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Search Results (266)

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Keywords = soil water characteristic curve

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16 pages, 6741 KiB  
Article
Geotechnical and Hydrogeological Zonation of Tailings Storage Facilities: Importance for Design, Construction, Operation, and Closure
by Roberto Rodríguez-Pacheco, Joanna Butlanska and Aldo Onel Oliva-González
Minerals 2025, 15(2), 105; https://doi.org/10.3390/min15020105 - 22 Jan 2025
Viewed by 340
Abstract
This study introduces a conceptual model for understanding the hydromechanical behavior and zonation within tailings storage facilities (TSFs) constructed using the hydraulic backfill method, which constitutes over 98% of TSFs worldwide. The model identifies four distinct zones—dike, discharge, transition, and distal—each characterized by [...] Read more.
This study introduces a conceptual model for understanding the hydromechanical behavior and zonation within tailings storage facilities (TSFs) constructed using the hydraulic backfill method, which constitutes over 98% of TSFs worldwide. The model identifies four distinct zones—dike, discharge, transition, and distal—each characterized by unique physical, geotechnical, and hydraulic properties. Key findings highlight gradients in parameters which systematically vary from the dam toward the settling pond. This study observes that seven parameters such as grain size, friction angle, shear strength, dry density, permeability, shear wave velocities, and liquefaction capacity decrease in value from the dike to the lagoon. Conversely, thirteen parameters such as fine content, porosity, cohesion, plasticity, degree of saturation, volumetric and gravimetric water content, capillary height, specific and volumetric surface of tailings, suction, air and water entry value in the soil water characteristic curve increase in value from the dike to the lagoon. These trends underscore the complex behavior of tailings and their implications for stability, drainage, and environmental impact. By integrating geological, geotechnical, hydrogeological, and geophysical data, this study provides a holistic framework for TSF management, addressing both current challenges and long-term environmental considerations. Full article
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Graphical abstract

Graphical abstract
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<p>Example of TSF constructed by the hydraulic backfill method with cycloned tailings and downstream growth: (<b>a</b>) “Las Tortólas”, Chile (modified image from Google Earth Pro 2024); (<b>b</b>) profile of the dike area of the TSF “Las Tortólas”; (<b>c</b>) view of the main dike of the PR “Las Tortólas”; and (<b>d</b>) dike covered by a geomembrane in the TDF “Ovejería”, Chile (modified from [<a href="#B17-minerals-15-00105" class="html-bibr">17</a>]—reproduced with permission from Editor: Roberto Rodriguez-Pacheco and Open Access Journal).</p>
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<p>Conceptual model of hydromechanical operation of tailings deposit: (<b>a</b>) upstream, (<b>b</b>) downstream, (<b>c</b>) centerline, (<b>d</b>) modified centerline, and (<b>e</b>) scheme indicating the trend in behavior of physical, geotechnical, and hydraulic parameters within the deposit (t<sub>i</sub>—initial position of the dam embankment axis, t<sub>f</sub>—final position of the dam embankment axis, 1—foundation, 2—initial embankment, 3—embankment raising, 4—discharge zone with coarse sands, 5—transition zone with fine-sand silt, 6—distal zone, 7—settling pond, and 8—discharge point (inactive spigot)).</p>
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<p>Particle size distribution in the different zones in %: (<b>a</b>) dike zone (Z1); (<b>b</b>) discharge zone (Z2); (<b>c</b>) transition zone or beach (Z3); and (<b>d</b>) distal zone or lagoon (Z4).</p>
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<p>Results of the internal friction angle in the different zones of the tailings’ deposits.</p>
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<p>Results of a study with SPT tests in an abandoned TSF: (<b>a</b>) air view; distribution with depth of (<b>b</b>) SPT N-value; (<b>c</b>) friction angle; (<b>d</b>) calculated Young’s modulus; and (<b>e</b>) saturated unit weight. Adapted from internal report [<a href="#B29-minerals-15-00105" class="html-bibr">29</a>]. Black boxes refer to the sampling position.</p>
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<p>Saturated permeability test results in TSFs as a function of: (<b>a</b>) porosity; (<b>b</b>) normalized distance to the discharge point; and (<b>c</b>) distance to the discharge point (literature review Refs. [<a href="#B35-minerals-15-00105" class="html-bibr">35</a>,<a href="#B36-minerals-15-00105" class="html-bibr">36</a>,<a href="#B37-minerals-15-00105" class="html-bibr">37</a>,<a href="#B38-minerals-15-00105" class="html-bibr">38</a>]).</p>
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<p>Results of the estimation of the capillary height in the different zones of the TSFs. The bar graph represents maximum and minimum values from <a href="#minerals-15-00105-t002" class="html-table">Table 2</a>.</p>
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<p>Shear wave velocity measured at different distances from the discharge points (D1–D6 refers to six TSFs different from <a href="#minerals-15-00105-t001" class="html-table">Table 1</a> and <a href="#minerals-15-00105-t002" class="html-table">Table 2</a>).</p>
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<p>Spatiotemporal evolution of a TSF “Rio Tinto”, Spain; (<b>a</b>,<b>b</b>) stationary deposit (no extraction activity); and (<b>c</b>,<b>d</b>) active deposit (extraction resumed).</p>
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<p>Results of an electrical tomography geophysical survey of an abandoned TSF: (<b>a</b>) location of the geophysical zones and profiles; (<b>b</b>) electrical tomography profiles; and (<b>c</b>) borehole saturation profile.</p>
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<p>Characteristics of tailings in the central pond of three different TSFs (adapted from [<a href="#B8-minerals-15-00105" class="html-bibr">8</a>]). Distribution with depth: (<b>a</b>) SPT N-value; (<b>b</b>) friction angle; (<b>c</b>) Young’s Modulus; and (<b>d</b>) shear strength.</p>
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29 pages, 31883 KiB  
Article
Optimal Land Selection for Agricultural Purposes Using Hybrid Geographic Information System–Fuzzy Analytic Hierarchy Process–Geostatistical Approach in Attur Taluk, India: Synergies and Trade-Offs Among Sustainable Development Goals
by Subbarayan Sathiyamurthi, Youssef M. Youssef, Rengasamy Gobi, Arthi Ravi, Nassir Alarifi, Murugan Sivasakthi, Sivakumar Praveen Kumar, Dominika Dąbrowska and Ahmed M. Saqr
Sustainability 2025, 17(3), 809; https://doi.org/10.3390/su17030809 - 21 Jan 2025
Viewed by 554
Abstract
The precise selection of agricultural land is essential for guaranteeing global food security and sustainable development. Additionally, agricultural land suitability (AgLS) analysis is crucial for tackling issues including resource scarcity, environmental degradation, and rising food demands. This research examines the synergies and trade-offs [...] Read more.
The precise selection of agricultural land is essential for guaranteeing global food security and sustainable development. Additionally, agricultural land suitability (AgLS) analysis is crucial for tackling issues including resource scarcity, environmental degradation, and rising food demands. This research examines the synergies and trade-offs among the sustainable development goals (SDGs) using a hybrid geographic information system (GIS)–fuzzy analytic hierarchy process (FAHP)–geostatistical framework for AgLS analysis in Attur Taluk, India. The area was chosen for its varied agro-climatic conditions, riverine habitats, and agricultural importance. Accordingly, data from ten topographical, climatic, and soil physiochemical variables, such as slope, temperature, and soil texture, were obtained and analyzed to carry out the study. The geostatistical analysis demonstrated the spatial variability of soil parameters, providing essential insights into key factors in the study area. Based on the receiver operating characteristic curve analysis, the results showed that the FAHP method (AUC = 0.71) outperformed the equal-weighting scheme (AUC = 0.602). Moreover, suitability mapping designated 17.31% of the study area as highly suitable (S1), 41.32% as moderately suitable (S2), and 7.82% as permanently unsuitable (N2). The research identified reinforcing and conflicting correlations with SDGs, emphasizing the need for policies to address trade-offs. The findings showed 40% alignment to climate action (SDG 13) via improved resilience, 33% to clean water (SDG 6) by identifying low-salinity zones, and 50% to zero hunger (SDG 2) through sustainable food systems. Conflicts arose with SDG 13 (20%) due to reliance on rain-fed agriculture, SDG 15 (11%) from soil degradation, and SDG 2 (13%) due to inefficiencies in low-productivity zones. A sustainable action plan (SAP) can tackle these issues by promoting drought-resistant crops, nutrient management, and participatory land-use planning. This study can provide a replicable framework for integrating agriculture with global sustainability objectives worldwide. Full article
(This article belongs to the Special Issue GIS Implementation in Sustainable Urban Planning)
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<p>(<b>a</b>) Geographic location of the study area in South Asia; (<b>b</b>) Administrative map of Tamil Nadu District in southern India; (<b>c</b>) Detailed map of Attur Taluk, indicating soil sampling sites and the current distribution of healthy vegetation lands used as inventory data.</p>
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<p>Methodology flowchart of the research study in Attur Taluk, India.</p>
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<p>Spatial variability map of topographical factors of Attur Taluk, India: (<b>a</b>) elevation and (<b>b</b>) slope (SL).</p>
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<p>Spatial variability map of climatic factors: (<b>a</b>) minimum annual temperature, (<b>b</b>) maximum annual temperature, and (<b>c</b>) average annual rainfall (AAR).</p>
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<p>Spatial variability map of soil physical characteristics in the study area: (<b>a</b>) sand (%), (<b>b</b>) silt (%), and (<b>c</b>) clay (%).</p>
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<p>Spatial variability map of soil chemical factors of Attur Taluk, India: (<b>a</b>) potential of hydrogen (pH), (<b>b</b>) electrical conductivity (EC), (<b>c</b>) organic carbon (OC), (<b>d</b>) available nitrogen (AN), (<b>e</b>) available phosphorus (AP), and (<b>f</b>) available potassium (AK).</p>
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<p>Ten thematic rated factors of agricultural land suitability (AgLS) of Attur Taluk, India: (<b>a</b>) slope (SL), (<b>b</b>) average annual temperature (AAT), (<b>c</b>) average annual rainfall (AAR), (<b>d</b>) soil texture (ST), (<b>e</b>) potential of hydrogen (pH), (<b>f</b>) electrical conductivity (EC), (<b>g</b>) available organic carbon (OC), (<b>h</b>) available nitrogen (AN), (<b>i</b>) available phosphorus (AP), and (<b>j</b>) available potassium (AK).</p>
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<p>Final agriculture land suitability (AgLS) maps of Attur Taluk, India: (<b>a</b>) the equal-weighted (AgLS<sub>Eq</sub>) map and (<b>b</b>) the fuzzy analytical hierarchy process (FAHP)-weighted (AgLS<sub>FAHP</sub>) map.</p>
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<p>Area under the curve (AUC) of receiver operating characteristic (ROC) plot for the validation of the agricultural land suitability (AgLS) maps of Attur Taluk, India, using different models.</p>
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<p>Validation of agricultural land suitability (AgLS) maps of Attur Taluk, India, using different approaches.</p>
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<p>Quantitative correlation of synergies/trade-offs between the study outcomes of land suitability in Attur Taluk, India, and sustainable development goals (SDGs).</p>
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<p>Sustainable action plan (SAP) along with enhancement percentages to mitigate conflicting linkages to sustainable development goals (SDGs) in Attur Taluk, India.</p>
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17 pages, 8585 KiB  
Article
Investigation of the Water Retention Characteristics and Mechanisms of Organic Clay
by Zeyu Song, Yue Gui, Lun Hua, Shisong Yuan and Ruisheng Hu
Water 2025, 17(3), 286; https://doi.org/10.3390/w17030286 - 21 Jan 2025
Viewed by 404
Abstract
With the acceleration of urbanization, clay with significant variations in organic matter content is commonly encountered in infrastructure construction. Its unique water retention capacity is crucial for engineering safety and stability. This study uses red clay as the matrix and incorporates peat to [...] Read more.
With the acceleration of urbanization, clay with significant variations in organic matter content is commonly encountered in infrastructure construction. Its unique water retention capacity is crucial for engineering safety and stability. This study uses red clay as the matrix and incorporates peat to prepare soil samples with varying organic matter content. Soil–water characteristic tests were conducted using the pressure plate method, filter paper method, and vapor equilibrium method to obtain the soil–water characteristic curves across the entire suction range. Subsequently, scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) tests were performed to analyze the mechanisms underlying the water retention characteristics. The experimental results indicate that the three different suction tests accurately reflect the soil–water characteristic curves of organic clay across the entire suction range. As the organic matter content in the soil increases, the air entry value and residual value of the soil samples exhibit a linear relationship with the organic matter content, enhancing the soil’s water retention capacity. The increase in organic matter content alters the microstructure of the clay, transforming the mineral–organic aggregates from ellipsoidal to plate-like shapes. While organic matter can influence the water retention of clay, within a certain suction range, the water retention capacity of organic clay is also related to the pore structure and the state of water within the pores. This is crucial for ensuring engineering safety and optimizing design solutions. Full article
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<p>Experimental soil samples.</p>
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<p>XRD pattern.</p>
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<p>Soil–water characteristic curves of organic clay by pressure plate method.</p>
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<p>Soil–water characteristic curves of organic clay by filter paper method.</p>
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<p>Soil–water characteristic curves of organic clay by vapor equilibrium method.</p>
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<p>Soil–water characteristic curves across the full suction range.</p>
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<p>Microscopic structure of soil under scanning electron microscopy.</p>
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<p>Microscopic structure of soil under scanning electron microscopy.</p>
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<p>Curves of pore size distribution and cumulative pore volume from mercury intrusion tests.</p>
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<p>Curves of pore size distribution and cumulative pore volume from mercury intrusion tests.</p>
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<p>Schematic representation of the change in pore water with increasing suction in soil containing organic matter.</p>
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15 pages, 705 KiB  
Article
Characterization of the Pore Network of a Cohesive Oxisol Through Morphological and Pore Complexity Analyses
by Jocenei A. T. de Oliveira, Thaís N. Pessoa, José V. Gaspareto, Adolfo N. D. Posadas, André L. F. Lourenço, Paulo L. Libardi and Luiz F. Pires
Agriculture 2025, 15(2), 200; https://doi.org/10.3390/agriculture15020200 - 17 Jan 2025
Viewed by 381
Abstract
Cohesive Oxisols are a type of soil common in the Coastal Plateau in Brazil. These soils represent a challenge for agriculture and their study is fundamental to better land use. There have been a few studies on the porous system of cohesive soils [...] Read more.
Cohesive Oxisols are a type of soil common in the Coastal Plateau in Brazil. These soils represent a challenge for agriculture and their study is fundamental to better land use. There have been a few studies on the porous system of cohesive soils on the micrometer scale. Our study aimed to provide a detailed analysis of the pore complexity of the cohesive horizon of a Brazilian Oxisol using 3D images (volumetric data reconstructed by 2D CT slices) and to correlate these parameters with soil physical–hydric attributes. For this purpose, images with two different resolutions were analyzed from multifractal, lacunarity, and entropy analyses. Additionally, a characterization of hydraulic properties was carried out based on a soil water retention curve (SWRC). No differences were observed between the resolutions for the different physical parameters analyzed. The lacunarity analysis showed a greater homogeneity of the pore system with pores grouped in clusters. The multifractal analysis showed fractal characteristics for the cohesive horizon, suggesting a more homogeneous pore distribution. The main results obtained from the SWRC showed a low available water content due to the predominance of ultramicropores. Overall, the results show a less complex pore system, indicating the presence of pores of small sizes, affecting the water retention and conduction through the soil. Full article
(This article belongs to the Section Agricultural Soils)
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<p>(<b>a</b>) 3D lacunarities and (<b>b</b>) their derivatives for two different resolutions.</p>
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<p>Cohesive soil pore network at two different resolutions. (<b>a</b>) S_R1 = 9 µm. (<b>b</b>) S_R2 = 15 µm.</p>
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<p>Multifractal spectra at two different resolutions representing the replicates: (<b>a</b>) sample 1 with a lower resolution and (<b>b</b>) sample 2 with a higher resolution. Both samples exhibit homogeneous pore size distributions concentrated in a cluster pattern.</p>
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<p>The 3D normalized Shannon’s entropies of two different resolutions.</p>
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<p>(<b>a</b>) Soil water retention curves, (<b>b</b>) pore size classes, (<b>c</b>) hydraulic conductivity, and (<b>d</b>) field capacity (FC), available water content (AWC), and permanent wilting point (PWP).</p>
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<p>(<b>a</b>) Location of the state and (<b>b</b>) city on the map of Brazil. (<b>c</b>) A visual representation of the city where the soil samples were collected. (<b>d</b>) Cohesive soil profile trench. (<b>e</b>) Cohesive soil block of the sampled profile. (<b>f</b>) Subsamples of approximately cubic soil samples of different sizes.</p>
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20 pages, 3542 KiB  
Article
Geotechnical Properties of Urmia Saltwater Lake Bed Sediments
by Davood Akbarimehr, Mohammad Rahai, Majid Ahmadpour and Yong Sheng
Geotechnics 2025, 5(1), 1; https://doi.org/10.3390/geotechnics5010001 - 31 Dec 2024
Viewed by 457
Abstract
Urmia Lake (UL) is the sixth-largest saltwater lake in the world; however, there is a dearth of geotechnical studies on this region. Geotechnical characteristics of a site are considered important from different engineering perspectives. In this research, the results of 255 laboratory tests [...] Read more.
Urmia Lake (UL) is the sixth-largest saltwater lake in the world; however, there is a dearth of geotechnical studies on this region. Geotechnical characteristics of a site are considered important from different engineering perspectives. In this research, the results of 255 laboratory tests and the data of 55 in situ tests were used to determine the geotechnical properties of sediment in UL. The changes of parameters in depth are presented in this study. The results indicate that compressibility, initial void ratio, water content, over-consolidated ratio (OCR), and sensitivity have larger values near the lake bed. Moreover, increasing the sediment depth leads to significant reductions in these values. According to the sediment strength analysis through the vane shear and standard penetration tests and the unit weight of sediments, there is an increasing trend caused by the increased depths of layers. Diverse applied correlations are proposed and can be used as preliminary estimates in similar types of sediments in engineering projects as well as scientific studies. Furthermore, undrained shear strength and compression index trends in depth and the Su/σ’v Curve against OCR are compared with the literature, and the results reveal similar trends in similar sediments. The main minerals identified in these sediments include calcite, dolomite, quartz, calcium chloride, and halite. The salinity of the lake water is caused by the presence of calcium chloride and halite minerals. The Cao factor observed in chemical compounds can have a significant impact on the cohesion of the soil particles. This research provides comprehensive information on the geotechnical characteristics of UL. Moreover, the results of this study show that UL Sediments are soft and sensitive, especially in shallow depths, and they contain a significant amount of organic content; therefore, it is recommended to use suitable improvement methods in future geotechnical and structural designs. This study and similar surveys can help prepare the groundwork for designing safer marine structures. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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<p>Location of UL on the map of Iran and images of UL (<b>a</b>) Iran map, (<b>b</b>) UL, and (<b>c</b>) UL causeway.</p>
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<p>XRD analysis of a sample of Urmia soft sediment.</p>
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<p>SEM analysis of a sample of Urmia soft sediment: (<b>a</b>) 10 K×, (<b>b</b>) 2.5 K×.</p>
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<p>Sediments (<b>a</b>) layering, (<b>b</b>) classification, and (<b>c</b>) a sample of sediments.</p>
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<p>CPTu profile.</p>
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<p>CPTu classification of sediments.</p>
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<p>Variation of geotechnical properties in depth: (<b>a</b>) Wn, (<b>b</b>) Atterberg limits, (<b>c</b>) wet unit weight, (<b>d</b>) undrained shear strength, (<b>e</b>) sensitivity, and (<b>f</b>) Nspt.</p>
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<p>Variations of parameters in depth: (<b>a</b>) Cc, and (<b>b</b>) OCR.</p>
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<p>Comparison with the literature: (<b>a</b>) variation of Cc against W, (<b>b</b>) changes of undrained shear strength against depth, and (<b>c</b>) curves of Su/σ’<sub>v</sub> with respect to OCR [<a href="#B53-geotechnics-05-00001" class="html-bibr">53</a>,<a href="#B56-geotechnics-05-00001" class="html-bibr">56</a>,<a href="#B57-geotechnics-05-00001" class="html-bibr">57</a>,<a href="#B58-geotechnics-05-00001" class="html-bibr">58</a>].</p>
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12 pages, 4139 KiB  
Article
Temperature Effect on Stability of Tunnel Face Under Unsaturated Seepage Condition
by Yi Xie, Hong Liao and De Zhou
Appl. Sci. 2025, 15(1), 298; https://doi.org/10.3390/app15010298 - 31 Dec 2024
Viewed by 483
Abstract
As tunnel excavation technology matures and the demand for transportation infrastructure continues to grow, several high-temperature tunnels have successively emerged in high geothermal areas. The construction of tunnels in high-temperature regions is gradually becoming a new challenge encountered in the engineering field. This [...] Read more.
As tunnel excavation technology matures and the demand for transportation infrastructure continues to grow, several high-temperature tunnels have successively emerged in high geothermal areas. The construction of tunnels in high-temperature regions is gradually becoming a new challenge encountered in the engineering field. This study aims to conduct a stability analysis of tunnel face excavation under different temperatures. In addition, soil is often considered to be unsaturated. A framework for assessing the stability of tunnel faces in unsaturated soils under fluctuating temperature conditions is proposed, with an analytical approach. The theoretical basis of this framework is established on the influence of temperature on the shear strength of unsaturated soil. The matric suction of unsaturated soil changes with temperature, thereby inducing variations in shear strength. The temperature-induced variation in apparent cohesion is quantified utilizing a temperature-sensitive effective stress model coupled with a soil–water characteristic curve. These models are subsequently incorporated into the stability assessment of tunnel faces in unsaturated soils under steady-state flow conditions. A three-dimensional logarithmic spiral model is utilized to ascertain the unsupported pressure on tunnel faces, with the safety factor (FS) being calculated through an iterative methodology. Subsequently, a comprehensive suite of parametric studies is undertaken to explore the influence of temperature on tunnel face stability under unsaturated seepage conditions, offering valuable insights for practical engineering endeavors. Full article
(This article belongs to the Special Issue Slope Stability and Earth Retaining Structures—2nd Edition)
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<p>Schematic of the failure mechanism.</p>
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<p>Iterative flow chart.</p>
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<p>FS versus 1/<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> for different <math display="inline"><semantics> <mrow> <mi mathvariant="normal">n</mi> </mrow> </semantics></math>, with <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">φ</mi> <mo>=</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">q</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">γ</mi> <mo>=</mo> <mn>20</mn> <mo> </mo> <mi mathvariant="normal">k</mi> <mi mathvariant="normal">N</mi> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">C</mi> <mo>=</mo> <mn>20</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>5</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">C</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>FS versus temperature for different soil types and seepage conditions, with <math display="inline"><semantics> <mrow> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">C</mi> <mo>=</mo> <mn>20</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">q</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">q</mi> <mo>=</mo> <mn>1.15</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">q</mi> <mo>=</mo> <mo>−</mo> <mn>3.14</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>.</p>
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<p>FS versus temperature for different <math display="inline"><semantics> <mrow> <mi mathvariant="normal">q</mi> </mrow> </semantics></math> and temperature, with <math display="inline"><semantics> <mrow> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">C</mi> <mo>=</mo> <mn>20</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mo>=</mo> <mn>10</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mo>=</mo> <mn>25</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mo>=</mo> <mn>40</mn> <mo> </mo> <mo>°</mo> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>.</p>
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15 pages, 5533 KiB  
Article
Measurement of Soil–Water Characteristic Curve of Vegetative Soil Using Polymer-Based Tensiometer for Maintaining Environmental Sustainability
by Widjojo Adi Prakoso, Abdul Halim Hamdany, Martin Wijaya, Rabbani Isya Ramadhan, Aldo Wirastana Adinegara, Alfrendo Satyanaga, Glenn Adriel Adiguna and Jong Kim
Sustainability 2025, 17(1), 218; https://doi.org/10.3390/su17010218 - 31 Dec 2024
Viewed by 577
Abstract
The interaction between moisture content and soil suction is commonly represented by a soil–water characteristic curve (SWCC). The direct measurement of water content can be easily achieved, but it usually requires a destructive method where the soil sample needs to be oven-dried. Hence, [...] Read more.
The interaction between moisture content and soil suction is commonly represented by a soil–water characteristic curve (SWCC). The direct measurement of water content can be easily achieved, but it usually requires a destructive method where the soil sample needs to be oven-dried. Hence, indirect measurement is commonly employed for monitoring purposes. The limitation of this approach is the variability in water content at the wilting point, particularly for plants in different types of soil. While the moisture content at the wilting point varies greatly, suction at the wilting point is typically around 1500 kPa despite varying slightly depending on the type of plant. However, suction measurement using a normal tensiometer is limited to 100 kPa due to cavitation. Hence, it is not sufficient to cover up to the wilting point. The focus of this paper is the establishment of a polymer-based tensiometer utilizing a 15 bar ceramic disc for the measurement of high suction. The suitability of the polymer-based tensiometer in measuring the soil suction of vegetative soil is conducted by performing a soil–water characteristic curve test on vegetative soil. The SWCC produced from the polymer-based tensiometer is verified using SWCC produced from a centrifuge test. The results show that the SWCCs from both polymer-based tensiometer and centrifuge tests are comparable. Hence, suction measurement using a polymer-based tensiometer is deemed reliable. This advancement in suction measurement technology is crucial for improving irrigation practices, optimizing water use, and enhancing agricultural productivity, which in turn contributes to environmental sustainability by minimizing water waste and ensuring efficient soil management. Full article
(This article belongs to the Section Soil Conservation and Sustainability)
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<p>Vegetative soil specimen. (<b>a</b>) Original soil. (<b>b</b>) Reconstituted soil.</p>
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<p>Compaction curve of vegetative soil.</p>
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<p>Grain size distribution of vegetative soil.</p>
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<p>PBT design modified from Hamdany et al. (2022) [<a href="#B15-sustainability-17-00218" class="html-bibr">15</a>].</p>
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<p>Synthesis process of PAM polymer.</p>
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<p>SWCC test by using PBT.</p>
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<p>Specimen for centrifuge test.</p>
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<p>Change in polymer pressure and temperature over time.</p>
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<p>Temperature correction.</p>
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<p>Evaporation test results.</p>
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<p>Maximum polymer pressure.</p>
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<p>Shrinkage curve of vegetative soil [<a href="#B30-sustainability-17-00218" class="html-bibr">30</a>].</p>
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<p>Monitoring of soil suction and volumetric water content for 20 days.</p>
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<p>SWCC-w of vegetative soil [<a href="#B29-sustainability-17-00218" class="html-bibr">29</a>].</p>
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<p>SWCC-θ<sub>w</sub> of vegetative soil [<a href="#B29-sustainability-17-00218" class="html-bibr">29</a>].</p>
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20 pages, 3240 KiB  
Article
Modeling and Application of the Hydrus-2D Model for Simulating Preferential Flow in Loess Soil Under Various Scenarios
by Shengnan Li, Ting Lu, Kexin Zhou, Yidong Gu, Bihui Wang and Yudong Lu
Water 2024, 16(24), 3653; https://doi.org/10.3390/w16243653 - 18 Dec 2024
Viewed by 538
Abstract
Soil hydraulic properties are mainly governed by the soil’s heterogeneity, anisotropy, and discontinuous structural characteristics, primarily when connected soil macropores characterize the structures. Therefore, researchers must document reliable hydrological models to elucidate how the soil medium affects the movement of soil water. This [...] Read more.
Soil hydraulic properties are mainly governed by the soil’s heterogeneity, anisotropy, and discontinuous structural characteristics, primarily when connected soil macropores characterize the structures. Therefore, researchers must document reliable hydrological models to elucidate how the soil medium affects the movement of soil water. This study, utilizing a field-scale staining tracer test, distinguishes between matrix flow and preferential flow areas in the seepage field of Xi’an loess. The Xi’an loess’s soil water characteristic curve (SWCC) was explored through field investigations and laboratory analyses. A dual-permeability model that couples matrix and macropore flow was developed using the Hydrus-2D model, enabling simulations of water migration under varying initial soil water content, rainfall intensity, and crack width. The results showed that (1) The SWCC of macropores in the preferential flow area exhibits a bimodal distribution, and the Fredlund & Xing model is applied for sectional fitting to obtain the corresponding soil water characteristic parameters. (2) Initial soil water content and rainfall intensity significantly influence water distribution, while crack width has a relatively minor effect. (3) The cumulative flux under the preferential flow is significantly higher than in the matrix area, and the wetting front depth increases with higher initial water content and rainfall intensity. This study reveals the key characteristics of preferential flow and moisture migration in the matrix zone and their influencing factors in loess. It constructs a two-domain infiltration model by integrating loess’s diverse structural characteristics and pore morphology. This model provides a theoretical basis and technical support for simulating preferential flow and studying the moisture dynamics of loess profiles. Full article
(This article belongs to the Special Issue Advance in Groundwater in Arid Areas)
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<p>Dye tracer test of preferential flow of loess profile.</p>
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<p>Hydrogeological conceptual model of loess profile.</p>
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<p>Distribution of water content in profiles at different times: (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.1 cm<sup>3</sup>/cm<sup>3</sup>; (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.15 cm<sup>3</sup>/cm<sup>3</sup>; (<b>c</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.2 cm<sup>3</sup>/cm<sup>3</sup>; (<b>d</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 cm<sup>3</sup>/cm<sup>3</sup>.</p>
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<p>Distribution of water content in profiles at different times: (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.1 cm<sup>3</sup>/cm<sup>3</sup>; (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.15 cm<sup>3</sup>/cm<sup>3</sup>; (<b>c</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.2 cm<sup>3</sup>/cm<sup>3</sup>; (<b>d</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 cm<sup>3</sup>/cm<sup>3</sup>.</p>
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<p>Water content distribution with different rainfall in the profiles: (<b>a</b>) <span class="html-italic">P</span> = 0.5 cm/h <sup>3</sup>; (<b>b</b>) <span class="html-italic">P</span> = 1 cm/h; (<b>c</b>) <span class="html-italic">P</span> = 1.5 cm/h; (<b>d</b>) <span class="html-italic">P</span> = 2 cm/h.</p>
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<p>Water content distribution with different crack widths in the profiles: (<b>a</b>) <span class="html-italic">L</span> = 0.5 cm; (<b>b</b>) <span class="html-italic">L</span> = 1 cm; (<b>c</b>) <span class="html-italic">L</span> = 1.5 cm; (<b>d</b>) <span class="html-italic">L</span> = 2 cm.</p>
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<p>Variation of water content at observation points under different scenarios: (<b>a</b>) Scenario A, (<b>b</b>) Scenario B, and (<b>c</b>) Scenario C.</p>
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<p>Cumulative flux variation at observation points in different scenarios: (<b>a</b>) Scenario A, (<b>b</b>) Scenario B, and (<b>c</b>) Scenario C.</p>
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20 pages, 6879 KiB  
Article
Exploring the Hydraulic Properties of Unsaturated Soil Using Deep Learning and Digital Imaging Measurement
by Yanni Huang and Zhoujie Wang
Water 2024, 16(24), 3550; https://doi.org/10.3390/w16243550 - 10 Dec 2024
Viewed by 569
Abstract
This work aims to improve the accuracy of traditional models for analyzing the hydraulic properties of unsaturated soil by integrating digital imaging measurement with deep learning techniques. The work first reviews current research on the basic characteristics of unsaturated soil and the applications [...] Read more.
This work aims to improve the accuracy of traditional models for analyzing the hydraulic properties of unsaturated soil by integrating digital imaging measurement with deep learning techniques. The work first reviews current research on the basic characteristics of unsaturated soil and the applications of deep learning in this field. Next, it examines the impact of soil specimens’ physical properties on their hydraulic properties. This includes acquiring hydraulic parameters and the soil-water characteristic curve through full-surface digital imaging measurements. Finally, a soil hydraulic property model based on the backpropagation neural network (BPNN) is implemented, trained, and validated. Results indicate that the model’s predicted soil-water characteristic curve aligns closely with the experimental findings from previous studies. Moreover, the proposed BPNN-based unsaturated soil hydraulic property model uses the Levenberg–Marquardt algorithm, which reduces computational time and noise compared to alternative algorithms. Meanwhile, analysis of the model parameters suggests that ten neurons in the hidden layer provide optimal performance. By incorporating correlations between physical parameters, such as soil particle size and soil hydraulic properties, the model demonstrates lower error rates compared to other literature models. Overall, this BPNN model effectively represents the relationship between soil’s physical and hydraulic parameters, streamlining traditional soil correlation coefficient estimation. Full article
(This article belongs to the Special Issue Soil Erosion and Soil and Water Conservation)
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<p>SEM images of various soil types.</p>
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<p>Morphological differences between saturated and unsaturated soils.</p>
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<p>Illustration of a BPNN.</p>
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<p>Particle grading distribution curve of the specimen.</p>
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<p>SWCC curve of the specimen.</p>
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<p>Fitting curve of sand specimens.</p>
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<p>Fitting curve of the silty loam specimen.</p>
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<p>Fitting curve of sandy loam specimen.</p>
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<p>Fitting curve of clay specimen.</p>
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<p>Van Genuchten model’s linear regression analysis results regarding R<sup>2</sup>.</p>
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<p>Results of RSS for linear regression analysis of Van Genuchten model.</p>
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<p>Correlation coefficient results for models with different numbers of hidden-layer neurons.</p>
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<p>MCC of Van Genuchten model parameters during training.</p>
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<p>MSE of Van Genuchten model parameters during training.</p>
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14 pages, 11343 KiB  
Article
Study of the Shear Strength Model of Unsaturated Soil in the Benggang Area of Southern China
by Maojin Yang, Nanbo Cen, Zumei Wang, Bifei Huang, Jinshi Lin, Fangshi Jiang, Yanhe Huang and Yue Zhang
Water 2024, 16(23), 3528; https://doi.org/10.3390/w16233528 - 7 Dec 2024
Viewed by 885
Abstract
Benggangs are a unique type of soil erosion commonly found in southern China, with the gully wall being the most dynamic component of the Benggang system and is crucial for assessing its overall progression. The unsaturated shear strength of soil in Benggang areas [...] Read more.
Benggangs are a unique type of soil erosion commonly found in southern China, with the gully wall being the most dynamic component of the Benggang system and is crucial for assessing its overall progression. The unsaturated shear strength of soil in Benggang areas is a key factor influencing the stability of the gully wall. However, quantitative analyses of the unsaturated shear strength in the gully walls of Benggangs remain limited. In this study, the soil–water characteristic curves (SWCC) and shear strengths of undisturbed soil samples from four different soil layers in the gully wall of Benggang were measured using a pressure membrane meter and a quadruple direct shear apparatus. The results revealed that the water holding capacity of the soil decreased gradually with increasing matrix suction, and the order of the water holding capacity was the sandy soil layer > transition layer > laterite layer > clastic layer. With an increasing soil water content (SWC), the shear strength, cohesion (c), and internal friction angle (φ) of the four soil layers decreased significantly, and the φ showed a power function decreasing curve (p < 0.05), whereas c in the laterite layer and transition layer exhibited a power function decreasing curve (p < 0.01). The c of the sandy soil layer and clastic layer decreased linearly and logarithmically (p < 0.01) with increasing SWC, respectively. The unsaturated shear strength model for the four soil layers was developed based on the Vanapalli model. The root mean square error (RMSE) of the simulated and measured values was less than 29.349, while the Nash–Sutcliffe efficiency (NSE) and R2 values were greater than 0.638 and 0.788, respectively. The model can be used to analyze and predict the unsaturated shear strength in different layers of Benggang gully walls, providing a theoretical foundation for studying the erosion mechanisms of Benggangs. Full article
(This article belongs to the Section Soil and Water)
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<p>The related map of the study area includes (<b>a</b>) a map of the locations of sampling points; (<b>b</b>) a drone image of the Benggang; and (<b>c</b>) a simplified diagram showing the division of soil layers at the sampling point.</p>
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<p>SWCC test equipment.</p>
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<p>Samples from quadruple direct shear and standard circular ring cutter 1.</p>
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<p>SWCC of different soil layers of the Benggang gully wall.</p>
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<p>Relationships between the soil shear strength and normal stress in the various soil layers of the Benggang gully wall under different SWC conditions. Note: (<b>a</b>–<b>d</b>) denote the laterite layer, transition layer, sandy soil layer, and clastic layer, respectively.* indicates a significant correlation (<span class="html-italic">p</span> &lt; 0.05); ** indicates a highly significant correlation (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Fitted curves of the SWC versus <span class="html-italic">c</span> (<b>A</b>) and angle of <span class="html-italic">φ</span> (<b>B</b>) for different soil layers of the Benggang gully wall. Note: a–d denote the laterite layer, transition layer, sandy soil layer, and clastic layer, respectively. * indicates a significant correlation (<span class="html-italic">p</span> &lt; 0.05); ** indicates a highly significant correlation (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Comparison of the measured values from this experiment with the values predicted by Vanapalli’s (1996) [<a href="#B23-water-16-03528" class="html-bibr">23</a>] model. Note: (<b>a</b>–<b>d</b>) denote the laterite layer, transition layer, sandy soil layer, and clastic layer, respectively.</p>
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11 pages, 1311 KiB  
Article
Influence of Annual Ryegrass (Lolium multiflorum) as Cover Crop on Soil Water Dynamics in Fragipan Soils of Southern Illinois, USA
by Amitava Chatterjee, Dana L. Dinnes, Daniel C. Olk and Peter L. O’Brien
Soil Syst. 2024, 8(4), 126; https://doi.org/10.3390/soilsystems8040126 - 3 Dec 2024
Viewed by 713
Abstract
Fragipans are dense subsurface soil layers that severely restrict root penetration and water movement. The presence of shallow fragipan horizons limits row crop production. We hypothesized that the roots of cover crop might improve soil physiochemical properties and biological activity, facilitating drainage and [...] Read more.
Fragipans are dense subsurface soil layers that severely restrict root penetration and water movement. The presence of shallow fragipan horizons limits row crop production. We hypothesized that the roots of cover crop might improve soil physiochemical properties and biological activity, facilitating drainage and increasing effective soil depth for greater long-term soil water storage. To evaluate annual ryegrass as one component of a cover crop (CC) mix for promoting the characteristics and distribution of soil water, on-farm studies were conducted at Marion and Springerton in southern Illinois, USA. Soil samples were collected at 15 cm increments to 60 cm (Marion) and 90 cm (Springerton) depths during the fall of 2022. Both sites had low total soil carbon and nitrogen contents and acidic soil pH (≤6.4). A soil water retention curve was fitted using the van Genuchten equation. At Springerton, the CC treatment increased saturated (thetaS) and residual (thetaR) soil water contents above those of the no cover crop (NCC) at the 60–75 cm and 75–90 cm depths. Changes in volumetric soil water content were measured using a multi-depth soil water sensor for the Springerton site during late July to early August of the soybean growing phase of 2022; NCC had higher soil water than CC within the 0–15 cm depth, but CC had higher soil water than NCC at the 30–45 cm depth. These findings indicate that cover crop mix has the potential to improve soil water movement for soils with restrictive subsoil horizon, possibly through reducing the soil hydraulic gradient between the surface and restrictive subsurface soil layers. Full article
(This article belongs to the Special Issue Land Use and Management on Soil Properties and Processes)
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<p>(<b>a</b>) Location of two growers’ fields, Marion and Springerton in southern Illinois, (<b>b</b>) schematic diagram of soil profile of two dominant soil series, ‘Bluford’ and ‘Rend’ found at Springerton and Marion and sites, respectively, and (<b>c</b>) daily high and low air temperatures (°C) and precipitation (cm) during 2022 growing season in Carbondale, Illinois.</p>
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<p>(<b>a</b>) Changes in volumetric soil moisture content (cm<sup>3</sup> cm<sup>−3</sup>), (<b>b</b>) comparison of drydown time (days), and (<b>c</b>) changes in soil water storage (cm) with (CC) and without (NCC) annual ryegrass as cover crop during 2022 (soybean growing phase) at Springerton, Illinois.</p>
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17 pages, 2887 KiB  
Article
Assessing Roles of Aggregate Structure on Hydraulic Properties of Saline/Sodic Soils in Coastal Reclaimed Areas
by Yuanhang Fei, Dongli She, Shengqiang Tang, Hongde Wang, Xiaoqin Sun, Xiao Han and Dongdong Liu
Agronomy 2024, 14(12), 2877; https://doi.org/10.3390/agronomy14122877 - 3 Dec 2024
Viewed by 556
Abstract
During coastal reclamation processes, land use conversion from natural coastal saline/sodic soils to agricultural land changes the soil’s physicochemical properties. However, the impact of soil structure evolution on soil hydraulic properties (SHPs, e.g., hydraulic conductivity and soil water retention curves) during long-term reclamation [...] Read more.
During coastal reclamation processes, land use conversion from natural coastal saline/sodic soils to agricultural land changes the soil’s physicochemical properties. However, the impact of soil structure evolution on soil hydraulic properties (SHPs, e.g., hydraulic conductivity and soil water retention curves) during long-term reclamation has rarely been reported. In this study, we aimed to evaluate the effect of reclamation duration and land use types on the soil aggregate stability and SHPs of coastal saline/sodic soils and incorporate the aggregate structures into the SHPs. In this study, a total of 90 soil samples from various reclaimed years (2007, 1960, and 1940) and land use patterns (cropland, grassland, forestland, and wasteland) were taken to analyze the quantitative effects of soil saline/sodic characteristics and the aggregate structure on SHPs through pedotransfer functions (PTFs). We found that soil macroaggregate contents in the old reclaimed areas (reclaimed in 1940 and 1960) were significantly larger than those in the new reclamation area (reclaimed in 2007). The soil saturated hydraulic conductivity (Ks) of forestland was larger than that of grassland in each reclamation year. Soil structure contributed to 22.13%, 24.52%, and 23.93% of the total variation in Ks and soil water retention parameters (α and n). The PTFs established in our study were as follows: log(Ks) = 0.524 − 0.177 × Yk3 − 0.093 × Yk1 + 0.135 × Yk4 − 0.054 × Yk2, 1/α = 477.244 − 91.732 × Yα2 − 81.283 × Yα4 + 38.106 × Yα3, and n = 1.679 − 0.086 × Yn2 + 0.045 × Yn1 − 0.042 × Yn3 (Y are principal components). The mean relative errors of the prediction models for log(Ks), 1/α, and n were 79.30%, 36.1%, and 9.89%, respectively. Our findings quantify the vital roles of the aggregate structure on the SHPs of coastal saline/sodic soils, which will help us understand related hydrological processes. Full article
(This article belongs to the Special Issue Soil Evolution, Management, and Sustainable Utilization)
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<p>Geographical location of (<b>a</b>) study area in Rudong County, Nantong City of China; (<b>b</b>) soil sampling points in three reclamation areas.</p>
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<p>Distribution of soil water stable aggregate content at (<b>a</b>) 0–20 cm, (<b>b</b>) 20–40 cm, and (<b>c</b>) 40–60 cm. The cropland within the reclamation area in 2007 was wheat land. In 1960, the cropland within the reclamation area was broad bean land, and in 1940, it was wheat land. Different lowercase letters indicate that there was a significant difference between different reclamation durations under the same land use type (<span class="html-italic">p</span> &lt; 0.05); different capital letters indicate that there was a significant difference between different land use types under the same reclamation duration (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Eigenvalues and the variance contribution rate of soil <span class="html-italic">K</span><sub>s</sub>, <span class="html-italic">a</span>, and <span class="html-italic">n</span> based on principal component analysis. PC1: the first principal component; PC2: the second principal component; PC3: the third principal component; PC4: the fourth principal component.</p>
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<p>Factor load matrix of (<b>a</b>) soil <span class="html-italic">K</span><sub>s,</sub> (<b>b</b>) 1/<span class="html-italic">α</span>, and (<b>c</b>) <span class="html-italic">n</span> and principal component load matrix of (<b>d</b>) soil <span class="html-italic">K</span><sub>s</sub>, (<b>e</b>) 1/<span class="html-italic">α</span>, and (<b>f</b>) <span class="html-italic">n</span>. Note: <span class="html-italic">K</span><sub>s</sub>, saturated hydraulic conductivity; BD, bulk density; MWD, average weight diameter of soil water stable aggregates; CEC, cation exchange capacity; SOM, soil organic matter; ESP, exchangeable sodium percentage; SAR, sodium adsorption ratio, saturated moisture content; <span class="html-italic">α</span> and <span class="html-italic">n</span>, van Genuchten–Mualem parameters.</p>
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<p>Correlations of the soil variables with the two main principal components of (<b>a</b>) <span class="html-italic">K</span><sub>s</sub>, (<b>b</b>) <span class="html-italic">α</span>, and (<b>c</b>) <span class="html-italic">n</span> after varimax rotation. Note: Orange points, soil hydraulic properties; yellow points, soil saline–alkali properties; light blue points, soil texture; dark blue points, soil nutrients; green points, soil structure; purple points, soil ions; <span class="html-italic">K</span><sub>s</sub>, saturated hydraulic conductivity; BD, bulk density; MWD, average weight diameter of soil water stable aggregates; CEC, cation exchange capacity; SOM, soil organic matter; ESP, exchangeable sodium percentage; SAR, sodium adsorption ratio, saturated moisture content; <span class="html-italic">α</span> and <span class="html-italic">n</span>, van Genuchten–Mualem parameters.</p>
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<p>Measured and predicted values of soil hydraulic properties: (<b>a</b>) <span class="html-italic">K</span><sub>s</sub>, (<b>b</b>) 1/<span class="html-italic">α</span>, and (<b>c</b>) <span class="html-italic">n</span>.</p>
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31 pages, 35859 KiB  
Article
Research on Propagation Characteristics of Fracture Grouting in Clay Formation
by Rong Fan, Tielin Chen, Man Li and Xueda Wei
Symmetry 2024, 16(12), 1599; https://doi.org/10.3390/sym16121599 - 30 Nov 2024
Viewed by 501
Abstract
Splitting grouting is a highly effective technique for reinforcing tunnels and underground structures, ensuring their operational stability and facilitating long-term maintenance. It has been widely adopted in the prevention and remediation of geological hazards. However, the theoretical research on the diffusion mechanisms of [...] Read more.
Splitting grouting is a highly effective technique for reinforcing tunnels and underground structures, ensuring their operational stability and facilitating long-term maintenance. It has been widely adopted in the prevention and remediation of geological hazards. However, the theoretical research on the diffusion mechanisms of split grouting lags behind its practical applications. This study addresses several key scientific challenges in understanding the diffusion behavior of split grouting. By integrating experimental design, numerical simulations, and theoretical analysis, we conduct a systematic investigation into the diffusion process and vein morphology of split grouting in both homogeneous and heterogeneous formations. We first employed a self-developed two-dimensional grouting test system to perform diffusion experiments on cohesive strata, focusing on the influence of various factors such as grout density, water/cement ratio, soil consistency, and fracture characteristics. The results provide insights into the diffusion patterns, morphology, soil pressure distribution, and surface uplift behavior of the grout veins. Subsequently, a numerical simulation program, developed in-house, based on the finite element method (FEM) and the volume of fluid (VOF) approach, was employed to model the entire process of fracturing grouting within clay strata. The experimental and numerical results indicate that grout vein diffusion in layered soil follows a Y-shaped pattern with an inclined deflection. In uniform strata, the surface uplift curve displays both symmetrical and asymmetrical “convex” elevations, while in heterogeneous soft and hard strata, the uplift is characterized by distinct “convex” deformations. Finally, based on these findings and the principles of contact mechanics, we analyze the underlying mechanisms. The results suggest that weak contact zones undergo tensile cracking and horizontal deflection prior to the formation of grout veins. Additionally, local stress rotations in the soil can induce tilting and deflection. The theoretical insights derived from this study provide valuable guidance for practical engineering applications. Full article
(This article belongs to the Section Engineering and Materials)
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<p>Composition diagram of two-dimensional grouting test system.</p>
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<p>100 min water precipitation rate and viscosity of slurry. (<b>a</b>) Water precipitation rate of slurry in 100 min; (<b>b</b>) slurry viscosity at 100 min.</p>
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<p>Homogeneous formation measuring point layout diagram.</p>
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<p>Slurry vein diffusion process.</p>
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<p>Grouting diffusion form in soft strata. (<b>a</b>) The water/cement ratio is 1.5:1; (<b>b</b>) the water/cement ratio is 1:1; (<b>c</b>) the water/cement ratio is 0.75:1.</p>
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<p>Grouting diffusion form in medium dense strata. (<b>a</b>) The water/cement ratio is 1.5:1; (<b>b</b>) the water/cement ratio is 1:1; (<b>c</b>) the water/cement ratio is 1.5:1.</p>
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<p>Grouting diffusion form in dense stratum. (<b>a</b>) The water/cement ratio is 1.5:1; (<b>b</b>) the water/cement ratio is 1:1; (<b>c</b>) the water/cement ratio is 0.75:1.</p>
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<p>Soft soil pressure. (<b>a</b>) Horizontal soil pressure; (<b>b</b>) vertical earth pressure.</p>
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<p>Earth pressure of medium dense stratum. (<b>a</b>) Horizontal soil pressure; (<b>b</b>) vertical earth pressure.</p>
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<p>Earth pressure of dense stratum. (<b>a</b>) Horizontal soil pressure; (<b>b</b>) vertical earth pressure.</p>
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<p>Phased curve of surface uplift in soft strata. (<b>a</b>) 1.5:1; (<b>b</b>) 1:1; (<b>c</b>) 0.75:1.</p>
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<p>Medium dense strata surface uplift stage curve. (<b>a</b>) 1.5:1; (<b>b</b>) 1:1; (<b>c</b>) 0.75:1.</p>
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<p>The surface uplift stage curve of dense stratum. (<b>a</b>) 1.5:1; (<b>b</b>) 1:1; (<b>c</b>) 0.75:1.</p>
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<p>Fissure layout diagram (unit: mm).</p>
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<p>Soft and hard uneven formation measuring point layout diagram.</p>
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<p>Layout diagram of measuring points in weak fractured stratum.</p>
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<p>Morphology of slurry veins in soft and hard uneven strata. (<b>a</b>) Vertical penetration shape; (<b>b</b>) symmetrical deflection; (<b>c</b>) unilateral deflection; (<b>d</b>) vertical blocking shape.</p>
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<p>Soil pressure of typical grout vein shape. (<b>a</b>) Slightly hard-soft (vertical penetration shape); (<b>b</b>) medium hard-soft (symmetrical deflection shape); (<b>c</b>) medium hard-soft (unilateral deflection); (<b>d</b>) hard-soft (vertical blocking).</p>
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<p>Surface uplift under typical vein morphology. (<b>a</b>) Vertical penetration shape; (<b>b</b>) symmetric deflection; (<b>c</b>) unilateral deflection; (<b>d</b>) vertical blocking shape.</p>
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<p>Uplifts at different locations on the surface of the earth.</p>
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<p>Slurry vein shape of different fracture length (v = 10 cm). (<b>a</b>) l = 5 cm; (<b>b</b>) l = 10 cm; (<b>c</b>) l = 15 cm; (<b>d</b>) l = 20 cm; (<b>e</b>) l = 25 cm; (<b>f</b>) l = 30 cm.</p>
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<p>Slurry vein shape with different distances from cracks to grouting holes (l = 15 cm). (<b>a</b>) v = 5 cm; (<b>b</b>) v = 15 cm; (<b>c</b>) v = 20 cm.</p>
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<p>Soil pressure in fractured formation (l = 5 cm, v = 10 cm).</p>
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<p>Surface uplift in weak and fractured strata. (<b>a</b>) Ground heave; (<b>b</b>) uplift at different positions on the surface of the earth.</p>
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<p>Model Information (unit: mm). (<b>a</b>) Computational model. (<b>b</b>) Model meshing.</p>
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<p>Comparison diagram of test results and numerical simulation results.</p>
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<p>The maximum uplift value of the pulp vein at different positions.</p>
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<p>Model information (unit: mm). (<b>a</b>) Hierarchical model; (<b>b</b>) hierarchical model mesh; (<b>c</b>) hierarchical model; (<b>d</b>) hierarchical model mesh.</p>
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<p>Slurry vein morphology of layered soils.</p>
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<p>Surface uplift curves of two layers of soil.</p>
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<p>Slurry vein morphology of layered soil.</p>
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<p>The shape of grout veins in fractured strata. (l: fracture length (m), v: the distance between fracture and grouting hole (m)).</p>
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<p>Surface heave curve of two layers of soil.</p>
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<p>Variation of minor principal stress σ3 in fractured soil (<b>a</b>) 445 s, (<b>b</b>) 640 s, and (<b>c</b>) 960 s.</p>
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<p>Layout of measuring points.</p>
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<p>Radar chart of variation of minor principal stress with diffusion length of grout pulse.</p>
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17 pages, 5773 KiB  
Article
Advanced Scanning Technology for Volume Change Measurement of Residual Soil
by Saltanat Orazayeva, Alfrendo Satyanaga, Yongmin Kim, Harianto Rahardjo, Zhai Qian, Sung-Woo Moon and Jong Kim
Appl. Sci. 2024, 14(23), 10938; https://doi.org/10.3390/app142310938 - 25 Nov 2024
Viewed by 603
Abstract
Weathering processes of rocks lead to the formation of residual soil layers, which are typically characterized by a deep groundwater table and a thick unsaturated zone. Hence, the calculation of a slope’s safety factor under the influences of climatic circumstances is a function [...] Read more.
Weathering processes of rocks lead to the formation of residual soil layers, which are typically characterized by a deep groundwater table and a thick unsaturated zone. Hence, the calculation of a slope’s safety factor under the influences of climatic circumstances is a function of unsaturated characteristics, such as the soil–water characteristic curve (SWCC). To determine the SWCC, the volume of the soil specimen must be determined in order to compute the void ratio and degree of saturation. The drying processes of the soil specimen led to uneven soil volume change during laboratory SWCC testing, demanding the development of a soil shrinkage curve. Several methods for measuring soil volume change have been developed over the years. However, there are significant limitations, and it is rarely used due to the difficulty linked to accurately measuring the soil volume during drying processes. In this study, a revised scanning approach is developed to evaluate residual soil volume change utilizing 3D scanning technology. The proposed method is applied in a case study on residual soil from the Old Alluvium in Singapore. The laboratory data and analysis results suggested that 3D scanning technology should be required to provide a correct estimation of the air-entry value of soil. Full article
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<p>Locations of the soil samples on a Singapore geological map.</p>
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<p>Initial calibration of the 3D scanner.</p>
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<p>Point cloud of a soil specimen after editing in MFstudio.</p>
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<p>Point cloud of a soil specimen imported into MeshLab.</p>
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<p>Exterior mesh surface after trimming in MeshLab.</p>
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<p>Creation of a solid soil specimen in Meshmixer.</p>
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<p>Volume measurement of a solid soil specimen in Meshmixer.</p>
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<p>SWCC of soil specimen from Tampines with respect to gravimetric water content.</p>
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<p>Shrinkage curve of soil specimen from Tampines.</p>
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<p>SWCC of Tampines soil specimens with regard to degree of saturation, using shrinkage curve from 3D scan.</p>
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<p>SWCC of Tampines soil samples in terms of the degree of saturation without shrinkage curve from 3D scan.</p>
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22 pages, 9253 KiB  
Article
New Method for Hydraulic Characterization of Variably Saturated Zone in Peatland-Dominated Permafrost Mires
by Radhakrishna Bangalore Lakshmiprasad, Stephan Peth, Susanne K. Woche and Thomas Graf
Land 2024, 13(12), 1990; https://doi.org/10.3390/land13121990 - 22 Nov 2024
Viewed by 792
Abstract
Modeling peatland hydraulic processes in cold regions requires defining near-surface hydraulic parameters. The current study aims to determine the soil freezing and water characteristic curve parameters for organic soils from peatland-dominated permafrost mires. The three research objectives are as follows: (i) Setting up [...] Read more.
Modeling peatland hydraulic processes in cold regions requires defining near-surface hydraulic parameters. The current study aims to determine the soil freezing and water characteristic curve parameters for organic soils from peatland-dominated permafrost mires. The three research objectives are as follows: (i) Setting up an in situ soil freezing characteristic curve experiment by installing sensors for measuring volumetric water content and temperature in Storflaket mire, Abisko region, Sweden; (ii) Conducting laboratory evaporation experiments and inverse numerical modeling to determine soil water characteristic curve parameters and comparing three soil water characteristic curve models to the laboratory data; (iii) Deriving a relationship between soil freezing and water characteristic curves and optimizing this equation with sensor data from (i). A long-lasting in situ volumetric water content station has been successfully set up in sub-Arctic Sweden. The soil water characteristic curve experiments showed that bimodality also exists for the investigated peat soils. The optimization results of the bimodal relationship showed excellent agreement with the soil freezing cycle measurements. To the best of our knowledge, this is one of the first studies to establish and test bimodality for frozen peat soils. The estimated hydraulic parameters could be used to better simulate permafrost dynamics in peat soils. Full article
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<p>Schematic diagram of overall methodology: It is divided into three sections. The yellow boxes display the first section about soil freezing characteristic curve experiments. The blue boxes display the second section about conducting soil water characteristic curves (SWCC) experiments and inverse numerical modeling. The green box shows the final section about calibrating the <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>[</mo> <mi>h</mi> <mo>(</mo> <mi>T</mi> <mo>)</mo> <mo>]</mo> </mrow> </semantics></math> relationship. <math display="inline"><semantics> <mi>θ</mi> </semantics></math>, <span class="html-italic">T</span>, and <span class="html-italic">h</span> are the volumetric water content, soil temperature, and soil water potential head, respectively.</p>
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<p>Study area: (<b>a</b>) Location of Abisko within Sweden. (<b>b</b>) Abisko Scientific Research Station and the Storflaket Mire are shown within the Abisko region. The land use maps and country borders were obtained from OpenStreetMap contributors [<a href="#B32-land-13-01990" class="html-bibr">32</a>].</p>
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<p>Schematic diagram of volumetric water content sensor installation layout: It shows the aerial and cross-sectional views. The soil profiles are labeled from S1 to S6, and the sensors are labeled from T1 to T12. Two sensors are present at each soil profile.</p>
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<p>Conceptual diagram for determining SWCC: (<b>a</b>) A conceptual model of the evaporation experiment was set up to measure the soil water potential head and wet soil weight. (<b>b</b>) Conceptual model showing a 1D model setup that simulates the variably saturated processes in the subsurface domain due to evaporation that occurs from (<b>a</b>).</p>
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<p>Photos from evaporation experiment: (<b>a</b>) Saturation of soil samples by placing a porous lid and cloth material on the bottom of the sample, placing the sample within a container, and filling water close to the brim of the soil sample. (<b>b</b>) Degassing the sensor units to remove any air bubbles. (<b>c</b>) Evaporation measurements of soil water potential head and wet soil weight. (<b>d</b>) Soil samples after oven drying to determine the dry bulk density and porosity of the soil sample.</p>
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<p>Results of sensor data—Part 1: Soil temperature and volumetric water content at the three soil profiles from S1 to S3 at depths 0.1 m and 0.25 m from July 2022 to November 2023. The volumetric water content periods can be divided into thawed, freezing transition, frozen, thawing transition, and thawed periods.</p>
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<p>Pre-processed sensor data: Soil Freezing Characteristic Curves (SFCC) for the first freezing transition period (October to November 2022) at depths 0.1 m, 0.25 m, 0.3 m, 0.4 m, and 0.5 m. Only the measurements made in the −1 to 1 °C range are shown in this figure.</p>
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<p>Results of SWCC analysis—Part 1: Measured and simulated soil water retention curves [<math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </semantics></math>] and hydraulic conductivity curves [<math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </semantics></math>] for soil profiles SP1, SP2, and SP4 at depths 0.1 m and 0.25 m. The simulated values are the three models that fit with the optimum parameters. The x-axis represents the soil water potential head and is displayed as pF = <math display="inline"><semantics> <mrow> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mo>|</mo> <mi>h</mi> <mo>|</mo> <mo>)</mo> </mrow> </mrow> </semantics></math> where <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>h</mi> <mo>|</mo> </mrow> </semantics></math> is in cm.</p>
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<p>Results of SWCC analysis—Part 2: The box plot of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> value of soil water retention curve (first plot—<math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </semantics></math>), hydraulic conductivity curve (second plot—<math display="inline"><semantics> <mrow> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>10</mn> </msub> <mi>K</mi> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) and Akaike information criterion (third plot) for the three model fits—VGcOrg, VGcPDI, and VGcBiPDI with respect to the measured values from the SWCC experiments.</p>
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<p>Optimization results of <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>[</mo> <mi>h</mi> <mo>(</mo> <mi>T</mi> <mo>)</mo> <mo>]</mo> </mrow> </semantics></math> relationship: The preprocessed SFCC measured data and simulated values after optimization for the four soil profiles SP1, SP2, SP3, and SP4 at 0.1 and 0.25 m. The <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the measured and simulated volumetric water content values are shown above each plot.</p>
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<p><b>Fieldphotos of the volumetric water content sensor installation:</b> (<b>a</b>) Main trench dug to install PVC pipes that contain the sensor cables. (<b>b</b>) Sensors installed at the depths of 0.1 m and 0.25 m in the soil profile SP1. (<b>c</b>) ZL6 data logger and housing box connected to the sensors.</p>
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<p><b>Photos of the six profiles from SP1 to SP6:</b> Each soil profile had a rough dimension of 0.6 m · 0.8 m · 0.3 m (length · breadth · depth). The volumetric water content sensors were installed on the walls of the cut soil profile within the ground. Similar soil profiles were taken adjacent to these rectangular blocks to extract the soil samples for SWCC experiments.</p>
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<p><b>Results of sensor data—Part 2:</b> Soil temperature and volumetric water content at the three soil profiles (SP4, SP5, and SP6) from depths 0.1 m to 0.5 m. The volumetric water content measurements can be divided into thawed, transition, and frozen periods.</p>
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<p><b>All sample results of SWCC analysis:</b> Measured and simulated soil water retention curves [<math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </semantics></math>] and hydraulic conductivity curves [<math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </semantics></math>] for soil profiles SP1 to SP6 at depths 0.1 m and 0.25 m. The simulated values are the three model fits with optimum parameters—VGcOrg, VGcPDI, and VGcBiPDI. The x-axis is displayed as pF = <math display="inline"><semantics> <mrow> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mo>|</mo> <mi>h</mi> <mo>|</mo> <mo>)</mo> </mrow> </mrow> </semantics></math> where <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>h</mi> <mo>|</mo> </mrow> </semantics></math> is in cm.</p>
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