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Geotechnics, Volume 4, Issue 2 (June 2024) – 18 articles

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20 pages, 14837 KiB  
Review
3D Numerical Modeling of Geosynthetics for Soil Reinforcement: A Bibliometric Analysis and Literature Review
by Lucas Paiva, Margarida Pinho-Lopes, António Miguel Paula and Robertt Valente
Geotechnics 2024, 4(2), 673-692; https://doi.org/10.3390/geotechnics4020036 - 18 Jun 2024
Viewed by 828
Abstract
Soil reinforcement using geosynthetics is an efficient and cost-effective solution for a variety of geotechnical structures. Along with the increasing use of geosynthetics, there is a need to expand and enhance the design methodologies for these elements, which are still frequently based on [...] Read more.
Soil reinforcement using geosynthetics is an efficient and cost-effective solution for a variety of geotechnical structures. Along with the increasing use of geosynthetics, there is a need to expand and enhance the design methodologies for these elements, which are still frequently based on conservative limit equilibrium approaches. In this paper, a bibliometric analysis was conducted on geosynthetic-reinforced soil structures (GRS), identifying the state of the art, research trends, and other indicators. The data were obtained from the Scopus platform and processed by VOSViewer v1.6 software. The initial search comprised 552 papers and the screening process selected 516 relevant papers from 1992 to October 2023. The study analyzed the occurrence of publications by year, keyword trends, authors, citations/co-citations, and bibliographic coupling. Then, a focus was given to 3D modeling research on geosynthetics, highlighting the dominant modeling techniques, material properties, and design challenges in GRS. The bibliometric analysis provided a crucial guideline in the identification of relevant papers and research trends, and a series of conclusions were presented regarding the 3D modeling techniques, choice of material properties, and boundary conditions. Full article
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Graphical abstract

Graphical abstract
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<p>Steps of the bibliometric analysis carried out in the present paper.</p>
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<p>Annual trend in GRS publications meeting the search criteria.</p>
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<p>Co-authorship of countries for the papers meeting the search criteria.</p>
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<p>Co-occurrence of keywords map for papers meeting the search criteria.</p>
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<p>Average publication year of co-occurring keywords for papers meeting the search criteria.</p>
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<p>(<b>a</b>) Observed membrane effect on experimental test; (<b>b</b>) Simulation of membrane effect on geotextile [<a href="#B29-geotechnics-04-00036" class="html-bibr">29</a>].</p>
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<p>(<b>a</b>) Cross-section of GESC model; (<b>b</b>) 3D column model [<a href="#B30-geotechnics-04-00036" class="html-bibr">30</a>].</p>
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<p>Coupled FE/DE strip footing [<a href="#B32-geotechnics-04-00036" class="html-bibr">32</a>].</p>
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<p>Comparison between isotropic, orthotropic, and truss element models: (<b>a</b>) effect of embankment height on the reinforcement stresses; (<b>b</b>) effect of pile spacing on the reinforcement stresses; (<b>c</b>) effect of geogrid stiffness; (<b>d</b>) effect of compression index of subsoil [<a href="#B33-geotechnics-04-00036" class="html-bibr">33</a>].</p>
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<p>Cross-section of modeled embankment [<a href="#B34-geotechnics-04-00036" class="html-bibr">34</a>].</p>
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<p>Geometry of the extruded geogrid used in 3D FEM models [<a href="#B15-geotechnics-04-00036" class="html-bibr">15</a>].</p>
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<p>The 2D and 3D FEM geometry in [<a href="#B36-geotechnics-04-00036" class="html-bibr">36</a>].</p>
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<p>Details of the 3D mesh geometry for pullout modeling: (<b>a</b>) 5-part model, where BS and TS are the bottom and top soil domains, respectively; Soil<sub>OPN</sub> is the soil between the geogrid openings; GEO is the geogrid and CLAMP is the non-deformable element that pulls the geogrid; (<b>b</b>) soil-geogrid contact interfaces [<a href="#B37-geotechnics-04-00036" class="html-bibr">37</a>].</p>
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18 pages, 29917 KiB  
Article
The Geomechanics of the Dangkhar Landslide, Himachal Pradesh, India
by Markus Kaspar and D. Scott Kieffer
Geotechnics 2024, 4(2), 655-672; https://doi.org/10.3390/geotechnics4020035 - 14 Jun 2024
Viewed by 486
Abstract
The Dangkhar Landslide is an extremely large landslide located in the Spiti Valley of Himachal Pradesh, India. The landslide is situated in a remote high mountain desert within the Tethys Himalaya at elevations between 3400 m and 5600 m. It is amongst the [...] Read more.
The Dangkhar Landslide is an extremely large landslide located in the Spiti Valley of Himachal Pradesh, India. The landslide is situated in a remote high mountain desert within the Tethys Himalaya at elevations between 3400 m and 5600 m. It is amongst the five largest continental landslides on earth, covering an area of approximately 54 km2 and having an estimated volume of 15–20 km3. Geomechanical evaluations based on the block theory indicate that the Dangkhar Landslide formed as a result of unfavorable combinations of structural geological features and complex surface morphology. A massive kinematically removable block is created by a regional synclinal flexure that is crosscut and kinematically liberated by bounding side valleys. Three-dimensional block kinematics are necessary to permit the release of the giant block and its sliding along the synclinal flexure. Pseudostatic slope stability sensitivity analyses incorporating estimates of site seismicity and shear strength parameters suggest that earthquake shaking could have triggered instability if the static factor of safety was less than or in the range of about 1.5–1.9. Considering the glacial history of the region, ice debuttressing represents an additional potential triggering mechanism. Full article
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<p>The regional setting. (<b>A</b>) The geographic location of the study area within Himachal Pradesh, India. (<b>B</b>) The Spiti Valley with its major rivers and villages. (<b>C</b>) A simplified tectonic map of the Himalaya (modified from [<a href="#B23-geotechnics-04-00035" class="html-bibr">23</a>]).</p>
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<p>Seismotectonic map of study area and surrounding regions (compiled after [<a href="#B13-geotechnics-04-00035" class="html-bibr">13</a>,<a href="#B24-geotechnics-04-00035" class="html-bibr">24</a>,<a href="#B34-geotechnics-04-00035" class="html-bibr">34</a>,<a href="#B40-geotechnics-04-00035" class="html-bibr">40</a>,<a href="#B41-geotechnics-04-00035" class="html-bibr">41</a>]). CF—Chandra Fault; KCF—Kaurik-Chango Fault; KNF—Karcham Normal Fault; LPF—Leo Pargil Fault; MPF—More Plain Fault; SF—Sarchu Fault; SFC—Syarma Fault Complex; SVF—Spiti Valley Fault; TMF—Tso Morari Fault. Moment tensors shown for major large earthquakes (Pentagrams) (KEQ—Kinnaur Earthquake). Figure obtained from Hintersberger et al. (2010) [<a href="#B41-geotechnics-04-00035" class="html-bibr">41</a>]: copyright The Geological Society of America, Inc., (Boulder, CO, USA) used with permission.</p>
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<p>(<b>a</b>) A geologic map of the study area showing bedrock units and surficial deposits supplemented by morphological and structural features. The locations of the details discussed in <a href="#geotechnics-04-00035-f005" class="html-fig">Figure 5</a> and <a href="#geotechnics-04-00035-f007" class="html-fig">Figure 7</a> are highlighted. (<b>b</b>) A geologic cross section through the study area.</p>
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<p>A photograph of the Dangkhar Landslide looking in the northwestern direction, highlighting the overall bedrock structure.</p>
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<p>The major geomorphologic features of the Dangkhar Landslide. (<b>a</b>) A Google Earth Pro satellite image of large-scale lineaments downhill of the Dangkhar Lake. (<b>b</b>) A field photograph of the typical morphology formed by the lineaments.</p>
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<p>Slope tectonic and internal landslide structures of the Dangkhar Landslide. (<b>a</b>) Graben with trapped sediments. (<b>b</b>) Lineaments as surface expressions of counterscarps resulting from toppling. (<b>c</b>) Folded rock strata in rock block incorporated within Dangkhar Landslide. (<b>d</b>) Basal slip surface at toe area expressed as gradual transition from bedrock formations into Dangkhar Landslide debris. Sketch of gravitational shear zone within fine grained rocks such as shales [<a href="#B45-geotechnics-04-00035" class="html-bibr">45</a>]. Reprinted from Engineering Geology, 32, Chigira, M., Long-term gravitational deformation of rocks by rock mass creep, pp. 157–184, Copyright (1992), with permission from Elsevier. Schematic sketch of slope tectonic features modified after [<a href="#B46-geotechnics-04-00035" class="html-bibr">46</a>]. Reprinted from Tectonophysics, Vol. 605, Jaboyedoff, M.; Penna, I.; Pedrazzini, A.; Baroň, I.; Crosta, G.B., An introductory review on gravitational-deformation induced structures, fabrics and modeling, pp. 1–12, Copyright (2013), with permission from Elsevier.</p>
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<p>Cross-cutting relations between rock glaciers and lineaments. (<b>a</b>) Bing maps satellite image of rock glaciers beneath Mount Chokula and cross-cutting relations with lineaments. (<b>b</b>) Field photograph of uppermost rock glaciers overriding lineaments.</p>
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<p>Dangkhar Landslide volume and geometry. (<b>a</b>) Overall landslide dimensions (ArcScene 3D visualization). (<b>b</b>) Best fit tangent planes representing basal slip surface formed along synclinal flexure. (<b>c</b>) Satellite image of Dangkhar Landslide with outlined removable block.</p>
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<p>Kinematic and mode analyses for Dangkhar Landslide. Failure mode of removable block is given in parentheses.</p>
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<p>Reduction in factor of safety according to yield coefficient.</p>
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<p>Schematic evolution of Spiti Valley during and after the last major valley glaciation. (<b>a</b>) Valley glacier setting with associated deposits. (<b>b</b>) Paraglacial setting after glacier recession. Key to characteristic landforms: 1 = moraine, 2 = sediment accumulations against glacier, 3 = rock fall deposits, 4 = large-scale landslide structures (scarps and lineaments), 5 = landslide deposit, 6 = fan deposit, 7 = braided river bed (modified from [<a href="#B59-geotechnics-04-00035" class="html-bibr">59</a>]). (<b>c</b>) Inferred valley glacier situation at Dangkhar viewed upstream of Spiti Valley.</p>
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<p>Interpretive development of Dangkhar Landslide over time (abbreviations of formation names correspond to those in <a href="#geotechnics-04-00035-f003" class="html-fig">Figure 3</a>).</p>
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<p>A summary chart of the evolution of the Dangkhar Landslide in a broader regional context. The color bars correspond to the glacial stages in <a href="#geotechnics-04-00035-f012" class="html-fig">Figure 12</a>. The inferred initiation and episodic activity of the Dangkhar Landslide are shown between the solid red diamonds (modified and compiled after [<a href="#B20-geotechnics-04-00035" class="html-bibr">20</a>,<a href="#B26-geotechnics-04-00035" class="html-bibr">26</a>,<a href="#B34-geotechnics-04-00035" class="html-bibr">34</a>,<a href="#B54-geotechnics-04-00035" class="html-bibr">54</a>,<a href="#B64-geotechnics-04-00035" class="html-bibr">64</a>]). Reproduced with permission from Srivastava, P.; Ray, Y.; Phartiyal, B.; Sharma, A., Late Pleistocene-Holocene morphosedimentary architecture, Spiti River, arid Higher Himalaya, Int. J. Earth Sci., 102, pp. 1967–1984; published by Springer Nature, 2013. <a href="https://link.springer.com/article/10.1007/s00531-013-0871-y/figures/16" target="_blank">https://link.springer.com/article/10.1007/s00531-013-0871-y/figures/16</a>, accessed on 1 May 2024.</p>
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19 pages, 3364 KiB  
Article
Determination of Constrained Modulus of Granular Soil from In Situ Tests—Part 2 Application
by K. Rainer Massarsch
Geotechnics 2024, 4(2), 636-654; https://doi.org/10.3390/geotechnics4020034 - 14 Jun 2024
Viewed by 524
Abstract
The paper demonstrates how the concepts presented in the companion paper: “Determination of Constrained Modulus of Granular Soil from In Situ Tests—Part 1 Analyses” can be applied in practice. A settlement design based on the tangent modulus method is described. Extensive in situ [...] Read more.
The paper demonstrates how the concepts presented in the companion paper: “Determination of Constrained Modulus of Granular Soil from In Situ Tests—Part 1 Analyses” can be applied in practice. A settlement design based on the tangent modulus method is described. Extensive in situ tests were performed on a well-documented test site consisting of sand with silt and clay layers. The field tests comprised different types of penetration tests, such as the cone penetration test, the flat dilatometer, and the seismic down-hole test. The modulus number and the constrained tangent modulus were derived from the cone penetration test with pore water pressure measurement and the flat dilatometer test. In addition, the shear wave speed was determined from two seismic down-hole tests, from which the small-strain shear modulus could be evaluated. The constrained modulus obtained from the cone penetration test with pore water pressure measurement (CPTU) and the flat dilatometer (DMT) was compared with that from the seismic down-hole tests. The importance of the stress history on the constrained modulus was demonstrated. The range of modulus numbers, derived from different in situ tests, compares favorably with empirical values reported in the literature. Full article
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Figure 1
<p>Soil sample from borehole A3 and SPT <span class="html-italic">N</span>-values and cone resistance, <span class="html-italic">q</span><sub>c</sub>, adapted from [<a href="#B31-geotechnics-04-00034" class="html-bibr">31</a>].</p>
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<p>Results of CPTU: (<b>a</b>) cone resistance, (<b>b</b>) sleeve resistance, (<b>c</b>) hydrostatic and pore water pressure, and (<b>d</b>) soil behavior index.</p>
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<p>Overconsolidation ratio determined from CPTU based on Equations (3) and (4).</p>
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<p>Modulus number derived from CPTU for assuming normally consolidated (<span class="html-italic">NC</span>). The effect of preloading (<span class="html-italic">OC</span>) was considered according to Equation (5).</p>
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<p>Tangent constrained modulus as a function of depth, determined from modulus number, <span class="html-italic">m</span>, according to Equation (9) given in the companion paper.</p>
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<p>Results of DMT: (<b>a</b>) pressure readings, (<b>b</b>) material index, (<b>c</b>) horizontal stress index, and (<b>d</b>) dilatometer modulus.</p>
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<p>Variation in constrained modulus from DMT according to Equation (8).</p>
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<p>The variation in modulus number with depth from DMT derived from the tangent modulus according to Equation (9).</p>
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<p>(<b>a</b>) Shear wave speed as determined from seismic down-hole tests (SCPT and SDMT) and (<b>b</b>) Small-strain shear modulus, <span class="html-italic">G</span><sub>0</sub>, from SCPT and SDMT.</p>
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<p>Relationship between maximum shear modulus, <span class="html-italic">G</span><sub>0</sub>, and modulus number, <span class="html-italic">m</span>, cf. Equation (10) at 0.25% shear strain. Dashed lines <span class="html-italic">σ</span>′<sub>v</sub> = 50 kPa; full line: <span class="html-italic">σ</span>′<sub>v</sub> = 100 kPa. The blue line indicates the modulus number for medium-dense, normally consolidated sand.</p>
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<p>Modulus number derived from seismic tests (SCPTU and SDMT) for normally consolidated (NC) and overconsolidated (OC) conditions.</p>
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<p>Tangent constrained modulus from seismic tests (SCPTU and SDMT) for normally consolidated (NC) and overconsolidated (OC) conditions.</p>
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32 pages, 16801 KiB  
Article
A Critical Review of Cone Penetration Test-Based Correlations for Estimating Small-Strain Shear Modulus in North Sea Soils
by Bruno Stuyts, Wout Weijtjens, Carlos Sastre Jurado, Christof Devriendt and Anis Kheffache
Geotechnics 2024, 4(2), 604-635; https://doi.org/10.3390/geotechnics4020033 - 14 Jun 2024
Viewed by 1381
Abstract
The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. [...] Read more.
The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. Since direct measurements of Vs are generally not available at all foundation locations in a wind farm, correlations with cone penetration test (CPT) results are often used to determine location-specific stiffness parameters for foundation design. Existing correlations have mostly been calibrated to onshore datasets which may not contain the same soil types and stress conditions found in the North Sea. The distinct geological history of the North Sea necessitates a critical review of these existing CPT-based correlations. They are evaluated against an extensive database of in situ Vs measurements in the southern North Sea. The importance of modelling the stress-dependent nature of Vs is highlighted, and a novel stress-dependent model for Vs from CPT data, which leads to an improved fit, is presented. As the small-strain stiffness is used as an input to foundation response calculations, the model uncertainty of the correlation can introduce significant uncertainty into the resulting foundation response. This transformation uncertainty is quantified for each of the correlations evaluated in this study and shows important variations. Full article
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<p>Depositional environments in the North Sea during the early Eocene [<a href="#B24-geotechnics-04-00033" class="html-bibr">24</a>] (?-symbols indicate uncertain boundaries of the depositional environments or sediment supply).</p>
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<p>Depositional environments in the North Sea during the Mid-Miocene [<a href="#B24-geotechnics-04-00033" class="html-bibr">24</a>].</p>
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<p>Depositional environments in the North Sea during the Early Pleistocene [<a href="#B24-geotechnics-04-00033" class="html-bibr">24</a>].</p>
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<p>Schematic depositional history for the IJmuiden Ver offshore wind farm zone from the Mid-Pleistocene to the Mid-Eemian [<a href="#B25-geotechnics-04-00033" class="html-bibr">25</a>].</p>
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<p>Schematic depositional history for the IJmuiden Ver offshore wind farm zone from the Mid-Eemian to present day [<a href="#B25-geotechnics-04-00033" class="html-bibr">25</a>].</p>
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<p>Geographical location of the S-PCPT tests in the Belgian, Dutch, German and Danish sectors of the North Sea.</p>
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<p>Effect of the CPT testing method on the relation between vertical effective stress and shear wave velocity.</p>
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<p>Combined box and violin plots of the shear wave velocity dataset.</p>
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<p>Relation between vertical effective stress, total cone resistance, soil behaviour type index and shear wave velocity.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by [<a href="#B8-geotechnics-04-00033" class="html-bibr">8</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by Rix and Stokoe [<a href="#B9-geotechnics-04-00033" class="html-bibr">9</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by Mayne and Rix [<a href="#B10-geotechnics-04-00033" class="html-bibr">10</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by Peuchen et al. [<a href="#B18-geotechnics-04-00033" class="html-bibr">18</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Wride et al. [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Verification of the linear trend between <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>/</mo> <msub> <mi>q</mi> <mrow> <mi>c</mi> <mn>1</mn> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math> identified by Hegazy and Mayne for the North Sea data.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Hegazy and Mayne [<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Andrus et al. [<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Tonni and Simonini [<a href="#B14-geotechnics-04-00033" class="html-bibr">14</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Predicted variation in <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> with depth for the 6 reference sediments provided by Lyu et al. [<a href="#B41-geotechnics-04-00033" class="html-bibr">41</a>].</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Robertson and Cabal [<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by McGann et al. [<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Combined box and violin plots of the ratio <math display="inline"><semantics> <mfrac> <msub> <mi>G</mi> <mrow> <mi>max</mi> <mo>,</mo> <mi>c</mi> <mi>a</mi> <mi>l</mi> <mi>c</mi> <mi>u</mi> <mi>l</mi> <mi>a</mi> <mi>t</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> <msub> <mi>G</mi> <mrow> <mi>max</mi> <mo>,</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> </mfrac> </semantics></math> [<a href="#B8-geotechnics-04-00033" class="html-bibr">8</a>,<a href="#B9-geotechnics-04-00033" class="html-bibr">9</a>,<a href="#B10-geotechnics-04-00033" class="html-bibr">10</a>,<a href="#B18-geotechnics-04-00033" class="html-bibr">18</a>].</p>
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<p>Combined box and violin plots of the ratio <math display="inline"><semantics> <mfrac> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>c</mi> <mi>a</mi> <mi>l</mi> <mi>c</mi> <mi>u</mi> <mi>l</mi> <mi>a</mi> <mi>t</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> </mfrac> </semantics></math> [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>,<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>,<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>,<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>,<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>].</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the new stress-dependent correlation. The measurements are colour coded according to the soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Dependence of the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> on the values of <math display="inline"><semantics> <msub> <mi>q</mi> <mi>c</mi> </msub> </semantics></math>. A marginal histogram for <math display="inline"><semantics> <msub> <mi>q</mi> <mi>c</mi> </msub> </semantics></math> is shown in the uppermost panel and a marginal histogram for the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> is shown in the rightmost panel.</p>
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<p>Dependence of the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> on the values of <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>. A marginal histogram for <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math> is shown in the uppermost panel and a marginal histogram for the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> is shown in the rightmost panel.</p>
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<p>Dependence of the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> on the values of <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>v</mi> <mn>0</mn> </mrow> <mo>′</mo> </msubsup> </semantics></math>. A marginal histogram for <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>v</mi> <mn>0</mn> </mrow> <mo>′</mo> </msubsup> </semantics></math> is shown in the uppermost panel and a marginal histogram for the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> is shown in the rightmost panel.</p>
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<p>Comparison of <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> profiles obtained with different correlations with direct <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> measurements for location IJV162-SCPT with uniform sandy soil [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>,<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>,<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>,<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>,<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>].</p>
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<p>Comparison of <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> profiles obtained with different correlations with direct <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> measurements for location IJV038-SCPT with layered soil [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>,<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>,<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>,<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>,<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>].</p>
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23 pages, 22509 KiB  
Article
Finer Measurement Scales for Induced Hydrophobicity Using the Water Droplet Penetration Test
by Max Veneris and Arvin Farid
Geotechnics 2024, 4(2), 581-603; https://doi.org/10.3390/geotechnics4020032 - 11 Jun 2024
Cited by 1 | Viewed by 542
Abstract
The Water Droplet Penetration Test (WDPT) is commonly used in most soil water repellency (SWR) research and is particularly prominent in field studies after wildfire events. Suppose a water droplet does not infiltrate the soil within the first five seconds. This soil is [...] Read more.
The Water Droplet Penetration Test (WDPT) is commonly used in most soil water repellency (SWR) research and is particularly prominent in field studies after wildfire events. Suppose a water droplet does not infiltrate the soil within the first five seconds. This soil is considered to contain some degree of water repellency, classified by the overall penetration time. Our results show an inflection point in the plot of the height of a droplet vs. droplet penetration time during a WDPT trial. This inflection point is indicative of a combination of two possible flow patterns influencing droplet penetration, one governing and the other—caused by particle lift—drastically impeding the infiltration rate. The reorganization of the intrinsic particle lift at the air–water interface leads to contact angles hindering the expected penetration, delaying the expected infiltration rate to degrees larger than a continuously flat porous hydrophobic surface would. The particle lift creates an instability that can create two competing regimes, leading to two sets of water droplet penetration times. The similarity among sorptivity values for coarse grains at higher hydrophobicity levels, medium grains at intermediate hydrophobicity levels, and fine grains at lower hydrophobicity levels suggests that interpretation of the WDPT needs to be adjusted based on grain size. Full article
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Figure 1

Figure 1
<p>Experimental setup for imaging WDPTs, including height and contact angle.</p>
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<p>Water Droplet Penetration Test results (i.e., height vs. time) for coarse-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Water Droplet Penetration Test results (i.e., height vs. time) for medium-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Water Droplet Penetration Test results (i.e., height vs. time) for fine-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Changes in the average contact angle of the water droplet over time for coarse-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8.</p>
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<p>Changes in the average contact angle of the water droplet over time for medium-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Changes in the average contact angle of the water droplet over time for fine-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Average sorptivity values across grain size and surrogate dilution of: (<b>a</b>) coarse, (<b>b</b>) medium, and (<b>c</b>) fine grained samples.</p>
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<p>Example of sorptivity calculated for 16 drops on coarse-grained soil at 0.6% dilution. The black dashed line represents the average value shown in <a href="#geotechnics-04-00032-f008" class="html-fig">Figure 8</a>.</p>
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<p>From left to right, droplets are assigned a value of 0% (or negligible), 50%, 75%, and 100% for particle lift.</p>
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<p>Hydrophobic, fine-grained, 0.8% concentration Drop 9 (with faster penetration) at: (<b>a</b>) initial contact, (<b>b</b>) 3 s, and (<b>c</b>) 4 s later.</p>
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<p>Hydrophobic, fine-grained, 0.8% concentration Drop 1 (with slower penetration) at: (<b>a</b>) initial contact, (<b>b</b>) 1 min, and (<b>c</b>) 5 min later.</p>
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<p>Coarse grain, 0.7% concentration, Drops 2 and 1 from left to right with particle lift and reorientation at 5 and 8 s, respectively (i.e., grains protruding from the droplet marble, reorientating during penetration).</p>
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<p>Water Droplet Penetration Test best-fit third-order polynomial (i.e., height vs. time) for coarse-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Water Droplet Penetration Test best-fit third-order polynomial (i.e., height vs. time) for medium-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Water Droplet Penetration Test best-fit third-order polynomial (i.e., height vs. time) for fine-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Average Water Droplet Penetration Test values for uninterrupted infiltration cases (Regime 1 only) across all specimens inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Average Water Droplet Penetration Test values for interrupted infiltration cases (Regime 2 exists) across all specimens inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Calculated sorptivity values for each droplet that was observed through the full penetration time for the coarse-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%. Red and green dashed lines mark the average of cases with interrupted and uninterrupted infiltration by particle lift with slower and faster WDP, respectively.</p>
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<p>Calculated sorptivity values for each droplet that was observed through the full penetration time for the medium-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%. Red and green dashed lines mark the average of cases with interrupted and uninterrupted infiltration by particle lift with slower and faster WDP, respectively.</p>
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<p>Calculated sorptivity values for each droplet that was observed through the full penetration time for the fine-grained soil for hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%. Red and green dashed lines mark the average of cases with interrupted and uninterrupted infiltration by particle lift with slower and faster WDP, respectively.</p>
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17 pages, 4311 KiB  
Article
Risk Assessment in Landslide-Prone Terrain within a Complex Geological Setting at Kadugannawa, Sri Lanka: Implications for Highway Maintenance
by Sunera Mahinsa Pitawala, Harindu Wimalakeerthi and Thomas Heinze
Geotechnics 2024, 4(2), 564-580; https://doi.org/10.3390/geotechnics4020031 - 8 Jun 2024
Viewed by 827
Abstract
The major highway in Sri Lanka that links the capital, Colombo, with the second capital, Kandy, passes through Kadugannawa, characterized by steep hills. The geological and geomorphological setting of the terrain often leads to slope failures. The objective of this study is to [...] Read more.
The major highway in Sri Lanka that links the capital, Colombo, with the second capital, Kandy, passes through Kadugannawa, characterized by steep hills. The geological and geomorphological setting of the terrain often leads to slope failures. The objective of this study is to interpret the key factors influencing the slope failures that occurred in close proximity at two separate locations with two different slope conditions. Typical local and regional brittle and ductile structures include fault scarps, deep-seated detachments, and variable folding. According to our results, one of the studied locations experienced translational landslides because of weakened basement rock surfaces, hydrophilic clay minerals, and anthropogenic influences, whereas the other location experienced multiple stages of mass movement influenced by inhomogeneous colluvial soil and regional, geological, and hydrogeological conditions. Based on the present study, it can be concluded that geological studies must be carried out within the local area rather than at the regional scale. Otherwise, the constructions for the prevention of landslides in complicated geological settings will fail or may not be used for a long period. Moreover, consideration of future climate change is essential when undertaking construction in challenging terrains. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Climatic zones of Sri Lanka based on Punyawardena (2020) [<a href="#B48-geotechnics-04-00031" class="html-bibr">48</a>] with major cities and the national railway and highway network. (<b>b</b>) The two test sites along the Colombo–Kandy Road, close to Kadugannawa (satellite image by Google Maps 2024 Airbus/CNES). (<b>c</b>) Monthly precipitation and rainfall days of 2021 for the study area, with data taken from World Weather Online.</p>
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<p>Map showing the major structural setting of the study area and its surroundings based on data from [<a href="#B53-geotechnics-04-00031" class="html-bibr">53</a>] and the present study. The red lines marked in the map are possible lineaments according to the aerial photographs and satellite images.</p>
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<p>Geomorphological mapping of study site 1 (<b>a</b>) and site 2 (<b>b</b>). The water runoff originates from springs at higher slopes. Both landslides reached the Colombo–Kandy road, with rock boulders present at site 2 while residual soil was visible along both sides of the debris at site 1.</p>
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<p>Photographs of (<b>a</b>) the failure area at site 1, revealing a soil mass devoid of rock boulders with large trees in the surroundings; (<b>b</b>) the scarp slope in the crown area of landslides at site 2; and (<b>c</b>) a boulder surrounded by boutique construction (site 2).</p>
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<p>Particle size distribution curves of three soil samples taken at each site at the locations marked in <a href="#geotechnics-04-00031-f003" class="html-fig">Figure 3</a>.</p>
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<p>Mineralogical composition of the fine fraction of the soil ((<b>a</b>)—site 1; (<b>b</b>)—site 2). Ch—chlorite; Hy—hypersthene; I—illite; K—kaolinite; Mg—magnetite; Mu—muscovite; P—plagioclase; Q—quartz. The scale does not account for intensity, given the exceptionally high intensity of certain peaks.</p>
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<p>Conceptual model of the failure mechanisms at site 1 (<b>a</b>) and site 2 (<b>b</b>). Slope failure is caused by a lubrication effect in combination with increasing pore pressure due to water infiltrating the boundary between residual soil and weathered bedrock. At site 2, the tensile cracks allow for fast water migration towards the weathered bedrock, where the increased pore pressure along the cohesionless cracks and boulder–soil interfaces reduce the normal load to initiate failure.</p>
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15 pages, 4295 KiB  
Article
Comparative Analysis of Non-Pneumatic Tire Spoke Designs for Off-Road Applications: A Smoothed Particle Hydrodynamics Perspective
by Charanpreet Sidhu and Zeinab El-Sayegh
Geotechnics 2024, 4(2), 549-563; https://doi.org/10.3390/geotechnics4020030 - 5 Jun 2024
Viewed by 694
Abstract
This study explores the development of a terramechanics-based model for non-pneumatic tire–terrain interaction, focusing on different spoke designs. The research investigates how four spoke shapes (honeycomb, modified honeycomb, re-entrant honeycomb, and straight spokes) affect non-pneumatic tire performance in off-road conditions. Using the finite [...] Read more.
This study explores the development of a terramechanics-based model for non-pneumatic tire–terrain interaction, focusing on different spoke designs. The research investigates how four spoke shapes (honeycomb, modified honeycomb, re-entrant honeycomb, and straight spokes) affect non-pneumatic tire performance in off-road conditions. Using the finite element method (FEM) to model non-pneumatic tires, and smoothed-particle hydrodynamics (SPH) to model dry, loose soil, simulations were conducted to replicate real-world loading conditions. This study utilizes virtual environment solution finite element analysis software to examine the interaction between a non-pneumatic tire and dry, loose soil, with a focus on calculating longitudinal and vertical forces. These forces play a pivotal role in determining the motion resistance coefficient. The results show distinct variations in the motion-resistance coefficients among the spoke designs on dry, loose soil. This analysis helps to identify the spoke configurations that optimize energy efficiency and fuel consumption. By comparing and evaluating the four spoke designs, this study shows the effect of spoke design on tire motion resistance. This study concluded that the modified honeycomb spoke design is the most stable and the least sensitive to operating conditions. Full article
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Figure 1
<p>Bald non-pneumatic tire assembly.</p>
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<p>Different non-pneumatic spoke tire designs. (<b>a</b>) Honeycomb. (<b>b</b>) Modified honeycomb. (<b>c</b>) Re-entrant honeycomb. (<b>d</b>) Straight spokes conventional Tweel.</p>
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<p>FEM to SPH conversion for a square structure. (<b>a</b>) FEM mesh. (<b>b</b>) Element center nodes. (<b>c</b>) Mesh-free particles.</p>
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<p>Simulation setup of the pressure–sinkage test.</p>
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<p>Measured and simulated results for dry, loose soil during a pressure–sinkage test.</p>
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<p>Direct shear-strength test simulation setup.</p>
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<p>Shear-strength relationship of measured and simulated test for dry, loose soil.</p>
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<p>Three-dimensional mesh representing the interaction between non-pneumatic tires and SPH dry, loose soil bin, conducted to assess motion resistance (refer to the online version for color illustrations).</p>
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<p>Soil density change for a modified honeycomb tire model running at 4.2 m/s (<b>a</b>) 2 kN (<b>b</b>) 4 kN and (<b>c</b>) 6 kN.</p>
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<p>Motion resistance coefficient test as a function of normal force at different operating velocities for (<b>a</b>) honeycomb spokes, (<b>b</b>) modified honeycomb, (<b>c</b>) re-entrant honeycomb, (<b>d</b>) straight spoke Tweel (refer to the online version for color illustrations).</p>
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<p>Tire penetration under of normal force of 6 kN and 5.5 m/s longitudinal velocities: (<b>a</b>) honeycomb spokes, (<b>b</b>) modified honeycomb, (<b>c</b>) re-entrant honeycomb, (<b>d</b>) straight spoke Tweel (refer to the online version for color illustrations).</p>
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19 pages, 7311 KiB  
Article
Derivation of Contour Plots for the Characterization of the Behaviour of Sand under Undrained Loading
by Jann-Eike Saathoff and Martin Achmus
Geotechnics 2024, 4(2), 530-548; https://doi.org/10.3390/geotechnics4020029 - 4 Jun 2024
Viewed by 739
Abstract
The soil response under the inherent cyclic loading conditions when dealing with offshore foundations can be considered by using contour plots. These plots are derived from several cyclic laboratory tests and characterize the general cyclic soil behaviour. In the design process with explicit [...] Read more.
The soil response under the inherent cyclic loading conditions when dealing with offshore foundations can be considered by using contour plots. These plots are derived from several cyclic laboratory tests and characterize the general cyclic soil behaviour. In the design process with explicit numerical methods, such plots are needed in order to assess the soil behaviour under arbitrary loading conditions and hence estimate the cyclic foundation response. In the paper, excess pore pressure contour plots for a poorly graded medium sand are derived from numerous constant volume (CV) cyclic direct simple shear (DSS) tests and a new approach for parametrization of the plots is presented. Subsequently, the data are assessed regarding scaling for other sand soils, i.e., construction of contour plots with only a small number of test results by using the general trends observed. Full article
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Figure 1
<p>Definitions in cyclic element tests (example with <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>σ</mi> </mrow> <mrow> <mn>0</mn> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> kPa, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>c</mi> <mi>y</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> kPa): (<b>a</b>) excess pore pressure over time, (<b>b</b>) shear stress over time.</p>
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<p>Explanation of the way to derive contour plots for excess pore pressure ratio from a series of cyclic laboratory tests by regression analysis (following Andersen (2015) [<a href="#B19-geotechnics-04-00029" class="html-bibr">19</a>]): (<b>a</b>) test results, (<b>b</b>) derivation of regression curves.</p>
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<p>Grain size distribution (<b>a</b>) and microscopic image of particles (<b>b</b>) of reference soil S30T.</p>
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<p>Vertical effective stress against shear stress (<b>a</b>) and excess pore pressure over number of applied cycles (<b>b</b>) for a load-controlled constant-volume cyclic direct simple shear test for a relative density of 85% and a CSR of 0.084 (MSR = 0).</p>
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<p>Shear stress over shear strain for the first 500 cycles (<b>a</b>) and shear strain over number of cycles (<b>b</b>) for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.85, CSR = 0.084 and MSR = 0.</p>
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<p>Cyclic response of S30T sand showing excess pore pressure ratio (<b>a</b>,<b>d</b>,<b>g</b>), cyclic shear strain (<b>b</b>,<b>e</b>,<b>h</b>) over number of cycles and the shear stress over the vertical stress (<b>c</b>,<b>f</b>,<b>i</b>) with MSR = 0 (<b>a</b>–<b>c</b>), MSR = 0.05 (<b>d</b>–<b>f</b>) and MSR = 0.10 (<b>g</b>–<b>i</b>) for different CSR values and a relative density of 85%.</p>
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<p>Excess pore pressure ratio over number of cycles for S30T sand with different relative densities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.40 (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.50 (<b>b</b>) and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.60 (<b>c</b>)) for MSR = 0.</p>
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<p>Contour plots of the reference soil S30T with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.85 based on Equations (4)–(6) for MSR = 0: (<b>a</b>) Equation (4), (<b>b</b>) Equation (5), (<b>c</b>) Equation (6). Circles show DSS test results.</p>
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<p>Regression of fitting parameter over normalized excess pore pressure ratio for parameters <math display="inline"><semantics> <mrow> <mi>a</mi> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <mi>b</mi> </mrow> </semantics></math> (<b>b</b>) of Equation (7) for MSR = 0. Circles show values from distinct test results, blue lines show the fitting functions.</p>
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<p>Contour plot of the reference soil S30T with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.85 based on Equation (7) for MSR = 0. Circles show DSS test results.</p>
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<p>Excess pore pressure ratio contour plots of S30T sand at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.85 for MSR = 0.00 (<b>a</b>), MSR = 0.05 (<b>b</b>), MSR = 0.10 (<b>c</b>) and MSR = 0.15 (<b>d</b>). Circles show DSS test results.</p>
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<p>Excess pore pressure ratio <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>u</mi> </mrow> </msub> </mrow> </semantics></math> (from bottom to top: 0.01, 0.05, 0.10, 0.20, 0.50, 0.95) for S30T sand at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.85 over CSR, MSR and number of cycles N. Blue lines: CSR (N) for constant MSR, red dashed lines: CSR (MSR) for constant N.</p>
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<p>Fitting accuracy of excess pore pressure ratio for measured and estimated number of cycles for MSR = 0 (<b>a</b>) and MSR = 0.10 (<b>b</b>) with <span class="html-italic">R</span><sup>2</sup> = 0.49 and <span class="html-italic">R</span><sup>2</sup> = 0.89, respectively.</p>
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<p>Normalized liquefaction curves proposed from Andersen (2015) [<a href="#B19-geotechnics-04-00029" class="html-bibr">19</a>] and derived from the tests with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.85 in comparison with results from literature.</p>
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<p>Comparison of number of cycles to liquefaction for different MSR values (<b>a</b>) and <span class="html-italic">C</span><span class="html-italic">S</span><span class="html-italic">R</span><sub><span class="html-italic">N</span>liq=10</sub> over MSR for reference boundary conditions (<b>b</b>) for a relative density of 85%.</p>
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<p>Normalized liquefaction curves proposed from Andersen (2015) [<a href="#B19-geotechnics-04-00029" class="html-bibr">19</a>] and derived from the tests with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.40 (<b>a</b>) and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> = 0.50 and 0.60 (<b>b</b>) in comparison with the literature results.</p>
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<p>Cyclic stress ratio for 10 cycles to liquefaction under symmetrical two-way cyclic loading as a function of relative density for S30T sand under DSS conditions with a vertical stress of 100 kPa and for Baskarp sand according to Andersen (2015) [<a href="#B19-geotechnics-04-00029" class="html-bibr">19</a>].</p>
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18 pages, 2969 KiB  
Article
The Swelling–Shrinkage Properties of Intact and Disturbed Clayey and Marly Soils: The Density Effect
by Lamis Makki, Myriam Duc, Thibault Coppée and Fabien Szymkiewicz
Geotechnics 2024, 4(2), 512-529; https://doi.org/10.3390/geotechnics4020028 - 28 May 2024
Viewed by 711
Abstract
Expansive soils commonly encountered beneath foundations often lead to structural issues inducing expensive repairs. With the increase of the frequency of dry summers and irregular rainfall patterns, the clayey and marly soils become more and more sensitive to shrinking and swelling phenomena. So [...] Read more.
Expansive soils commonly encountered beneath foundations often lead to structural issues inducing expensive repairs. With the increase of the frequency of dry summers and irregular rainfall patterns, the clayey and marly soils become more and more sensitive to shrinking and swelling phenomena. So to find solutions and improve the knowledge on such phenomena especially in temperate countries where the saturation state is considered as the usual soil state, the impact of the soil density on shrinkage was studied by varying the compaction mode and introducing a swelling step before shrinkage. As expected, dynamically or statically compacted clayey or marly soils exhibited high shrinkage deformation when the soil had a low density. The swelling before shrinkage impacted the soil structure but ultimately had a low effect on shrinkage deformation. Swelling deformation was also influenced by density; the denser the soil, the more sensitive the compacted soil became to swelling. Furthermore, compaction modes induced differences in swelling or shrinkage amplitude that couldn’t be explained by microstructural observations. Finally, results demonstrated that intact soil behavior after shrinkage could be extrapolated from swelling–shrinkage tests conducted on remolded soil samples, thus decreasing the cost of field investigations. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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Graphical abstract

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<p>Position of the initial states of tested GC and BM samples. (<b>A</b>) Unsaturated samples after dynamic compaction “the initial moisture is constant for each sample series”. (<b>B</b>) Initially saturated and unsaturated samples after static compaction.</p>
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<p>The free-swelling curves versus time on (<b>1</b>) green clay (GC-W) and (<b>2</b>) blue marl (BM-W) after W-disturbance and static compaction at various dry densities.</p>
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<p>Evolution of B and G parameters with dry density.</p>
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<p>Typical shrinkage curve on a macroporous soil (The shrinkage curve is in red color while the dashed line identifies the remarkable points on the curve) [<a href="#B39-geotechnics-04-00028" class="html-bibr">39</a>,<a href="#B40-geotechnics-04-00028" class="html-bibr">40</a>].</p>
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<p>Effect of the dry density (g/cm<sup>3</sup>) on the shrinkage curve of W-disturbed (statically compacted) GC and BM samples: (<b>1</b>) initially saturated samples (initial moisture content varies), (<b>2</b>) initially unsaturated samples (samples are prepared at similar initial water content), and (<b>3</b>) after a free-swelling test on initially unsaturated samples. The saturation line (black line) is calculated with a solid density <span class="html-italic">ρ<sub>s</sub></span> = 2.7 g/cm<sup>3</sup>. The color scale from cold (blue) to hot (red) corresponds to a decreasing soil density.</p>
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<p>Pore size distribution (PSD) of intact (I) and W-disturbed BM and GC at different densities (static compaction) at the end of a direct shrinkage test after drying at 105 °C. (<b>1</b>) Initially saturated samples (initial moisture content varies), (<b>2</b>) initially unsaturated samples (samples are prepared at similar initial moisture), and (<b>3</b>) after a free-swelling test on initially unsaturated samples. The color or grey levels make easier the curves distinction.</p>
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<p>Pore size distribution (PSD) of intact (I) and W-disturbed BM and GC at different densities (static compaction) at the end of a direct shrinkage test after drying at 105 °C. (<b>1</b>) Initially saturated samples (initial moisture content varies), (<b>2</b>) initially unsaturated samples (samples are prepared at similar initial moisture), and (<b>3</b>) after a free-swelling test on initially unsaturated samples. The color or grey levels make easier the curves distinction.</p>
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<p>Relationship between the dry density and the initial and final void ratios after shrinkage on GC-W and BM-W disturbed samples. (<b>1</b>) Shrinkage on statically compacted and initially saturated samples. (<b>2</b>) or (<b>3</b>) Shrinkage with or without an initial swelling step on statically compacted and initially unsaturated samples. (<b>4</b>) Shrinkage on dynamically compacted and initially unsaturated samples. The solid lines correspond to tendencies drawn from experimental points. The tendencies for BM (<b>1</b>) and GC (<b>1</b>) were drawn with dashed lines for easier comparison with the BM or GC (<b>2</b>) to (<b>4</b>) behaviors.</p>
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<p>Photos of W-disturbed compacted samples compared with an (I) intact sample of green clay (GC) and blue marl (BM).</p>
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<p>Variation of ∆H/H, ∆D/D and the ratio R = (∆V/V)/(∆H/H) versus water content during the shrinkage test on W-BM (<b>2</b>) and W-GC (<b>2</b>) samples, as well as on intact BM and GC soils.</p>
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<p>Swelling tests followed by shrinkage tests on W- and C-disturbed GC and BM soils prepared at different densities by dynamic compaction. Blue and red colors refer, respectively, to specimen prepared at initial water content equal to 27% and 30%. Dark and light colors correspond to, respectively, C and W disturbance; <span class="html-italic">e<sub>air,i</sub></span> refers to the void ratio occupied by air at initial state. On the right, GC measurements points were drawn in grey to help the comparison with BM. The colored dashed lines correspond to the trend of initial states and the black dashed lines to extrapolation at low <span class="html-italic">e<sub>air,i</sub></span> values of the colored trending curves.</p>
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13 pages, 5124 KiB  
Article
The Effect of Clay Infill on Strength of Jointed Sandstone: Laboratory and Analysis
by Chen Cui and Ivan Gratchev
Geotechnics 2024, 4(2), 499-511; https://doi.org/10.3390/geotechnics4020027 - 25 May 2024
Viewed by 727
Abstract
The strength of jointed rock is a fundamental factor in the slope stability of rock mass. This research investigates the effect of infill thickness on the strength of jointed rock specimens. Unlike previous studies involving artificial rock-like materials and saw-tooth surfaces, this work [...] Read more.
The strength of jointed rock is a fundamental factor in the slope stability of rock mass. This research investigates the effect of infill thickness on the strength of jointed rock specimens. Unlike previous studies involving artificial rock-like materials and saw-tooth surfaces, this work has been conducted on two natural types of sandstone with various rock surfaces. Natural low-plasticity clay of different thicknesses (1 mm to 3 mm) was used as the infill material. A series of shear box tests with a range of initial normal stresses from 0.5 MPa to 1.5 MPa were performed to obtain high-quality data regarding the shear strength of natural rock and to provide insights into the effect of infill and rock surface roughness on shear strength. The obtained results were also used to improve the current methods of rock strength predictions, which were initially designed to estimate the strength of artificial rock-like material. Based on the obtained laboratory data and the strength estimation using different methods, a newly proposed procedure was proved to provide more accurate estimations of the shear strength of jointed rock. Full article
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<p>Cross-section (<b>a</b>) saw-tooth model with infill, (<b>b</b>) natural core rock joint.</p>
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<p>Rock sample: (<b>a</b>) Sandstone 1, (<b>b</b>) Sandstone 2.</p>
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<p>X-ray diffraction (XRD) for two sandstones: (<b>a</b>) Sandstone 1, (<b>b</b>) Sandstone 2.</p>
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<p>Experiment set up and specimens: (<b>a</b>) half test specimen, (<b>b</b>) whole test specimen.</p>
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<p>S1 shear strength vs. displacement with different thicknesses of infill (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> = 0.5 MPa, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> = 1.0 MPa.</p>
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<p>S2 shear strength vs. displacement with different thicknesses of infill (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo> </mo> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> = 1.5 MPa.</p>
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<p>Shear strength vs. different infill thickness: (<b>a</b>) Sandstone 1, (<b>b</b>) Sandstone 2.</p>
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<p>Experiment data and the theoretical results: (<b>a</b>) S1, (<b>b</b>) S2.</p>
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<p>Tested result and the modified result (<b>a</b>) S1 (<b>b</b>) S2.</p>
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29 pages, 6138 KiB  
Article
An Investigation into the Utility of Large Language Models in Geotechnical Education and Problem Solving
by Liuxin Chen, Amir Tophel, Umidu Hettiyadura and Jayantha Kodikara
Geotechnics 2024, 4(2), 470-498; https://doi.org/10.3390/geotechnics4020026 - 9 May 2024
Cited by 1 | Viewed by 1281
Abstract
The study explores the capabilities of large language models (LLMs), particularly GPT-4, in understanding and solving geotechnical problems, a specialised area that has not been extensively examined in previous research. Employing a question bank obtained from a commonly used textbook in geotechnical engineering, [...] Read more.
The study explores the capabilities of large language models (LLMs), particularly GPT-4, in understanding and solving geotechnical problems, a specialised area that has not been extensively examined in previous research. Employing a question bank obtained from a commonly used textbook in geotechnical engineering, the research assesses GPT-4’s performance across various topics and cognitive complexity levels, utilising different prompting strategies like zero-shot learning, chain-of-thought (CoT) prompting, and custom instructional prompting. The study reveals that while GPT-4 demonstrates significant potential in addressing fundamental geotechnical concepts and problems, its effectiveness varies with specific topics, the complexity of the task, and the prompting strategies employed. The paper categorises errors encountered by GPT-4 into conceptual, grounding, calculation, and model inherent deficiencies related to the interpretation of visual information. Custom instructional prompts, specifically tailored to address GPT-4’s shortcomings, significantly enhance its performance. The study reveals that GPT-4 achieved an overall problem-solving accuracy of 67% with custom instructional prompting, significantly higher than the 28.9% with zero-shot learning and 34% with CoT. However, the study underscores the importance of human oversight in interpreting and verifying GPT-4’s outputs, especially in complex, higher-order cognitive tasks. The findings contribute to understanding the potential and limitations of current LLMs in specialised educational fields, providing insights for educators and researchers in integrating AI tools like GPT-4 into their teaching and problem-solving approaches. The study advocates for a balanced integration of AI in education to enrich educational delivery and experience while emphasising the indispensable role of human expertise alongside technological advancements. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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<p>Topic-Wise Distribution of Our Question Bank.</p>
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<p>The Difficulty Level of the Questions in Our Data Bank According to the Revised Bloom’s Taxonomy Proposed by Krathwohl [<a href="#B41-geotechnics-04-00026" class="html-bibr">41</a>].</p>
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<p>The Difficulty Level of The Questions in Our Data Bank According to The Revised Bloom’s Taxonomy Proposed by Krathwohl [<a href="#B41-geotechnics-04-00026" class="html-bibr">41</a>].</p>
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<p>The Overall Accuracy of Each Prompting Strategy with Various Topics.</p>
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<p>The Overall Accuracy of Each Prompting Strategy with Various Topics.</p>
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<p>The Overall Accuracy of Each Prompting Strategy with Various Bloom’s Levels.</p>
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<p>Accuracy of Each Prompting Strategy with Different Question Types.</p>
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<p>Error Types Across Various Prompting Strategies with GPT-4.</p>
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<p>Comparative Heatmap Analysis of Error Types Across Chapters for Different Prompting Strategies.</p>
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<p>Comparative Heatmap Analysis of Error Types across Bloom’s Taxonomy Levels for Different Prompting Strategies.</p>
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23 pages, 8658 KiB  
Article
Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments
by Thanh T. Nguyen, Viet D. Le, Thien Q. Huynh and Nhu H.T. Nguyen
Geotechnics 2024, 4(2), 447-469; https://doi.org/10.3390/geotechnics4020025 - 3 May 2024
Cited by 1 | Viewed by 942
Abstract
Understanding the role that settlement can have on the base resistance of piles is a crucial matter in the design and safety control of deep foundations under various buildings and infrastructure, especially for long to super-long piles (60–90 m length) in soft soil. [...] Read more.
Understanding the role that settlement can have on the base resistance of piles is a crucial matter in the design and safety control of deep foundations under various buildings and infrastructure, especially for long to super-long piles (60–90 m length) in soft soil. This paper presents a novel assessment of this issue by applying explainable machine learning (ML) techniques to a robust database (1131 datapoints) of fully instrumented pile tests across 37 real-life projects in the Mekong Delta. The analysis of data based on conventional methods shows distinct responses of long piles to rising settlement, as compared to short piles. The base resistance can rapidly develop at a small settlement threshold (0.015–0.03% of pile’s length) and contribute up to 50–55% of the total bearing capacity in short piles, but it slowly rises over a wide range of settlement to only 20–25% in long piles due to considerable loss of settlement impact over the depth. Furthermore, by leveraging the advantages of ML methods, the results significantly enhance our understanding of the settlement–base resistance relationship through explainable computations. The ML-based prediction method is compared with popular practice codes for pile foundations, further attesting to the high accuracy and reliability of the newly established model. Full article
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<p>Contributions of base and shaft resistances to the total bearing capacity of piles in 3 different cases: (<b>a</b>) Case 1, where the base resistance is predominant; (<b>b</b>) Case 2, where the shaft resistance is predominant; and (<b>c</b>) Case 3, where both the base and shaft resistances are significant contributors to the total bearing capacity.</p>
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<p>(<b>a</b>) Location of the investigated projects relative to the Mekong River delta, and (<b>b</b>) typical geological profiles near the Mekong River.</p>
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<p>Distribution of the tested pile’s primary features: (<b>a</b>) the embedded length and equivalent diameter, (<b>b</b>) the maximum applied load and settlement, (<b>c</b>) the SPT value of soil beneath the pile toe.</p>
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<p>Contribution of base resistance to the total bearing capacity of short and medium piles in comparison with hyperbolic model: (<b>a</b>) previous studies [<a href="#B16-geotechnics-04-00025" class="html-bibr">16</a>,<a href="#B29-geotechnics-04-00025" class="html-bibr">29</a>,<a href="#B30-geotechnics-04-00025" class="html-bibr">30</a>,<a href="#B33-geotechnics-04-00025" class="html-bibr">33</a>]; and (<b>b</b>) combined previous studies and the O-cell test data in the current study.</p>
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<p>Base resistance of long piles in comparison with short and medium piles.</p>
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<p>Two different stages in the development of base resistance over increasing settlement in long piles (Le ≥ 60 m).</p>
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<p>Model implementation using the collected database: (<b>a</b>) Random Forest; and (<b>b</b>) XGBoost.</p>
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<p>Performance of XGBoost and Random Forest (RF) models through 10-fold cross validation using 85% of the original dataset: (<b>a</b>) coefficient of determination; and (<b>b</b>) root-mean-squared error.</p>
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<p>Coefficient of determination (R<sup>2</sup>) of model predictions by: (<b>a</b>) XGBoost; and (<b>b</b>) Random Forest.</p>
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<p>Assessment of influencing factors on base resistance <span class="html-italic">Q<sub>b</sub></span> using SHAP method: (<b>a</b>,<b>b</b>) XGBoost; (<b>c</b>,<b>d</b>) Random Forest (RF).</p>
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<p>Assessment of influencing factors on base resistance <span class="html-italic">Q<sub>b</sub></span> using SHAP method: (<b>a</b>,<b>b</b>) XGBoost; (<b>c</b>,<b>d</b>) Random Forest (RF).</p>
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<p>Influence of settlement on base resistance predicted through machine learning assessment (partial dependence plot PDP): (<b>a</b>) XGBoost; and (<b>b</b>) Random Forest.</p>
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<p>Influence of settlement on base resistance predicted through machine learning assessment (partial dependence plot PDP): (<b>a</b>) XGBoost; and (<b>b</b>) Random Forest.</p>
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<p>Individual effect of settlement on base resistance <span class="html-italic">Q<sub>b</sub></span> through machine learning assessment (individual conditional expectation (ICE) plots): (<b>a</b>) XGBoost; and (<b>b</b>) Random Forest.</p>
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<p>Individual effect of settlement on base resistance <span class="html-italic">Q<sub>b</sub></span> through machine learning assessment (individual conditional expectation (ICE) plots): (<b>a</b>) XGBoost; and (<b>b</b>) Random Forest.</p>
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<p>Contribution of the base resistance to total bearing capacity with settlement normalised over the pile length [<a href="#B16-geotechnics-04-00025" class="html-bibr">16</a>,<a href="#B29-geotechnics-04-00025" class="html-bibr">29</a>,<a href="#B30-geotechnics-04-00025" class="html-bibr">30</a>,<a href="#B33-geotechnics-04-00025" class="html-bibr">33</a>].</p>
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<p>Comparison of trend line given by ML (XGBoost) model and field test datapoints for short and long piles.</p>
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<p>Comparison of machine learning-based predictions incorporating the influence of settlement, and conventional practice methods: (<b>a</b>) 3D distribution of <span class="html-italic">Q<sub>b</sub></span> in relation to <span class="html-italic">S</span> and SPT; and (<b>b</b>) percentage error of prediction.</p>
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17 pages, 9979 KiB  
Article
Measurements of Shear Wave Velocity for Collapsible Soil
by Omar EI-Shafee, Inthuorn Sasanakul, Tarek Abdoun and Mourad Zeghal
Geotechnics 2024, 4(2), 430-446; https://doi.org/10.3390/geotechnics4020024 - 28 Apr 2024
Viewed by 784
Abstract
This paper examines the effects of collapsible soil structure on shear wave velocity. The study attempts to simulate hydraulic fill sand deposits, which represent a natural soil deposition process that can result in a collapsible soil structure. A series of resonant column tests [...] Read more.
This paper examines the effects of collapsible soil structure on shear wave velocity. The study attempts to simulate hydraulic fill sand deposits, which represent a natural soil deposition process that can result in a collapsible soil structure. A series of resonant column tests and bender element tests on Ottawa sand was conducted on sand specimens and prepared by dry pluviation and simulated hydraulic fill methods subjected to various confining pressures. Shear wave velocities measured from both methods of deposition are compared and discussed. Results from this study show that for soil specimens with the same void ratio, samples prepared by simulated hydraulic fill have a lower shear modulus and shear wave velocity than the specimens prepared by dry pluviation, and the differences are more pronounced at higher confining pressures. The resonant column test results performed in this study were consistent with results from the discrete element analysis, full-scale testing, and centrifuge testing. The discrete element analysis suggests that soil fabric and number of particle contacts are the key factors affecting the shear wave velocity. These factors are dependent on the methods of deposition. Results from this study examining hydraulic fill collapsible structure shear wave velocity provide a step forward toward a better correlation between soil dynamic properties measured in field and laboratory tests. Full article
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<p>Shear wave velocity measured from a full-scale test prepared by hydraulic fill and a centrifuge test prepared by dry pluviation (modified from [<a href="#B22-geotechnics-04-00024" class="html-bibr">22</a>]).</p>
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<p>Microscopic images of Ottawa sand particles; (<b>a</b>) general view of Ottawa sand particles; (<b>b</b>) detailed view of dispersed Ottawa sand particles [<a href="#B23-geotechnics-04-00024" class="html-bibr">23</a>].</p>
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<p>Gradation curve of Ottawa Sand F#55 [<a href="#B12-geotechnics-04-00024" class="html-bibr">12</a>].</p>
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<p>Modified mold for the simulated hydraulic fill preparation [<a href="#B23-geotechnics-04-00024" class="html-bibr">23</a>].</p>
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<p>Simulated hydraulic fill procedure for 5.5” sample [<a href="#B23-geotechnics-04-00024" class="html-bibr">23</a>].</p>
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<p>Modulus reduction (<b>a</b>) and damping curves (<b>b</b>) for Ottawa sand with a void ratio of 0.6 at confining pressures (15, 30, and 60 kPa) [<a href="#B28-geotechnics-04-00024" class="html-bibr">28</a>].</p>
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<p>Modulus reduction (<b>a</b>) and damping curves (<b>b</b>) for Ottawa sand with a void ratio of 0.77 at confining pressures (15, 30, and 60 kPa) [<a href="#B28-geotechnics-04-00024" class="html-bibr">28</a>].</p>
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<p>Comparison of Vs measured by resonant column and bender element tests.</p>
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<p>Comparison of <span class="html-italic">V<sub>s</sub></span> for dry pluviation with similar results from [<a href="#B16-geotechnics-04-00024" class="html-bibr">16</a>].</p>
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<p>Comparison of <span class="html-italic">V<sub>s</sub></span> from dry pluviation and simulated hydraulic fill from [<a href="#B16-geotechnics-04-00024" class="html-bibr">16</a>].</p>
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<p>Comparison of shear modulus for dry pluviation for saturated and dry samples from [<a href="#B16-geotechnics-04-00024" class="html-bibr">16</a>].</p>
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<p>Comparison of shear modulus for dry pluviation and simulated hydraulic fill from [<a href="#B16-geotechnics-04-00024" class="html-bibr">16</a>].</p>
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<p>Variation of the low-strain shear modulus of two synthetic soil samples [<a href="#B31-geotechnics-04-00024" class="html-bibr">31</a>].</p>
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<p>Variation of normalized shear modulus and the damping of two synthetic soil samples [<a href="#B31-geotechnics-04-00024" class="html-bibr">31</a>].</p>
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<p>Correlation of shear modulus and number of particle contact, <span class="html-italic">M<sub>n</sub></span> for synthetic soils with different fabrics [<a href="#B31-geotechnics-04-00024" class="html-bibr">31</a>].</p>
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<p>Effect of preshaking on shear wave velocity of simulated hydraulic soil specimen [<a href="#B16-geotechnics-04-00024" class="html-bibr">16</a>].</p>
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15 pages, 2998 KiB  
Article
Improving Strength by Increased Compaction of Gypsum—Enriched Soil under Long-Term Soaking Conditions
by Sabah Said Razouki and Dina Kuttah
Geotechnics 2024, 4(2), 415-429; https://doi.org/10.3390/geotechnics4020023 - 23 Apr 2024
Cited by 1 | Viewed by 669
Abstract
This study investigated the effect of compaction effort and soaking time on the shear strength properties of fine-grained gypsum-containing soils. The objective was to demonstrate that increasing compaction effort increases soil strength, specifically cohesion and the angle of shear strength, when subjected to [...] Read more.
This study investigated the effect of compaction effort and soaking time on the shear strength properties of fine-grained gypsum-containing soils. The objective was to demonstrate that increasing compaction effort increases soil strength, specifically cohesion and the angle of shear strength, when subjected to soaking in freshwater. Unconsolidated undrained triaxial tests were carried out on CBR soil samples with different soaking times. The results showed a transition from brittle to ductile failure behaviour as the soaking time increased. Mohr–Coulomb failure envelopes showed reduced cohesion and angle of shear strength with increasing soak time. Regression models were developed to establish correlations between soaked and unsoaked strength parameters. Strong relationships were found between soil strength properties, compaction effort and soaking time. Empirical equations were proposed to estimate the cohesion and angle of shear strength from compaction effort and soaking time. This study highlighted the importance of considering gypsum-rich soils in civil engineering design. Gypsum dissolution during wetting significantly affected soil strength parameters. The regression models and empirical equations provide engineers with tools to assess the influence of compaction effort and soaking time on soil strength, thus aiding decision making when designing structures on gypsum-rich soils. Full article
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<p>Deviator stress versus axial strain for unsoaked triaxial samples compacted at 3421 kN m/m<sup>3</sup> (corresponding to 70 blows/layer in CBR mould).</p>
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<p>The failure mode of unsoaked triaxial samples for the case corresponding to compaction effort of 56 blows/layer.</p>
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<p>Effect of compaction effort on failure mode of triaxial samples soaked for 120 days.</p>
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<p>Mohr circles for the four compaction efforts corresponding to 12, 25, 56 and 70 blows/layer together with the corresponding Mohr–Coulomb failure envelopes.</p>
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<p>Time variation in cohesion with soaking period for each compaction effort.</p>
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<p>Time variation in angle of shear strength with soaking period for each compaction effort.</p>
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<p>A three-dimensional presentation for the cohesion ratio versus soaking period and compaction effort for laboratory measurement and empirical modelling data.</p>
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<p>A three-dimensional presentation for the friction angle ratio versus soaking period and compaction effort for laboratory measurement and empirical modelling data.</p>
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16 pages, 8067 KiB  
Article
Application of Electrical Resistivity Tomography in Geotechnical and Geoenvironmental Engineering Aspect
by Md Jobair Bin Alam, Asif Ahmed and Md Zahangir Alam
Geotechnics 2024, 4(2), 399-414; https://doi.org/10.3390/geotechnics4020022 - 4 Apr 2024
Cited by 1 | Viewed by 2044
Abstract
Electrical resistivity tomography (ERT) has turned out to be one of the most applied and user-friendly geophysical methods in geotechnical and geoenvironmental research. ERT is an emerging technology that is becoming popular nowadays for investigating subsurface conditions. Multiple attributes of the technology using [...] Read more.
Electrical resistivity tomography (ERT) has turned out to be one of the most applied and user-friendly geophysical methods in geotechnical and geoenvironmental research. ERT is an emerging technology that is becoming popular nowadays for investigating subsurface conditions. Multiple attributes of the technology using various electrode configurations significantly reduce measurement time and are suitable for applications even in hardly accessible mountain areas. It is a noninvasive test for subsurface characterization and a very sensitive method used to determine geophysical properties, i.e., structural integrity, water content, fluid composition, etc. This paper aimed to elucidate the ERT technique’s main features and applications in geotechnical and geoenvironmental engineering through four case studies. The first case study investigated the possible flow paths and areas of moisture accumulation after leachate recirculation in a bioreactor landfill. The second case study attempted to determine the moisture variation along highway pavement. The third case study explored the slope failure investigation by ERT. The fourth case study demonstrated the efficiency of the ERT method in the landfill evapotranspiration (ET) cover to investigate moisture variation on a broader scale and performance monitoring. In all of the four cases, ERT exhibited promising performance. Full article
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<p>Variations in subsurface electric current density (redrawn from [<a href="#B3-geotechnics-04-00022" class="html-bibr">3</a>]). At least four electrodes are required for electrical resistivity measurement. A, B are the current electrodes (through which electricity passes) whereas M, N are the potential electrodes (through which potential differences are measured), and V is the voltage.</p>
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<p>Relation between resistance and resistivity (redrawn from [<a href="#B1-geotechnics-04-00022" class="html-bibr">1</a>]).</p>
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<p>Equipotential and current lines for a pair of current electrodes (redrawn from [<a href="#B32-geotechnics-04-00022" class="html-bibr">32</a>]). A and B are the current electrodes, and M and N are the potential electrodes, and V is the voltage.</p>
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<p>Different array configuration for 2D resistivity tomography (redrawn from [<a href="#B2-geotechnics-04-00022" class="html-bibr">2</a>]). A,B are current electrodes; M, N are potential electrodes; ‘a’ is the distance between adjacent electrodes; ‘n’ is the ratio of the distance between the current and potential electrodes.</p>
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<p>Data interpretation in EarthImager 2D software.</p>
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<p>The authors conducting resistivity tomography at the site.</p>
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<p>Resistivity tomography results of pipe H2.</p>
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<p>(<b>a</b>) ERT experiment on pavement slope; (<b>b</b>) schematic of the location.</p>
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<p>Monthly resistivity plots: (<b>a</b>) higher resistivity; (<b>b</b>) low resistivity; (<b>c</b>) moisture intrusion.</p>
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<p>(<b>a</b>) Typical dry period (2016–17); (<b>b</b>) typical wet period (2016–17).</p>
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<p>Change in resistivity with rainfall at (<b>a</b>) 0.91 m (2 feet) and (<b>b</b>) 3.05 m (10 feet).</p>
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<p>(<b>a</b>) Cracks on the shoulder; (<b>b</b>) failure at the crest of the slope.</p>
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<p>(<b>a</b>) Failure condition of SH 183; (<b>b</b>) resistivity lines along the slope.</p>
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<p>(<b>a</b>) Test results of the resistivity line RL-1 (at crest); (<b>b</b>) test results of the resistivity line RL-2 (at the middle of the slope).</p>
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<p>(<b>a</b>) Field setup with resistivity meter; (<b>b</b>) execution of ERT test.</p>
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<p>Resistivity tomography results of lysimeter-2.</p>
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17 pages, 2058 KiB  
Article
Assessment of Bayesian Changepoint Detection Methods for Soil Layering Identification Using Cone Penetration Test Data
by Stephen K. Suryasentana, Brian B. Sheil and Myles Lawler
Geotechnics 2024, 4(2), 382-398; https://doi.org/10.3390/geotechnics4020021 - 4 Apr 2024
Viewed by 841
Abstract
This paper assesses the effectiveness of different unsupervised Bayesian changepoint detection (BCPD) methods for identifying soil layers, using data from cone penetration tests (CPT). It compares four types of BCPD methods: a previously utilised offline univariate method for detecting clay layers through undrained [...] Read more.
This paper assesses the effectiveness of different unsupervised Bayesian changepoint detection (BCPD) methods for identifying soil layers, using data from cone penetration tests (CPT). It compares four types of BCPD methods: a previously utilised offline univariate method for detecting clay layers through undrained shear strength data, a newly developed online univariate method, and an offline and an online multivariate method designed to simultaneously analyse multiple data series from CPT. The performance of these BCPD methods was tested using real CPT data from a study area with layers of sandy and clayey soil, and the results were verified against ground-truth data from adjacent borehole investigations. The findings suggest that some BCPD methods are more suitable than others in providing a robust, quick, and automated approach for the unsupervised detection of soil layering, which is critical for geotechnical engineering design. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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<p>Illustration of a series of data points (shown as grey markers). There are three distinct partitions in the data series, separated by changepoints. The changepoints are detected at locations where abrupt changes are observed.</p>
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<p>Illustration of the development of the most probable <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> </mrow> </semantics></math> for a sequence of data (shown in bottom subfigure) and how the changepoints coincide with the locations where the most probable <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> </mrow> </semantics></math> is 0. Here, <math display="inline"><semantics> <mrow> <mi>y</mi> </mrow> </semantics></math> is the measured quantity and <math display="inline"><semantics> <mrow> <mi>z</mi> </mrow> </semantics></math> is depth (for depth series data). The dashed lines represent the known locations of the changepoints.</p>
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<p>Workflow summarising the steps from the raw CPT measurements to the soil layer boundary predictions by the univariate and multivariate BCPD methods.</p>
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<p>Comparison of the best-fit inverse gamma cumulative distribution with the actual cumulative distribution of the variance in the CPT <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> data within each soil layer (known from the borehole data).</p>
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<p>Comparison of soil layer boundary predictions by the different BCPD methods (shown as horizontal black lines) with the corresponding Robertson (2009) predictions (labelled as ‘ROB’) and ground truth provided by neighbouring borehole data (labelled as ‘BH’), at location CPT01. Fine- and coarse-grained soils are shown as light and dark grey colours, respectively.</p>
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<p>Comparison of soil layer boundary predictions with the borehole data at location CPT02.</p>
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<p>Comparison of soil layer boundary predictions with the borehole data at location CPT03.</p>
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<p>Comparison of soil layer boundary predictions with the borehole data at location CPT04.</p>
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<p>Comparison of soil layer boundary predictions with the borehole data at location CPT05.</p>
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20 pages, 3161 KiB  
Article
Improved Wave Equation Analysis for Piles in Soil-Based Intermediate Geomaterials with LRFD Recommendations and Economic Impact Assessment
by Harish K. Kalauni, Nafis Bin Masud, Kam Ng and Shaun S. Wulff
Geotechnics 2024, 4(2), 362-381; https://doi.org/10.3390/geotechnics4020020 - 1 Apr 2024
Cited by 2 | Viewed by 987
Abstract
The Wave Equation Analysis of Pile Driving (WEAP) has been widely used to determine drivability, predict static resistance, and assure the integrity of piles in soils. Assigning static and dynamic properties of Soil-based Intermediate Geomaterials (S-IGMs) remains a challenge in WEAP, partly attributed [...] Read more.
The Wave Equation Analysis of Pile Driving (WEAP) has been widely used to determine drivability, predict static resistance, and assure the integrity of piles in soils. Assigning static and dynamic properties of Soil-based Intermediate Geomaterials (S-IGMs) remains a challenge in WEAP, partly attributed to IGMs that act as transition geomaterials between soil and hard rock. Furthermore, reliable static analysis methods for unit resistance predictions are rarely available for driven piles in S-IGMs in the default WEAP method. To alleviate these challenges, this study presents improved WEAP methods for steel piles driven in S-IGMs, including proposed damping parameters and Load and Resistance Factor Design (LRFD) recommendations based on newly developed static analysis methods and the classification of S-IGMs. A back calculation approach is used to generate the appropriate damping parameters for S-IGMs for three distinct subsurface conditions utilizing a database of 34 steel H- and pipe piles. Newly developed WEAP and LRFD procedures are also recommended. Additional independent 22 test pile data are used to compare and evaluate the accuracy and efficiency of the proposed WEAP methods with the default WEAP method. Compared with the default WEAP, bearing graph analysis results revealed that the selected proposed WEAP method, on average, reduces the underprediction of pile resistances by 6% and improves the reliability with a 43% reduction in the coefficient of variation (COV). Calibrated resistance factors for the proposed WEAP method increase to as high as 0.75 compared to the current AASHTO recommendation of 0.50. An economic impact assessment reveals that the proposed WEAP method is more efficient than the default WEAP method as the average difference in steel weight for 32 test piles is 0.06 kg/kN, almost close to zero, reducing the construction challenges in the current engineering practice. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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<p>Flowchart to classify the subsurface conditions I, II, and III.</p>
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<p>(<b>a</b>) Relationship between back-calculated damping factor and the product of undrained shear strength and slenderness ratio for FG-IGMs and (<b>b</b>) relationship between damping factor and the ratio of pile penetration to corrected SPT N-value for CG-IGMs.</p>
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<p>Flowchart comparing the four different WEAP methods.</p>
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<p>Comparison of measured pile resistances from CAPWAP and predicted pile resistance from WEAP SAD, WEAP UWD, WEAP SAR, and WEAP UWR for the 34-training pile dataset.</p>
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<p>Comparison of measured pile resistances from CAPWAP and predicted pile resistances from WEAP SAD, WEAP UWD, WEAP SAR, and WEAP UWR for the 22 independent pile data.</p>
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<p>A flow chart showing the methodology for economic impact assessment.</p>
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<p>Plot of the differences in steel weight based on the WEAP SAD and WEAP UWR methods.</p>
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12 pages, 5425 KiB  
Article
An Anchoring Capacity Study Focused on a Wheel’s Curvature Geometry for an Autonomous Underwater Vehicle with a Traveling Function during Contact with Loose Ground Containing Water
by Akira Ofuchi, Daisuke Fujiwara and Kojiro Iizuka
Geotechnics 2024, 4(2), 350-361; https://doi.org/10.3390/geotechnics4020019 - 25 Mar 2024
Viewed by 671
Abstract
The current scallop fishery sector allows many scallops to remain in specified fishing zones, and this process leads to heavy losses in the sector. Scallop fishermen aim to harvest the remaining scallops to reduce their losses. To achieve this, a fisherman must understand [...] Read more.
The current scallop fishery sector allows many scallops to remain in specified fishing zones, and this process leads to heavy losses in the sector. Scallop fishermen aim to harvest the remaining scallops to reduce their losses. To achieve this, a fisherman must understand the scallop ecology on the seafloor. In our previous study, we proposed a method for measuring scallops using wheeled robots. However, a wheeled robot must be able to resist disturbance from the sea to achieve high measurement accuracy. Strong anchoring of wheels against the seafloor is necessary to resist disturbance. To better understand anchoring performance, we confirmed the wheel anchoring capacity in water-containing sand in an experiment. In this experiment, we towed fixed wheels on water-containing sand and measured the resistance force acting between the wheel and the sand. Afterward, we considered the resistance force as the wheel anchoring capacity on the water-containing sand. The experimental results capture the tendency for the anchoring capacity of sand with/without water to increase with sinkage. The results also demonstrate that the anchoring capacity of water-containing sand is lower than that of non-water-containing sand. However, the results indicate that when the wheels possess lugs, their presence tends to increase the wheels’ anchoring capacity in water. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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<p>Scallop physical quantity measurement method involving the use of robots.</p>
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<p>Stress in sand applied to wheels.</p>
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<p>Relationship between sinkage and the wheel on loose soil.</p>
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<p>Experimental procedure for measuring the resistance force. (<b>a</b>) Initial state; (<b>b</b>) rotating state; (<b>c</b>) sinking state; (<b>d</b>) towing state.</p>
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<p>Experimental machine.</p>
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<p>Difference in wheel shape.</p>
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<p>Images of the experiment: (<b>a</b>) initial state; (<b>b</b>) rotating and sinking states; (<b>c</b>) towing state; (<b>d</b>) end state.</p>
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<p>The relationship between the number of rotations and initial sinkage. The vertical axis represents the initial sinkage, and the horizontal axis represents the number of wheel spins. The legend shows the wheel shape. (<b>a</b>) Non-water-containing sand and (<b>b</b>) water-containing sand.</p>
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<p>The relationship between the initial sinkage, experimental towing time, and anchoring capacity on non-water-containing sand. The horizontal axis represents the experimental towing time. The vertical axis represents the anchoring capacity. The legend shows the initial sinking amount. (<b>a</b>) Wheel A; (<b>b</b>) Wheel B; (<b>c</b>) Wheel C; (<b>d</b>) Wheel D.</p>
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<p>The relationship between the initial sinkage, experimental towing time, and anchoring capacity on water-containing sand. The horizontal axis represents the experimental towing time. The vertical axis represents the anchoring capacity. The legend shows the initial sinkage. (<b>a</b>) Wheel A; (<b>b</b>) Wheel B; (<b>c</b>) Wheel C; (<b>d</b>) Wheel D.</p>
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