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Search Results (1,377)

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14 pages, 3937 KiB  
Article
Fragility of Highway Embankments Exposed to Permanent Deformations Due to Underlying Fault Rupture
by Eleni Petala and Nikolaos Klimis
Geosciences 2024, 14(11), 312; https://doi.org/10.3390/geosciences14110312 - 15 Nov 2024
Viewed by 194
Abstract
Seismic risk expresses the expected degree of damage and loss following a catastrophic event. An efficient tool for assessing the seismic risk of embankments is fragility curves. This research investigates the influence of embankment’s geometry, the depth of rupture occurrence, and the underlying [...] Read more.
Seismic risk expresses the expected degree of damage and loss following a catastrophic event. An efficient tool for assessing the seismic risk of embankments is fragility curves. This research investigates the influence of embankment’s geometry, the depth of rupture occurrence, and the underlying sandy soil’s conditions on the embankment’s fragility. To achieve this, the response of three highway embankments resting on sandy soil was examined through quasi-static parametric numerical analyses. For the establishment of fragility curves, a cumulative lognormal probability distribution function was used. The maximum vertical displacement of the embankments’ external surface and the fault displacement were considered as the damage indicator and the intensity measure, respectively. Damage levels were categorized into three qualitative thresholds: minor, moderate, and extensive. All fragility curves were generated for normal and reverse faults, as well as the combination of those fault types (dip-slip fault). Finally, the proposed curves were verified via their comparison with those provided by HAZUS. It was concluded that embankment geometry and depth of fault rupture appearance do not significantly affect fragility, as exceedance probabilities show minimal differences (<4%). However, an embankment founded on dense sandy soil reveals slightly higher fragility compared to the one founded on loose sand. Differences regarding the probability of exceedance of a certain damage level are restricted by a maximum of 7%. Full article
(This article belongs to the Section Natural Hazards)
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<p>Model’s presentation for normal and reverse fault disruption.</p>
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<p>Typical mesh and shear strain increments of the model (<b>a</b>) before and (<b>b</b>) after fault rupture propagation.</p>
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<p>Dispersion plots of ln(δy<sub>max</sub>) − ln(d) and fitting curves for EmbA exposed to (<b>a</b>) normal, (<b>b</b>) reverse, and (<b>c</b>) dip-slip fault rupture propagation.</p>
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<p>Fragility curve for three highway embankments exposed to (<b>a</b>) normal, (<b>b</b>) reverse, and (<b>c</b>) dip-slip fault rupture propagation.</p>
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<p>Fragility curves of EmbA for (<b>a</b>) minor, (<b>b</b>) moderate, and (<b>c</b>) extensive damage state.</p>
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<p>Fragility curves of EmbA for the three rupture depths: (<b>a</b>) normal, (<b>b</b>) reverse, and (<b>c</b>) strike-slip fault.</p>
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<p>Fragility curves of EmbA for two conditions of sandy soil for (<b>a</b>) normal, (<b>b</b>) reverse, and (<b>c</b>) dip-slip fault rupture.</p>
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<p>Comparison of fragility curves with those proposed by HAZUS methodology [<a href="#B32-geosciences-14-00312" class="html-bibr">32</a>] for (<b>a</b>) normal, (<b>b</b>) reverse, and (<b>c</b>) dip-slip fault.</p>
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16 pages, 6376 KiB  
Article
Model Test on Thermomechanical Behavior of Deeply Buried Pipe Energy Pile Under Different Temperature Loads and Mechanical Loads
by Jianghuai Yuan, Zhi Chen, Yan Zhuang and Yongli Liu
Appl. Sci. 2024, 14(22), 10528; https://doi.org/10.3390/app142210528 - 15 Nov 2024
Viewed by 206
Abstract
Deeply buried pipe energy pile (DBP-EP) offers the capability to harness geothermal energy from significantly deeper subterranean layers than those available inside buried pipe energy pile (IBP-EP). Despite its potential, there is a notable scarcity of research on the thermomechanical behavior of DBP-EP. [...] Read more.
Deeply buried pipe energy pile (DBP-EP) offers the capability to harness geothermal energy from significantly deeper subterranean layers than those available inside buried pipe energy pile (IBP-EP). Despite its potential, there is a notable scarcity of research on the thermomechanical behavior of DBP-EP. This study meticulously observed the thermal variations in the soil surrounding the DBP-EP, the mechanical response of the pile itself, the earth pressure at the pile toe, and the displacement occurring at the pile’s top during the heating phase across various operational conditions. The findings show that for every 1 °C increase in inlet temperature, the temperature difference between the inlet and outlet increases by about 0.27 °C. The method of load application at the pile top during heating markedly influences the frictional resistance along the pile’s sides. Furthermore, When the pile top load rises from 0.26 kN to 0.78 kN, the observed vertical load at the pile foot decreases by 2.2–8.51%. This indicates that the increase in the pile top load reduces the downdrag effect on the sandy soil near the pile toe. This reduction subsequently diminishes the impact of vertical loads on the pile toe. Notably, after continuous operation for 8 h, the rate of increase in pile top displacement for DBP-EP shows a decline. Additionally, for every 1 °C rise in the inlet water temperature, the final displacement at the pile top diminishes by approximately 0.03‰D. This research endeavors to furnish a robust theoretical foundation for the structural design and practical engineering applications for DBP-EP. Full article
(This article belongs to the Section Applied Thermal Engineering)
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<p>Detailed drawing comparing IBP-EP and DBP-EP.</p>
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<p>Field diagram of indoor modeling test.</p>
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<p>Diagram of the internal layout of the model test.</p>
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<p>(<b>a</b>) Variation of heat exchanger tube temperature with time; (<b>b</b>) Variation of temperature in depth direction.</p>
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<p>Temperature variation of pile soil in horizontal direction with different water inlet temperatures.</p>
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<p>Curve of observed strain change of the pile body during heating under different working conditions. (<b>a</b>) <span class="html-italic">F<sub>top</sub></span> = 0 kN inside wall of pile. (<b>b</b>) <span class="html-italic">F<sub>top</sub></span> = 0 kN side wall of pile. (<b>c</b>) <span class="html-italic">F<sub>top</sub></span> = 0.26 kN inside wall of pile. (<b>d</b>) <span class="html-italic">F<sub>top</sub></span> = 0.26 kN side wall of pile. (<b>e</b>) <span class="html-italic">F<sub>top</sub></span> = 0.78 kN inside wall of pile. (<b>f</b>) <span class="html-italic">F<sub>top</sub></span> = 0.78 kN side wall of pile.</p>
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<p>Curves of side friction resistance of the pile body under different working conditions during the heating process. (<b>a</b>) <span class="html-italic">F<sub>top</sub></span> = 0 kN. (<b>b</b>) <span class="html-italic">F<sub>top</sub></span> = 0.26 kN. (<b>c</b>) <span class="html-italic">F<sub>top</sub></span> = 0.78 kN.</p>
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<p>Percentage of earth pressure and end bearing capacity at pile toe for different working conditions.</p>
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<p>Diagram of pile top displacement with time under different working conditions. (<b>a</b>) <span class="html-italic">F<sub>top</sub></span> = 0 kN. (<b>b</b>) <span class="html-italic">F<sub>top</sub></span> = 0.26 kN. (<b>c</b>) <span class="html-italic">F<sub>top</sub></span> = 0.78 kN.</p>
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16 pages, 5554 KiB  
Article
Unmanned Aerial Vehicle Photogrammetry for Monitoring the Geometric Changes of Reclaimed Landfills
by Grzegorz Pasternak, Klaudia Pasternak, Eugeniusz Koda and Paweł Ogrodnik
Sensors 2024, 24(22), 7247; https://doi.org/10.3390/s24227247 - 13 Nov 2024
Viewed by 281
Abstract
Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the [...] Read more.
Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans. Full article
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<p>View of the Słabomierz–Krzyżówka landfill from a (<b>a</b>) south-east and (<b>b</b>) north-east direction.</p>
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<p>Location of GCPs on the generated orthophotomap of the landfill (<b>left</b>) and on the DSM (<b>right</b>).</p>
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<p>Flowchart of research methodology.</p>
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<p>M3C2 algorithm functional rule.</p>
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<p>Distance Uncertainty calculated by the M3C2 algorithm.</p>
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<p>Differential point cloud showing vertical displacements of the Słabomierz–Krzyżówka landfill body.</p>
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<p>Vertical cross-section (<b>up</b>) and vertical displacements of the slope (<b>down</b>).</p>
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<p>Examples of damage caused by surface runoff of rainwater on orthophotomap—Set 1: (<b>a</b>,<b>d</b>,<b>g</b>);—Set 2: (<b>b</b>,<b>e</b>,<b>h</b>), and on differential point cloud: (<b>c</b>,<b>f</b>,<b>i</b>).</p>
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16 pages, 3040 KiB  
Article
A Variational Approach to Analyze the Settlement of Existing Tunnels Caused by Ground Surcharge
by Tianjian Chai, Jianwei Yan, Xuehui Jiang and Jiabao Li
Symmetry 2024, 16(11), 1511; https://doi.org/10.3390/sym16111511 - 11 Nov 2024
Viewed by 339
Abstract
This paper presents a variational approach to assess the settlement of the operational shield tunnels resulting from surface loading. The vertical additional force on the tunnel, induced by the surcharge, was computed using the Boussinesq solution. The structural behavior of the tunnel was [...] Read more.
This paper presents a variational approach to assess the settlement of the operational shield tunnels resulting from surface loading. The vertical additional force on the tunnel, induced by the surcharge, was computed using the Boussinesq solution. The structural behavior of the tunnel was modeled using the Timoshenko beam theory, which accounts for both bending and shear deformation mechanisms. Furthermore, the two-parameter Pasternak foundation model, which accounts for the continuity of foundation deformation, was used to model the interaction between the tunnel and the surrounding ground. A finite Fourier series was employed to approximate the vertical displacement and cross-sectional rotation angle of the tunnel. By conducting work and energy analyses, the energy balance equations for the tunnel and the soil were obtained. The governing equations were then formulated according to the minimum potential energy principle. The displacement and cross-sectional rotation angle of the tunnel were then expressed through the variational method. The accuracy of the proposed method was validated by comparison with in situ measurement data, confirming its effectiveness in predicting tunnel responses under a ground surcharge. Finally, a parametric study was conducted to evaluate the impact of various parameters on the settlement of the shield tunnel. Full article
(This article belongs to the Section Engineering and Materials)
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<p>A schematic of the position between the ground surcharge and existing tunnel. (<b>a</b>) The plan diagram; (<b>b</b>) the cross-section diagram.</p>
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<p>A schematic model for the longitudinal deformation of the tunnel.</p>
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<p>Schematic diagram of ground surcharge. (<b>a</b>) Plan diagram. (<b>b</b>) Cross-section diagram.</p>
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<p>Convergence and accuracy analysis of different (<b>a</b>) Fourier series expansion orders. Comparative analysis of existing tunnel settlement between proposed method and finite element method (FEM). (<b>b</b>) Maximum tunnel settlement on different Fourier series expansion orders.</p>
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<p>Relation between ground-heaped load and tunnel position. (<b>a</b>) Plan diagram, (<b>b</b>) Cross-section diagram.</p>
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<p>Relation between ground-heaped load and tunnel position. (<b>a</b>) Plan diagram, (<b>b</b>) Cross-section diagram.</p>
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<p>Comparison of existing tunnel settlement with in situ measurement data. EB model [<a href="#B32-symmetry-16-01511" class="html-bibr">32</a>], In suit data [<a href="#B33-symmetry-16-01511" class="html-bibr">33</a>].</p>
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<p>Influence curves of the interaction angle on the maximum settlement.</p>
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<p>Influence curves of tunnel burial depth on maximum settlement.</p>
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<p>Relationship curve between ground surcharge load and maximum settlement.</p>
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13 pages, 10903 KiB  
Article
Mechanical Effect of an Implant Under Denture Base in Implant-Supported Distal Free-End Removable Partial Dentures
by Naomichi Murashima, Yoshiyuki Takayama, Toshifumi Nogawa, Atsuro Yokoyama and Kiwamu Sakaguchi
Dent. J. 2024, 12(11), 358; https://doi.org/10.3390/dj12110358 - 11 Nov 2024
Viewed by 317
Abstract
Background: In recent years, implant-assisted removable partial dentures (IARPDs) have been used clinically. However, the extent to which additional implants reduce the burden of supporting tissues is unclear. The aim of this study was therefore to investigate the influence of implanted IARPDs [...] Read more.
Background: In recent years, implant-assisted removable partial dentures (IARPDs) have been used clinically. However, the extent to which additional implants reduce the burden of supporting tissues is unclear. The aim of this study was therefore to investigate the influence of implanted IARPDs on stress sharing among supporting tissues, using finite element (FE) analysis. Methods: FE models were constructed based on the computed tomography (CT) of a patient with a unilateral defect of the mandibular premolars and molars and the surface data of an RPD with cuspids as abutments, designed using computer-aided design software. A titanium implant was placed in the area equivalent to the first premolar, second premolar, or first molar (IARPD4, IARPD5, and IARPD6, respectively). FE analysis was performed for laterally symmetrical models, i.e., bilateral distal free-end IARPDs. A vertical load of 200 N was applied to the central fossa of the artificial premolars or molars (L4, L5, or L6). Results: Equivalent stress in the alveolar mucosa and vertical displacement of the denture was smaller, with IARPDs under L5 and L6 loads, compared to RPDs. However, abutment teeth were displaced upward under an L6 load in the IARPD5 model. Conclusions: Within the limitations of this study, the area corresponding to the first molar was recommended as the location for an implant under the denture base of bilateral distal free-end IARPDs. Implants located in the area corresponding to the second premolar may apply non-physiological extrusion force on abutment teeth under the load on the artificial second molar. Full article
(This article belongs to the Section Dental Implantology)
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<p>CT data (DICOM) for mandibular bone.</p>
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<p>CAD model (STL format).</p>
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<p>The denture metal frame was designed with a 3D CAD solution for RPD frameworks (DIGISTELL; C4W/DIGILEA, Montpellier, France) and imported with CAD software (Materialise 3-matic Medical 12.0; Materialise, Leuven, Belgium). The Co-Cr framework consisted of a set comprising a cingulum rest, an I-bar clasp, and distal proximal plate on each abutment tooth.</p>
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<p>Bilateral distal free-end RPD FE model (nodes: 11,203; elements: 48,586). Material properties are displayed in <a href="#dentistry-12-00358-t001" class="html-table">Table 1</a> [<a href="#B21-dentistry-12-00358" class="html-bibr">21</a>,<a href="#B22-dentistry-12-00358" class="html-bibr">22</a>,<a href="#B23-dentistry-12-00358" class="html-bibr">23</a>,<a href="#B24-dentistry-12-00358" class="html-bibr">24</a>]. All materials were assumed to be isotropic and elastic.</p>
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<p>Boundary condition.</p>
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<p>Load point.</p>
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<p>Equivalent stress in the mucosa area (RPD).</p>
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<p>Equivalent stress in the mucosa area (IARPD4).</p>
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<p>Equivalent stress in the mucosa area (IARPD5).</p>
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<p>Equivalent stress in the mucosa area (IARPD6).</p>
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<p>Displacement of denture.</p>
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<p>Equivalent stress in the canine PDL (RPD).</p>
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<p>Equivalent stress in the canine PDL (IARPD4).</p>
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<p>Equivalent stress in the canine PDL (IARPD5).</p>
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<p>Equivalent stress in the canine PDL (IARPD6).</p>
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<p>Displacement of the apical area of the canine.</p>
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23 pages, 8611 KiB  
Article
An Analysis of Vertical Infiltration Responses in Unsaturated Soil Columns from Permafrost Regions
by Lincui Li, Xi’an Li, Yonghong Li, Cheng Li, Yong Li, Li Wang, Yiping He and Chaowei Yao
Appl. Sci. 2024, 14(22), 10195; https://doi.org/10.3390/app142210195 - 6 Nov 2024
Viewed by 431
Abstract
Rainfall infiltration affects permafrost-related slope stability by changing the pore water pressure in soil. In this study, the infiltration responses under rainfall conditions were elucidated. The instantaneous profile method and filter paper method were used to obtain the soil–water characteristic curve (SWCC) and [...] Read more.
Rainfall infiltration affects permafrost-related slope stability by changing the pore water pressure in soil. In this study, the infiltration responses under rainfall conditions were elucidated. The instantaneous profile method and filter paper method were used to obtain the soil–water characteristic curve (SWCC) and hydraulic conductivity function (HCF). During the rainfall infiltration test, the vertical patters of volumetric moisture contents, total hydraulic head or suction and wetting front were recorded. Advancing displacement and rate of the wetting front, the cumulative infiltration, the instantaneous infiltration rate, and the average infiltration rate were determined to comprehensively assess the rainfall infiltration process, along with SWCC and HCF. Additionally, the effects of dry density and runoff on the one-dimensional vertical infiltration process of soil columns were evaluated. The results showed that the variation curve of wetting front displacement versus time obeys a power function relationship. In addition, the infiltration rate–time relationship curve and the unsaturated permeability curve could be roughly divided into three stages, and the SWCC and HCF calculated by volumetric moisture content are more sensitive to changes in dry density than to changes in runoff or hydraulic head height. Full article
(This article belongs to the Special Issue Advances in Permafrost)
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<p>Particle distribution curves of the test materials (<b>a</b>) and their XRD results (<b>b</b>).</p>
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<p>Schematic diagram of the infiltration column apparatus.</p>
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<p>Sample preparation for the filter paper test. (<b>a</b>) Whatman Quantitative Filter Papers, (<b>b</b>) component of specimen, (<b>c</b>) Sealed with waterproof tape, (<b>d</b>) sealed with plastic wrap, (<b>e</b>) Sealed with tinfoil, (<b>f</b>) Sealed with paraffin, (<b>g</b>) Numbered sample after completely sealed.</p>
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<p>Relationship curve of <span class="html-italic">k<sub>s</sub></span> vs. time. (<b>a</b>) data for CH soil, (<b>b</b>) data for QY soil, (<b>c</b>) data for YA soil, (<b>d</b>) data for LL soil.</p>
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<p>Relationship between the saturated permeability and dry density.</p>
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<p>Wetting front displacement of the soil column.</p>
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<p>Advancing rate of the wetting front of the soil column.</p>
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<p>Moisture content increment in the soil column section.</p>
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<p>Cumulative infiltration of the soil column.</p>
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<p>Instantaneous infiltration rate.</p>
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<p>Average infiltration rate at the upper surface of the soil column.</p>
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<p>SWCC and PSD of the compacted soil sample.</p>
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<p>Volumetric moisture profile and total hydraulic head profile of the soil column.</p>
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<p>Distributions of the hydraulic head (<b>a</b>) and moisture content (<b>b</b>) according to the instantaneous profile method.</p>
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<p>Unsaturated permeability of the soil column.</p>
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<p>Unsaturated permeability of the C1 and C2 soil columns.</p>
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<p>Unsaturated permeability of the C2 and C3 soil columns.</p>
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14 pages, 1316 KiB  
Article
Graviception Uncertainty, Spatial Anxiety, and Derealization in Patients with Persistent Postural-Perceptual Dizziness
by Kathrine Jáuregui-Renaud, Rodrigo Cabrera-Pereyra, José Adán Miguel-Puga and Mónica Alcántara-Thome
J. Clin. Med. 2024, 13(22), 6665; https://doi.org/10.3390/jcm13226665 - 6 Nov 2024
Viewed by 355
Abstract
Objectives: Persistent Postural-Perceptual Dizziness (PPPD) is a frequent diagnosis in patients with chronic dizziness, ineffective postural control, visual dependence, and emotional symptoms. Methods: 53 patients with PPPD (25–84 years old) and 53 adults (29–84 years old) with no vestibular disease agreed [...] Read more.
Objectives: Persistent Postural-Perceptual Dizziness (PPPD) is a frequent diagnosis in patients with chronic dizziness, ineffective postural control, visual dependence, and emotional symptoms. Methods: 53 patients with PPPD (25–84 years old) and 53 adults (29–84 years old) with no vestibular disease agreed to participate in this study. Assessments included: vestibular function tests (sinusoidal yaw rotation and vestibular-evoked myogenic potentials); accuracy and precision of Subjective Visual Vertical (SVV) estimation while static and during on-axis yaw rotation; static posturography with open/closed eyes and 30° neck extension, while standing on hard/soft surface; questionnaires on symptoms of unsteadiness, spatial anxiety, dizziness-related handicap, anxiety/depression, depersonalization/derealization, and perceived stress. After preliminary bivariate analyses, analysis of covariance was performed on the measurements of postural sway, spatial anxiety, and dizziness-related handicap (p < 0.05). Results: Higher intraindividual variability (reduced precision) on SVV estimations was evident in patients with PPPD compared to adults with no vestibular disease, which was related to the length of postural sway, to velocity displacement in the sagittal plane, as well as to spatial anxiety and common mental symptoms (including depersonalization/derealization symptoms). Covariance analysis showed contribution of these factors to the dizziness-related handicap reported by the patients. Conclusions: Unprecise graviception could be a contributing factor to the postural instability and mental symptoms reported by patients with PPPD, which in turn contribute to their dizziness-related handicap. Full article
(This article belongs to the Special Issue Clinical Diagnosis and Management of Vestibular Disorders)
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<p>Frequency of symptoms of unsteadiness apart from dizziness/vertigo that were reported by at least 50% of the 53 patients with Persistent Postural Perceptual Dizziness, and the frequency on 53 participants with no vestibular disease. Statistical difference between the two groups was significant for all the comparisons (<span class="html-italic">t</span> test for proportions, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The 10 most frequent symptoms of depersonalization/derealization reported by 53 patients with Persistent Postural Perceptual Dizziness and 53 participants with no vestibular disease. Statistical difference between the two groups was significant for all the comparisons (<span class="html-italic">t</span> test for proportions, <span class="html-italic">p</span> &lt; 0.05).</p>
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20 pages, 6270 KiB  
Article
Numerical Analysis and Validation of Horizontal and Vertical Displacements of a Floating Body for Different Wave Periods
by Marla Rodrigues de Oliveira, Liércio André Isoldi, Elizaldo Domingues dos Santos, Luiz Alberto Oliveira Rocha and Mateus das Neves Gomes
J. Mar. Sci. Eng. 2024, 12(11), 1996; https://doi.org/10.3390/jmse12111996 - 6 Nov 2024
Viewed by 359
Abstract
This study concentrates on numerically evaluating the behavior of a floating body with a box format. Although research on floating objects has been conducted, the numerical modeling of Wave Energy Converter (WEC) devices, considering the effects of fluctuations, remains underexplored. Therefore, this research [...] Read more.
This study concentrates on numerically evaluating the behavior of a floating body with a box format. Although research on floating objects has been conducted, the numerical modeling of Wave Energy Converter (WEC) devices, considering the effects of fluctuations, remains underexplored. Therefore, this research intends to facilitate the analysis of floating devices. First, the experimental data served as a benchmark for evaluating the motion paths of the floating box’s centroid. Second, the effects of various wave periods and heights on the floating body’s movement were analyzed. The Volume of Fluid (VOF) multiphase model was applied to simulate the interactions between phases. The computational model involved solving governing equations of mass conservation, volumetric fraction transport, and momentum, employing the Finite Volume Method (FVM). The validation demonstrated that the Normalized Root Mean Square Error (NRMSE) for the x/h ratio was 3.3% for a wave height of 0.04 m and 4.4% for a wave height of 0.1 m. Moreover, the NRMSE for the z-coordinate to the depth of water (z/h) was higher, at 5% for a wave height of 0.04 m and 5.8% for a wave height of 0.1 m. The overall NRMSE remained within acceptable ranges, indicating the reliability of the numerical solutions. Additionally, the analysis of horizontal and vertical velocities at different wave periods and heights showed that for H = 0.04 m, the wave periods had a minimal impact on the amplitude, but the oscillation frequency varied. At H = 0.1 m, both velocities exhibited significantly larger amplitudes, especially for T = 1.2 s and T = 2.0 s, indicating stronger motion with higher wave heights. Full article
(This article belongs to the Special Issue Computational Marine Hydrodynamics (CMH))
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<p>Computational domain.</p>
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<p>Details regarding the mesh generation.</p>
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<p>Evaluation of the mesh refinements.</p>
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<p>Evaluation of the time interval.</p>
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<p>Comparison of the Experimental solution of Ren et al. (2015) [<a href="#B5-jmse-12-01996" class="html-bibr">5</a>] and the present work: the ratio of <span class="html-italic">x</span>-coordinate value to water depth for <span class="html-italic">H</span> = 0.04 m and <span class="html-italic">T</span> = 1.2 s.</p>
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<p>Comparison of the Experimental solution of Ren et al. (2015) [<a href="#B5-jmse-12-01996" class="html-bibr">5</a>] and the present work: the ratio of <span class="html-italic">z</span>-coordinate value to water depth for <span class="html-italic">H</span> = 0.04 m and <span class="html-italic">T</span> = 1.2 s.</p>
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<p>Comparison of the Experimental solution of Ren et al. (2015) [<a href="#B5-jmse-12-01996" class="html-bibr">5</a>] and the present work: the ratio of <span class="html-italic">x</span>-coordinate value to water depth for <span class="html-italic">H</span> = 0.1 m and <span class="html-italic">T</span> = 1.2 s.</p>
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<p>Comparison of the Experimental solution of Ren et al. (2015) [<a href="#B5-jmse-12-01996" class="html-bibr">5</a>] and the present work: the ratio of <span class="html-italic">z</span>-coordinate value to water depth for <span class="html-italic">H</span> = 0.1 m and <span class="html-italic">T</span> = 1.2 s.</p>
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<p>Phase contours of the floating box for <span class="html-italic">H</span> = 0.04 m at <span class="html-italic">T</span> = 1.2 s: (<b>a</b>) <span class="html-italic">t</span> = 2.47 s, (<b>b</b>) <span class="html-italic">t</span> + <span class="html-italic">T</span>/4 s, (<b>c</b>) <span class="html-italic">t</span> + <span class="html-italic">T</span>/2 s, (<b>d</b>) <span class="html-italic">t</span> + 3<span class="html-italic">T</span>/4 s, (<b>e</b>) <span class="html-italic">t</span> + <span class="html-italic">T</span> s.</p>
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<p>The ratios of <span class="html-italic">x</span>-coordinate and <span class="html-italic">z</span>-coordinate values to water depth for <span class="html-italic">H</span> = 0.04 m, with different wave periods.</p>
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<p>The ratios of <span class="html-italic">x</span>-coordinate and <span class="html-italic">z</span>-coordinate values to water depth for <span class="html-italic">H</span> = 0.1 m, with different wave periods.</p>
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<p>Horizontal and vertical velocity values for <span class="html-italic">H</span> = 0.04 m, with different wave periods.</p>
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<p>Horizontal and vertical velocity values for <span class="html-italic">H</span> = 0.1 m, with different wave periods.</p>
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<p>Fourier analysis of horizontal velocity for <span class="html-italic">H</span> = 0.1 m.</p>
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<p>Fourier analysis of vertical velocity for <span class="html-italic">H</span> = 0.1 m.</p>
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<p>Fourier analysis of horizontal velocity for <span class="html-italic">H</span> = 0.04 m.</p>
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<p>Fourier analysis of vertical velocity for <span class="html-italic">H</span> = 0.04 m.</p>
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21 pages, 15197 KiB  
Article
Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR
by Francesco Pirotti, Felix Enyimah Toffah and Alberto Guarnieri
Remote Sens. 2024, 16(22), 4123; https://doi.org/10.3390/rs16224123 - 5 Nov 2024
Viewed by 387
Abstract
Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth’s surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed [...] Read more.
Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth’s surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed by land subsidence or uplift, particularly in regions prone to extreme weather events and climate variability. The correlation of periodic SVGM with climatic data from Earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets, respectively. Measurement points (MP) from the EGMS over Italy provided a value for ground vertical movement approximately every 6 days. The precipitation and temperature datasets were processed to provide drought code (DC) maps calculated ad hoc for this study at a 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analyzed to assess correlations with Spearman’s rank correlation coefficient (ρ) between this measure and the DCs from the Copernicus Emergency Management Service (DCCEMS), from MODIS + CHIRP (DC1km) and from the temperature. The results over the considered area (Italy) showed that 0.46% of all MPs (32,826 MPs out of 7,193,676 MPs) had a ρ greater than 0.7; 12,142 of these had a positive correlation, and 20,684 had a negative correlation. DC1km was the climatic factor that provided the highest number of correlated MPs, roughly giving +59% more correlated MPs than DCCEMS and +300% than the temperature data. If a ρ greater than 0.8 was considered, the number of MPs dropped by a factor of 10: from 12,142 to 1275 for positive correlations and from 20,684 to 2594 for negative correlations between the DC1km values and SVGM measurements. Correlations that lagged in time resulted in most of the correlated MPs being within a window of ±6 days (a single satellite overpass time). Because the DC and temperature are strongly co-linear, further analysis to assess which was superior in explaining the seasonality of the MPs was carried out, resulting in DC1km significantly explaining more variance in the SVGM than the temperature for the inversely correlated points rather than the directly correlated points. The spatial distribution of the correlated MPs showed that they were unevenly distributed in clusters across the Italian territory. This work will lead to further investigation both at a local scale and at a pan-European scale. An interactive WebGIS application that is open to the public is available for data consultation. This article is a revised and expanded version of a paper entitled “Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought condition” which was accepted and presented at the ISPRS Mid-Term Symposium, Belem, Brasil, 8–12 November 2024. Data are shared in a public repository for the replication of the method. Full article
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Figure 1
<p>Combined drought indicators for the first 10-day period of July 2022 in Italy (JRC, 2024). This plot shows drought alert indicators over the northern regions, where a state of emergency was declared for this year. This event was an exceptional incident as, in general, the south of Italy is warmer than the north.</p>
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<p>Flowchart of the methodology.</p>
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<p>Histogram of frequency distribution of Spearman correlation values (<math display="inline"><semantics> <mi>ρ</mi> </semantics></math>) between <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mi>km</mi> </mrow> </msub> </mrow> </semantics></math> and SVGM at MPs [<a href="#B1-remotesensing-16-04123" class="html-bibr">1</a>].</p>
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<p>Lag time distribution of best correlations between <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mi>km</mi> </mrow> </msub> </mrow> </semantics></math> and SVGM [<a href="#B1-remotesensing-16-04123" class="html-bibr">1</a>].</p>
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<p>Lag time distribution of best correlations between temperature derived from calibrated MODIS and SVGM [<a href="#B1-remotesensing-16-04123" class="html-bibr">1</a>].</p>
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<p>(<b>a</b>) Pairwise <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>C</mi> <mrow> <mi>C</mi> <mi>E</mi> <mi>M</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math>–<math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mi>km</mi> </mrow> </msub> </mrow> </semantics></math> values of correlations with ground motion; (<b>b</b>) temperature vs. <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mi>km</mi> </mrow> </msub> </mrow> </semantics></math> correlation. Top and bottom rows are negative and positive (<math display="inline"><semantics> <mi>ρ</mi> </semantics></math>) values from correlation testing with SVGM [<a href="#B1-remotesensing-16-04123" class="html-bibr">1</a>].</p>
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<p>Top row shows the spatial distribution of negative and positive correlations (<b>left</b> and <b>right</b>) in terms of the percentage of correlated MPs with respect to the total number of MPs recorded by the European Ground Motion Service. The percentage was calculated over a regular hexagon grid overlaid onto the study area. Bottom row pinpoints areas (red dots) with the highest percentage of correlated MPs, negative (<b>bottom-left</b>) and positive (<b>bottom-right</b>).</p>
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<p>Measurement points with positive (<b>a</b>,<b>b</b>) and negative (<b>c</b>,<b>d</b>) Spearman correlation values.</p>
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<p>Time series plot showing the correlations at the respective measurement points in <a href="#remotesensing-16-04123-f008" class="html-fig">Figure 8</a>.</p>
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<p>“Viadotto Gorsexio” with a central pillar taller than 172 m.</p>
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<p>Display of information related to EGMS and climate correlation values of the area in the WebGIS viewer.</p>
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<p>Time series plot of the corresponding correlation (<b>a</b>) and the same time series with normalized values (<b>b</b>).</p>
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23 pages, 38747 KiB  
Article
A New Method for Extracting Three-Dimensional Surface Deformation in Underground Mining Areas Based on the Differentiability of D-InSAR Line-of-Sight Displacements
by Junjie Chen, Chunsu Zhao, Weitao Yan and Zhiyu Chen
Remote Sens. 2024, 16(21), 4085; https://doi.org/10.3390/rs16214085 - 1 Nov 2024
Viewed by 742
Abstract
Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method [...] Read more.
Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method for extracting 3D mining-induced deformation based on the differentiability of D-InSAR LOS deformation fields. The method approximates the D-InSAR LOS deformation field in underground mining areas as a differentiable function and constructs a 3D deformation extraction model utilizing directional derivatives of this function. The least squares method is used for estimating and evaluating the 3D deformation. Simulation and real data experiments have been used to verify the feasibility of the method in extracting mining-induced 3D deformation. The simulation results show relative root mean square errors (RRMSES) of 1.24%, 6.05%, 0.97%, and 11.47% for vertical and horizontal displacements along the east–west and south–north directions, respectively. The real data experiments using Sentinel-1 images show that the root mean square errors (RMSES) of the up–down, south–north, and east–west directions are 14.06 mm, 7.37 mm, and 11.56 mm, respectively. Experimental results show that the method can provide a certain basis for 3D surface deformation monitoring of mining subsidence. Full article
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Graphical abstract

Graphical abstract
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<p>Relationship between surface 3D deformation and LOS direction deformation.</p>
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<p>Decomposition of the sliding parameter <math display="inline"><semantics> <mrow> <mi>W</mi> <mfenced> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> </mfenced> <mi>cot</mi> <mi>θ</mi> </mrow> </semantics></math> along the south–north and east–west directions.</p>
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<p>The arrangement of the LOS deformation raster map. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Δ</mi> <mi>x</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Δ</mi> <mi>y</mi> </mrow> </semantics></math> represent the grid resolution in the <span class="html-italic">X</span>-axis and <span class="html-italic">Y</span>-axis directions, respectively.</p>
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<p>Determination of the boundary of the surface movement basin. AB and CD represent the major sections along the strike and dip of the subsidence basin, respectively. The green, blue and red arrows represent the subsidence vector <math display="inline"><semantics> <mi>W</mi> </semantics></math>, the horizontal movement vector <math display="inline"><semantics> <mi>U</mi> </semantics></math> and the vector sum of the first two, respectively.</p>
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<p>Flowchart of DLB-3DEM method.</p>
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<p>Simulated 3D deformation and LOS deformation value: (<b>a</b>) simulated vertical displacements value, (<b>b</b>) simulated south–north horizontal displacements value, (<b>c</b>) simulated east–west horizontal displacements value, (<b>d</b>) simulated LOS deformation value. The arrows in (<b>b</b>,<b>c</b>) represent the direction of horizontal movement. Profiles AB and CD represent survey lines within the major section of the subsidence basin.</p>
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<p>Three-dimensional deformation and standard deviations map extracted using the DLB-3DEM method: (<b>a</b>) extracted vertical displacements value, (<b>b</b>) extracted south–north horizontal displacements value, (<b>c</b>) extracted east–west horizontal displacements value, (<b>d</b>) standard deviations map of vertical displacements, (<b>e</b>) standard deviations map of south–north displacements, (<b>f</b>) standard deviations map of east–west displacements.</p>
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<p>Comparison of 3D deformation values of profile lines extracted based on DLB-3DEM method with simulated values: (<b>a</b>) vertical displacements comparison of AB, (<b>b</b>) vertical displacements comparison of CD, (<b>c</b>) east–west horizontal movement comparison of AB, (<b>d</b>) south–north horizontal movement comparison of CD.</p>
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<p>Study area: (<b>a</b>) ALOS PALSAR DEM of study area; (<b>b</b>) enlarged view of the 2051 working face. The “2020.10” and “2022.02” represent October 2020 and February 2022, respectively, indicating the mining time for the first and last sections of 2051 working face.</p>
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<p>Coherence and LOS deformation field of study area. (<b>a</b>) Interferometric coherence; (<b>b</b>) LOS deformation field above the working face from 22 November 2020 to 2 February 2021. The area surrounded by black circles represents the decoherence area. The red part of the working face represents the coal seam that has been extracted as of 2 February 2021.</p>
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<p>Extracted 3D deformation field and uncertainty evaluation of 2051 working face as of 2 February 2021: (<b>a</b>) extracted vertical displacement value, (<b>b</b>) extracted south–north horizontal displacement value, (<b>c</b>) extracted east–west horizontal displacement value, (<b>d</b>) standard deviation map of vertical displacements, (<b>e</b>) standard deviation map of south–north horizontal displacements, (<b>f</b>) standard deviation map of east–west horizontal displacements.</p>
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<p>Comparison between measured deformation values and deformation values extracted by the DLB-3DEM method at 59 monitoring points: (<b>a</b>) vertical displacement, (<b>b</b>) south–north horizontal movement, (<b>c</b>) east–west horizontal movement. The meaning of the abscissa observation points indicates that the comparison of the measured and extracted deformation values at the 59 points is conducted in the order of strike direction points (A1–A34) followed by dip direction points (B1–B25).</p>
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<p>Gaussian noise added to the simulated LOS deformation field: (<b>a</b>) no noise; (<b>b</b>) mean: 0 mm, standard deviation: 20 mm; (<b>c</b>) mean: 0 mm, standard deviation: 40 mm; (<b>d</b>) mean: 0 mm, standard deviation: 60 mm; (<b>e</b>) mean: 0 mm, standard deviation: 80 mm; (<b>f</b>) mean: 0 mm, standard deviation: 100 mm.</p>
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<p>Comparison of subsidence extracted and the real values as the error increases. The red curve represents the true value, the blue curve represents the extracted value after adding the specified error, and the green curve represents the difference between the two. (<b>a</b>–<b>f</b>) Subsidence comparison of AB, (<b>g</b>–<b>l</b>) subsidence comparison of CD.</p>
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<p>Comparison of horizontal movement extracted and the real values as the error increases. The red curve represents the true value, the blue curve represents the extracted value after adding the specified error, and the green curve represents the difference between the two. (<b>a</b>–<b>f</b>) Horizontal movement comparison of AB, (<b>g</b>–<b>l</b>) horizontal movement comparison of CD.</p>
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<p>Relative error of 3D deformation extraction results. (<b>a</b>) Subsidence of AB, (<b>b</b>) subsidence of CD, (<b>c</b>) horizontal movement of AB, (<b>d</b>) horizontal movement of CD.</p>
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<p>Average relative error of 3D deformation. (A) Subsidence of AB, (B) subsidence of CD, (C) horizontal movement of AB, (D) horizontal movement of CD.</p>
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22 pages, 15149 KiB  
Article
A Peridynamics-Smoothed Particle Hydrodynamics Coupling Method for Fluid-Structure Interaction
by Chengjie Cao, Chenxu Gu, Chao Wang, Chunhui Wang, Pei Xu and Hui Wang
J. Mar. Sci. Eng. 2024, 12(11), 1968; https://doi.org/10.3390/jmse12111968 - 1 Nov 2024
Viewed by 470
Abstract
Ice–water interaction is a critical issue of engineering studies in polar regions. This paper proposes a methodology to simulate fluid–ice interactions by employing a structure modeled using ordinary state-based peridynamics (OSB-PD) within a smoothed particle hydrodynamics (SPH) framework, effectively representing a deformable moving [...] Read more.
Ice–water interaction is a critical issue of engineering studies in polar regions. This paper proposes a methodology to simulate fluid–ice interactions by employing a structure modeled using ordinary state-based peridynamics (OSB-PD) within a smoothed particle hydrodynamics (SPH) framework, effectively representing a deformable moving boundary. The forces at the fluid–structure interface are delineated by solving the fluid motion equations for normal forces exerted by the fluid on the structure, grounded in the momentum conservation law. Upon validating the PD and SPH methods, a dam break flowing through an elastic gate was simulated. When compared with experimental results, the model exhibited discrepancies of 3.8%, 0.5%, and 4.6% in the maximum horizontal displacement, maximum vertical displacement, and the waterline deviation (W = 0.05 m), respectively. Moreover, the method demonstrated a high degree of accuracy in simulating the fracture of in-situ cantilever ice beams, with deflection closely matching experimental data and a 7.4% error in maximum loading force. The proposed PD-SPH coupling approach demonstrates its effectiveness in capturing the complex fluid–structure interactions and provides a valuable tool for studying the deformation and fracture of structures under the influence of fluid forces. Full article
(This article belongs to the Section Ocean Engineering)
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Figure 1
<p>Peridynamic interaction between the central point <span class="html-italic">i</span> and its family member <span class="html-italic">j</span>.</p>
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<p>Fixed ghost particle boundary technique.</p>
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<p>Schematic of fluid–structure interaction. (<b>a</b>) Force exerted by fluid on structure; (<b>b</b>) Force exerted by structure on fluid.</p>
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<p>Numerical computational procedure of the coupled PD-SPH model.</p>
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<p>Comparison of displacement results between PD and FEM methods. (<b>a</b>) X-directional displacement; (<b>b</b>) Y-directional displacement.</p>
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<p>Simulation results of the PD method under different particle diameters.</p>
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<p>Schematic diagram of the cantilever ice beam.</p>
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<p>Vertical displacement before and after the loading failure of the cantilever ice beam.</p>
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<p>Simulation results of cantilever beams with different length-to-thickness ratios. (<b>a</b>) Fracture and fragmentation cloud diagram; (<b>b</b>) Displacement–load curve.</p>
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<p>Schematic diagram of the two-dimensional dam break.</p>
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<p>Pressure field of the dam break.</p>
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<p>Comparison of experimental pressure with different particle diameters.</p>
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<p>Schematic diagram of the dam break flowing through an elastic gate.</p>
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<p>Comparison of experimental result with simulation. (<b>a</b>) 0.08 s; (<b>b</b>) 0.16 s; (<b>c</b>) 0.24 s; (<b>d</b>) 0.32 s.</p>
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<p>Comparison of experimental result with simulation. (<b>a</b>) 0.08 s; (<b>b</b>) 0.16 s; (<b>c</b>) 0.24 s; (<b>d</b>) 0.32 s.</p>
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<p>Comparison of displacement results and water level results with experimental data. (<b>a</b>) Displacement results [<a href="#B4-jmse-12-01968" class="html-bibr">4</a>,<a href="#B37-jmse-12-01968" class="html-bibr">37</a>,<a href="#B58-jmse-12-01968" class="html-bibr">58</a>]; (<b>b</b>) Water level results [<a href="#B58-jmse-12-01968" class="html-bibr">58</a>].</p>
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<p>Schematic of the in-situ cantilever beam model.</p>
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<p>Vertical displacement of the beam and velocity of the flow field at different moments. (<b>a</b>) 0.15 s; (<b>b</b>) 2.5 s; (<b>c</b>) 3.2 s; (<b>d</b>) 2.5 s.</p>
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<p>Vertical displacement of the beam and velocity of the flow field at different moments. (<b>a</b>) 0.15 s; (<b>b</b>) 2.5 s; (<b>c</b>) 3.2 s; (<b>d</b>) 2.5 s.</p>
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<p>Comparison of simulated and experimental loads for in-situ cantilever beam [<a href="#B56-jmse-12-01968" class="html-bibr">56</a>].</p>
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<p>Water–ice interaction forces under different length-to-thickness ratios.</p>
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20 pages, 10173 KiB  
Article
Variability in Water Temperature Vertical Distribution and Advective Influences: Observations from Early Summer 2021 in the Central Yellow Sea
by Baekjin Kim, Seonghyeon Kim, Soonyeol Kwon, Donhyug Kang and Eung Kim
J. Mar. Sci. Eng. 2024, 12(11), 1963; https://doi.org/10.3390/jmse12111963 - 1 Nov 2024
Viewed by 402
Abstract
To analyze variations in the vertical distribution of water temperatures and the impact of advection in the central Yellow Sea, multi-layer water temperature and current observations were conducted from 31 May to 8 June 2021. Water temperatures exhibited a typical three-layer summer structure, [...] Read more.
To analyze variations in the vertical distribution of water temperatures and the impact of advection in the central Yellow Sea, multi-layer water temperature and current observations were conducted from 31 May to 8 June 2021. Water temperatures exhibited a typical three-layer summer structure, with a uniform deep-layer temperature averaging 8.23 ± 0.05 °C. The current field was dominated by northeast–southwest tidal currents, but residual current characteristics indicated that non-tidal components significantly influenced circulation. Water temperature changes lagged tidal changes by about 3 h, with strong correlations (R > 0.7), especially in deep layers. Residual currents showed significant correlations with water temperature variations, which were attributed to advective displacement or baroclinic currents. Empirical orthogonal function (EOF) and complex EOF analyses revealed that thermocline variations (T1, explaining approximately 75% of total variance) were driven by strong northward (C1, approximately 34%) and cyclonic (C2, approximately 32%) advection. In deep layers, slight temperature changes were caused by southward Yellow Sea Cold Water Mass (C1) and northward Yellow Sea Warm Current Water (C2) propagation. This study confirms that vertical water temperature variations result from a complex interaction between various advection patterns, with southward tide-induced residual currents (C3, approximately 12%) playing a key dynamic role. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Bathymetry of the central Yellow Sea. Contours (lines and dots) indicate water depths at 10 m intervals. The stations with CTD casts are marked with red circles and the bottom mooring site is shown with a blue square.</p>
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<p>Schematic diagram of the bottom mooring observation system.</p>
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<p>(<b>a</b>) Time series of water temperatures observed with the thermistor string at the bottom mooring site A01. (<b>b</b>) Water temperatures below 30 m in depth. (<b>c</b>) Vertical profiles of water temperatures observed with the thermistor string during observation periods (black lines) and 5 CTD casts (red, dark blue, green, orange, and light blue lines).</p>
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<p>Time series of current data observed by ADCP at the bottom mooring site A01. The diagram displays velocity data at 5 m depth intervals, with a time interval of 1 h. The direction of each straight line represents the current direction, while the length and color indicate the current speed.</p>
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<p>Time series of filtered residual current (black lines) and water temperature anomaly (red lines) data at (<b>a</b>–<b>f</b>) 20 m to 70 m in depth, measured at the bottom mooring site A01.</p>
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<p>Cross-correlation analysis of water temperatures and tidal currents at (<b>a</b>–<b>f</b>) 20 to 70 m in depth, measured at the bottom mooring site A01.</p>
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<p>Cross-correlation analysis of filtered water temperature anomalies and residual currents at (<b>a</b>–<b>f</b>) 20 m to 70 m in depth.</p>
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<p>Principal components of (<b>a</b>) the top three EOF modes for the amplitude of water temperature anomalies and (<b>b</b>) the top three CEOF modes for the amplitude of residual currents based on the filtered time series with a window of 2 h–4 d. Amplitudes are normalized.</p>
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<p>(<b>a</b>) Top three EOF modes for water temperature anomalies and (<b>b</b>) top three CEOF modes for residual currents. The color at each depth indicates normalized amplitude and black arrows indicate the direction of propagation.</p>
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<p>Power spectra for the top three principal components of (<b>a</b>–<b>c</b>) temperature anomalies and (<b>d</b>–<b>f</b>) residual currents. The red stars indicate the top three significant peaks.</p>
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<p>T-S diagram of observations below 20 m taken during a cruise in early summer 2021. The results from 42 CTD stations are plotted as black diamonds, and the results from 5 CTD observations at A01 are plotted as colored circles. Seawater properties were used to classify (a) the mixing of coastal waters and (b) warm and saline waters in the thermocline, and originated waters from (c) YSCWM and (d) YSWC in the deep layer.</p>
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14 pages, 4908 KiB  
Article
Study on the Ratio and Model Test of Similar Materials of Heavily Weathered Granite
by Guofeng Hu, Weihao Song, Xinran Yu, Mingbao Lin, Yunlong Tie and Ben He
Materials 2024, 17(21), 5324; https://doi.org/10.3390/ma17215324 - 31 Oct 2024
Viewed by 376
Abstract
To study the bearing characteristics of rock-socketed single piles on the southeast coast of Fujian Province, we conducted similar material ratio tests and single pile model tests. Initially, based on the mechanical parameters of strongly weathered granite, 10 groups of similar material samples [...] Read more.
To study the bearing characteristics of rock-socketed single piles on the southeast coast of Fujian Province, we conducted similar material ratio tests and single pile model tests. Initially, based on the mechanical parameters of strongly weathered granite, 10 groups of similar material samples were prepared using iron concentrate powder, barite powder, and quartz sand as aggregates, with rosin and alcohol as the cementing agents and gypsum as the modulating agent. Through triaxial testing and range and variance analysis, it was determined that the binder concentration has the most significant impact on the material properties. Consequently, Specimen 1 was selected as the simulation material. In the model test, the strongly weathered granite stratum was simulated using the ratio of Specimen 1. A horizontal load was applied using a pulley weight system, and the displacement at the top of the pile was measured with a laser displacement meter, resulting in a horizontal load–displacement curve. The results indicated that the pile foundation remained in an elastic state until a displacement of 2.5 mm. Measurements of the horizontal displacement and bending moment of the pile revealed that the model pile behaves as a flexible pile; the bending moment initially increases along the pile length and then decreases, approaching zero at the pile’s bottom. The vertical load test analyzed the relationship between vertical load and settlement of the single pile, as well as its variation patterns. This study provides an experimental basis for the design of single pile foundations in weathered granite formations on the southeast coast of Fujian Province and aids in optimizing offshore wind power engineering practices. Full article
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<p>Raw materials of similar materials.</p>
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<p>Range analysis of elastic modulus.</p>
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<p>Cohesion range analysis.</p>
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<p>Range analysis of internal friction angle.</p>
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<p>Model box and test environment.</p>
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<p>Laser displacement meters at different positions.</p>
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<p>Strain gauge installed on model pile.</p>
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<p>Horizontal load–displacement curve.</p>
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<p>Horizontal displacement curve of pile body.</p>
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<p>Bending moment curve with 1.1 mm top displacement.</p>
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<p>Horizontal displacement curve of pile with 2.3 mm top displacement.</p>
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<p>Bending moment curve with 2.3 mm top displacement.</p>
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<p>Vertical load–settlement curve.</p>
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24 pages, 7425 KiB  
Article
Experimental Study on the Influence of Sidewall Excavation Width and Rock Wall Slope on the Stability of the Surrounding Rock in Hanging Tunnels
by Hao Zhang, Tianyu Luo, Zhao Xiang, Zhiwei Cai, Tongqing Wu, Dong Zhang, Bing Liu and Hu Feng
Buildings 2024, 14(11), 3477; https://doi.org/10.3390/buildings14113477 - 31 Oct 2024
Viewed by 409
Abstract
Hanging tunnels are a unique type of highway constructed on hard cliffs and towering mountains, renowned for their steep and distinctive characteristics. Compared to traditional full tunnels or open excavations, hanging tunnels offer significant advantages in terms of cost and construction time. However, [...] Read more.
Hanging tunnels are a unique type of highway constructed on hard cliffs and towering mountains, renowned for their steep and distinctive characteristics. Compared to traditional full tunnels or open excavations, hanging tunnels offer significant advantages in terms of cost and construction time. However, the engineering design and construction cases of such tunnels are rarely reported, and concerns about construction safety and surrounding rock stability have become focal points. Taking the Shibanhe hanging tunnel as a case study, this paper focuses on the stability of the surrounding rock during the excavation of limestone hanging tunnels using physical analog model (PAM) experiments and numerical calculation. Firstly, based on the similarity principle and orthogonal experiments, river sand, bentonite, gypsum and P.O42.5 ordinary Portland cement were selected as the raw materials to configure similar materials from limestone. Secondly, according to the characteristics of hanging tunnels, geological models were designed, and excavation experiments with three different sidewall excavation widths and rock wall slopes were carried out. The effects of these variables on the stress and displacement behavior of the surrounding rock were analyzed, and the laws of their influence on the stability of the surrounding rock were explored. Finally, numerical simulations were employed to simulate the tunnel excavation, and the results of the numerical simulations and PAM experiments were compared and analyzed to verify the reliability of the PAM experiment. The results showed that the vertical stress on the rock pillars was significantly affected by the sidewall excavation widths, with a maximum increase rate of 53.8%. The displacement of the sidewall opening top was greatly influenced by the sidewall excavation widths, while the displacement of the sidewalls was more influenced by the rock wall slope. The experimental results of the PAM are consistent with the displacement and stress trends observed in the numerical simulation results, verifying their reliability. These findings can provide valuable guidance and reference for the design and construction of hanging tunnels. Full article
(This article belongs to the Special Issue Building Foundation Analysis: Soil–Structure Interaction)
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<p>The hanging tunnels. (<b>a</b>) The half tunnel T-7 located in the Sutlej Valley of the western Himalayas, “Reprinted/adapted with permission from Ref. [<a href="#B2-buildings-14-03477" class="html-bibr">2</a>]. Copyright 2003, Anbalagan, R”; (<b>b</b>) the Ganji half tunnel located on Skardu Road near the Karakoram Highway, “Reprinted/adapted with permission from Ref. [<a href="#B3-buildings-14-03477" class="html-bibr">3</a>]. Copyright 2022, Emad, M.”; (<b>c</b>–<b>f</b>) Shibanhe hanging tunnel in Guizhou Province, “Reprinted/adapted with permission from Refs. [<a href="#B4-buildings-14-03477" class="html-bibr">4</a>,<a href="#B5-buildings-14-03477" class="html-bibr">5</a>,<a href="#B6-buildings-14-03477" class="html-bibr">6</a>]. Copyright 2019, 2020, Xianpu Han”.</p>
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<p>The hanging tunnels. (<b>a</b>) The half tunnel T-7 located in the Sutlej Valley of the western Himalayas, “Reprinted/adapted with permission from Ref. [<a href="#B2-buildings-14-03477" class="html-bibr">2</a>]. Copyright 2003, Anbalagan, R”; (<b>b</b>) the Ganji half tunnel located on Skardu Road near the Karakoram Highway, “Reprinted/adapted with permission from Ref. [<a href="#B3-buildings-14-03477" class="html-bibr">3</a>]. Copyright 2022, Emad, M.”; (<b>c</b>–<b>f</b>) Shibanhe hanging tunnel in Guizhou Province, “Reprinted/adapted with permission from Refs. [<a href="#B4-buildings-14-03477" class="html-bibr">4</a>,<a href="#B5-buildings-14-03477" class="html-bibr">5</a>,<a href="#B6-buildings-14-03477" class="html-bibr">6</a>]. Copyright 2019, 2020, Xianpu Han”.</p>
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<p>Raw materials and specimen preparation process. (<b>a</b>) The raw materials of surrounding rock; (<b>b</b>) steps in specimen preparation and maintenance.</p>
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<p>Tunnel cross-section and sidewall opening dimensions of the prototype hanging tunnel (unit: cm). (<b>a</b>) The sectional dimensions of the prototype hanging tunnel; (<b>b</b>) the dimensions of the sidewall openings.</p>
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<p>Model test platform. (<b>a</b>) Schematic diagram of hanging tunnel model; (<b>b</b>) the PAM of hanging tunnel.</p>
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<p>PAM test measuring equipment.</p>
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<p>Layout diagram of measuring test components. (<b>a</b>) Schematic diagram of stress monitoring layout section; (<b>b</b>) schematic diagram of monitoring layout section; (<b>c</b>) schematic diagram of displacement monitoring layout section.</p>
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<p>PAM elaboration scheme.</p>
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<p>PAM test steps and excavation process.</p>
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<p>Stress variation curves of surrounding rock under different sidewall excavation widths and different construction steps at a rock wall slope of 80°. (<b>a</b>) Stress of the vault and sidewall near the mountain; (<b>b</b>) stress of tunnel floor and sidewall near the cliff; (<b>c</b>) stress of rock pillar.</p>
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<p>Stress variation curves of surrounding rock under different sidewall excavation widths and different construction steps at a rock wall slope of 80°. (<b>a</b>) Stress of the vault and sidewall near the mountain; (<b>b</b>) stress of tunnel floor and sidewall near the cliff; (<b>c</b>) stress of rock pillar.</p>
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<p>Stress variation curves of surrounding rock under rock wall slopes and different construction steps. (<b>a</b>) Stress of the vault and sidewall near the mountain; (<b>b</b>) stress of the tunnel floor and sidewall near the cliff; (<b>c</b>) stress of the rock pillar.</p>
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<p>Stress variation curves of surrounding rock under rock wall slopes and different construction steps. (<b>a</b>) Stress of the vault and sidewall near the mountain; (<b>b</b>) stress of the tunnel floor and sidewall near the cliff; (<b>c</b>) stress of the rock pillar.</p>
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<p>Radial displacement variation diagrams of the surrounding rock under sidewall excavation widths. (<b>a</b>) Displacement of the vault and sidewall near the mountain; (<b>b</b>) displacement of the sidewall opening top and hance near the mountain.</p>
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<p>Radial displacement variation diagrams of the surrounding rock under different rock wall slopes. (<b>a</b>) Displacement of the vault and sidewall near the mountain; (<b>b</b>) displacement of the sidewall opening top and hance near the mountain.</p>
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<p>Radial displacement variation diagrams of the surrounding rock under different rock wall slopes. (<b>a</b>) Displacement of the vault and sidewall near the mountain; (<b>b</b>) displacement of the sidewall opening top and hance near the mountain.</p>
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<p>Relationship between surrounding rock displacement and sidewall excavation width, and rock wall slope. (<b>a</b>) Relationship diagram between the displacement of sidewall opening top, vault and the sidewall excavation width; (<b>b</b>) relationship diagram between the displacement of sidewall near mountain and rock wall slope.</p>
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<p>Relationship between surrounding rock displacement and sidewall excavation width, and rock wall slope. (<b>a</b>) Relationship diagram between the displacement of sidewall opening top, vault and the sidewall excavation width; (<b>b</b>) relationship diagram between the displacement of sidewall near mountain and rock wall slope.</p>
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<p>Overall and grid map after excavation (unit: m). (<b>a</b>) The configuration of the numerical model before excavation; (<b>b</b>) the configuration of the numerical model after excavation.</p>
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<p>Comparison of displacements obtained by numerical simulation and PAM tests.</p>
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22 pages, 8021 KiB  
Article
An Experimental Study on the Effect of GFRP and CFRP Strengthening on the Static and Dynamic Behavior of R/C Beams Under Progressive Damage
by Onur Ozturkoglu, Umut Yucel, Cihan Karademir and Erkan Durmazgezer
Appl. Sci. 2024, 14(21), 9920; https://doi.org/10.3390/app14219920 - 30 Oct 2024
Viewed by 509
Abstract
This paper aims to investigate the effect of glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) strengthening materials on the static and dynamic behavior of reinforced concrete (R/C) beams subjected to progressive damage. Four identical beams, each strengthened with either GFRP or [...] Read more.
This paper aims to investigate the effect of glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) strengthening materials on the static and dynamic behavior of reinforced concrete (R/C) beams subjected to progressive damage. Four identical beams, each strengthened with either GFRP or CFRP, are tested under a cyclic quasi-static loading pattern. Impact hammer tests are performed for undamaged states and various damage levels of the beams. The dynamic test data are analyzed using the Enhanced Frequency Domain Decomposition (EFDD) method to estimate the dynamic characteristics of the beams. In this context, the first three vibration modes in both vertical and horizontal directions are considered. Strengthening is applied to both pre-damaged and undamaged beams, enabling a comparison of their performance before and after the strengthening procedure. Beams strengthened with CFRP exhibit a higher load-bearing capacity and stiffness but also fail at lower displacement levels compared to those strengthened with GFRP, which demonstrate more ductile behavior. Furthermore, the modal frequency ratios indicate that the first vibration mode is more sensitive to damage than the second and third modes. This study highlights the effectiveness of both strengthening materials in enhancing the structural performance of both undamaged and damaged beams. Full article
(This article belongs to the Section Civil Engineering)
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<p>Reinforcement details of the beams.</p>
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<p>(<b>a</b>) GFRP and (<b>b</b>) CFRP applications on beams.</p>
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<p>(<b>a</b>) Layout of the accelerometers, (<b>b</b>) schematic presentation of the static test system, and (<b>c</b>) general view of the test setup.</p>
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<p>Load–displacement curves for Beam_GFRP_PreStr and Beam_CFRP_PreStr.</p>
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<p>The failure mode of Beam_GFRP_PreStr.</p>
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<p>The failure mode of Beam_CFRP_PreStr.</p>
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<p>Load–displacement curves of Beam_GFRP_PostStr and Beam_CFRP_PostStr.</p>
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<p>Bare beams (Beam_GFRP_PostStr and Beam_CFRP_PostStr) at the D<sub>1</sub> damage level.</p>
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<p>The failure mode of Beam_GFRP_PostStr.</p>
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<p>The failure mode of Beam_CFRP_PostStr.</p>
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<p>Mode shapes of the bare beams in the undamaged state: (<b>a</b>) first, (<b>b</b>) second, and (<b>c</b>) third mode in the vertical direction; (<b>d</b>) first, (<b>e</b>) second, and (<b>f</b>) third mode in the horizontal direction.</p>
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<p>Variation in modal frequency ratios at different damage levels for Beam_GFRP_PreStr in (<b>a</b>) the vertical direction and (<b>b</b>) the horizontal direction.</p>
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<p>Variation in modal frequency ratios at different damage levels for Beam_CFRP_PreStr in both directions.</p>
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<p>Variation in modal frequency ratios at different damage levels for Beam_GFRP_PostStr in (<b>a</b>) the vertical direction and (<b>b</b>) the horizontal direction.</p>
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<p>Variation in modal frequency ratios at different damage levels for Beam_CFRP_PostStr in (<b>a</b>) the vertical direction and (<b>b</b>) the horizontal direction.</p>
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<p>Modal frequency ratios in the vertical direction for the (<b>a</b>) first, (<b>b</b>) second, and (<b>c</b>) third modes of all beams.</p>
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<p>Modal frequency ratios in the horizontal direction for the (<b>a</b>) first, (<b>b</b>) second, and (<b>c</b>) third modes of all beams.</p>
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