity tests and damage characterizations were conducted using a combination of FTCs and earthquakes. The results indicate that the proposed method addresses the challenge of continuous measurement, ensuring that the discrepancy between the calculated and CT test results remains within an acceptable range, where the relative error ranged from 0 to 0.15. In addition, in the top and bottom areas, where considerable soil moisture exists, the calculation error associated with the previous empirical model was reduced considerably, with the relative error primarily ranging from 0.04 to 0.44.

To ensure the sustainable development of the surrounding environment and the sustainable operation of landfills, detecting landfill leakage is of great significance. In landfills lacking a leakage monitoring system, the inability to detect and locate damaged High-Density Polyethylene (HDPE) membranes can lead to the contamination of soil and groundwater by landfill leachate. To address this issue, this study proposes a resistivity tomography inversion model based on the external-electrode power supply mode. Utilizing the resistivity difference between the leakage zone and the surrounding soil, electrodes are arranged symmetrically for both power supply and measurement. Upon applying direct current (DC) excitation, potential data are collected, with the finite volume method employed for inversion and the Gauss-Newton method integrated with an adaptive particle swarm optimization algorithm for parameter fitting. Experimental results show that the combined algorithm provides better clarity in edge recognition of low-resistance models compared with single algorithms. The maximum deviation between inferred leakage coordinates and the actual location is 10.1 cm, while the minimum deviation is 6.4 cm, satisfying engineering requirements. This method can effectively locate point sources and line sources, providing an accurate solution for subsequent leakage point filling and improving repair efficiency.

Owing to the alternating processes of rainfall and evaporation, the compacted loess employed in ground and roadbed construction frequently experiences drying and wetting (D-W) cycles. These cycles are prone to induce substantial deformation of the soil mass, posing a risk to the integrity of buildings and infrastructure. Consequently, this study delved into the effects of D-W cycles on the deformation behavior of compacted loess, considering varying initial dry densities and water contents. To achieve a profound understanding of the deformation characteristics of the compacted loess, we meticulously monitored the resistivity ratio, crack ratio, and microstructure throughout the tests. Furthermore, a constitutive model was developed to forecast the deformation of compacted loess under D-W cycles. The findings revealed that both the vertical strain and crack ratio exhibited an upward trend with the increase in D-W cycle numbers, while they exhibited a downward trend as dry density increased. Notably, water content was identified as a significant factor affecting both the crack ratio and resistivity ratio. Additionally, the occurrence and progression of D-W cycles and cracks led to a slight increase in particle abundance and the proportion of total pore area. Meanwhile, during the wetting process, the infiltration of water softened the cementing substances, resulting in a disruption of the connections between aggregates. This made it much easier for cracks in the soil to expand after the sample dried. The constitutive model was meticulously constructed by incorporating yield surfaces that account for decreasing and increasing water contents. The validity of the proposed model was substantiated through a comparative analysis of the measured and calculated data. This comprehensive investigation furnishes a theoretical foundation for assessing the stability of compacted loess ground and roadbeds subjected to D-W cycles.

Frozen soil resistivity exhibits high sensitivity to temperature variations and ice-water distribution. The conversion of soil water content (SWC) and resistivity based on petrophysical relationships enables the characterization of spatial distribution and changes in freezing and thawing states. Monitoring ground resistivity is essential for understanding frozen soil structure and evaluating climate change and ecosystems. The previous studies demonstrate that estimating soil resistivity below zero degrees based on the empirical model has significant errors. This work proposes a capillary bundle fractal model for frozen soil resistivity estimation based on SWC hydrologic parameters. The fractal theory describes the geoelectrical features of frozen porous media through the variable pore geometry and representative elementary volume. The sensitivity analysis discusses the potential relationships between pore parameters, conductance components, and fractal geometric parameters within frozen soil resistivity and reconstructs the hysteresis separation of freeze-thaw processes. The field test application in the seasonal freeze-thaw monitoring site demonstrates that the estimated resistivity and experimental samples are consistent with the field monitoring resistivity data. By combining unified conceptual assumptions, we established the connection between electrical permeability and thermal conductivity, offering a basis for exploring coupled hydro-thermal mechanisms in frozen soil. The proposed model accurately estimates the variations in seasonal frozen resistivity, providing a reliable reference for quantitatively analyzing the mechanisms of freeze-thaw processes.