[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (202)

Search Parameters:
Keywords = reverse fault

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 3600 KiB  
Article
A Novel Simplified Analysis Model to Predict Behaviors of Single Piles Subjected to Reverse Faulting
by Deping Guo, Yulin Liu, Jincai Tang, Zeng Zhang, Chaofan Yao, Yang Li and Wang Wu
Buildings 2025, 15(3), 335; https://doi.org/10.3390/buildings15030335 - 23 Jan 2025
Viewed by 323
Abstract
Pile foundations are vulnerable to fault deformations. However, both the physical and numerical modeling of pile foundations under fault deformations are complicated and time-consuming. A simplified model is required for design and engineering practices. This study proposed a novel simplified analysis model to [...] Read more.
Pile foundations are vulnerable to fault deformations. However, both the physical and numerical modeling of pile foundations under fault deformations are complicated and time-consuming. A simplified model is required for design and engineering practices. This study proposed a novel simplified analysis model to predict the behaviors of single piles subjected to reverse faulting. A two-dimensional beam–spring model is applied. The calculations of the stiffnesses of soil springs, skin friction, ultimate soil resistances, and Young’s modulus of sand are presented and discussed. The numerical results show a good agreement with the results of previous centrifuge tests. The parametric studies using the novel model show that ultimate horizontal soil resistance, skin friction, Young’s modulus of soil, pile stiffness, and sand density exhibit apparent effects on the responses of a single pile. The ultimate soil resistance controls the maximum inner forces, while Young’s modulus affects the increment of inner forces. The bending moment increases with pile stiffness initially and then remains relatively stable. Larger sand density leads to larger inner forces of the pile, owing to greater ultimate soil resistance and stiffness of the soil spring. Full article
Show Figures

Figure 1

Figure 1
<p>Two-dimensional beam–spring model.</p>
Full article ">Figure 2
<p>Distribution of ultimate horizontal soil resistance.</p>
Full article ">Figure 3
<p>Distributions of Young’s modulus with depth.</p>
Full article ">Figure 4
<p>Illustration of the test, adapted from Yao et al. [<a href="#B20-buildings-15-00335" class="html-bibr">20</a>].</p>
Full article ">Figure 5
<p>Validation of evaluated inner forces of the pile at X = 3.75 m in Case RP80: (<b>a</b>) bending moment distribution; (<b>b</b>) axial force distribution; (<b>c</b>) bending moment at Z = 5 m and −8 m; (<b>d</b>) axial force at Z= −6 m.</p>
Full article ">Figure 6
<p>Evaluated pile top behavior of the pile at X = 3.75 m in Case RP80: (<b>a</b>) δh and δv; (<b>b</b>) rotation.</p>
Full article ">Figure 7
<p>Validation of evaluated inner forces of the pile at X = 8.75 m in Case RP80: (<b>a</b>) bending moment distribution; (<b>b</b>) axial force distribution; (<b>c</b>) bending moment at Z = −3 m; (<b>d</b>) axial force at Z= −4 m.</p>
Full article ">Figure 8
<p>Evaluated top behavior of the pile at X = 8.75 m in Case RP80: (<b>a</b>) δh and δv; (<b>b</b>) rotation.</p>
Full article ">Figure 9
<p>Evaluated inner forces of the pile at X = 3.75 m with different ultimate soil resistances and skin frictions at δb = 1.2 m: (<b>a</b>) bending moment distributions under different ultimate soil resistances; (<b>b</b>) axical forces distributions under different skin frictions; (<b>c</b>) extreme bending moment under different ultimate soil resistances; (<b>d</b>) extreme axial force under different skin frictions.</p>
Full article ">Figure 10
<p>Evaluated inner forces of the pile at X = 8.75 m with different ultimate soil resistances and skin frictions at δb = 1.2 m: (<b>a</b>) bending moment distributions under different ultimate soil resistances; (<b>b</b>) axial forces distributions under different skin frictions; (<b>c</b>) extreme bending moment under different ultimate soil resistances; (<b>d</b>) extreme axial force under different skin frictions.</p>
Full article ">Figure 11
<p>Evaluated inner forces using different assumptions at δb = 1.2 m: (<b>a</b>,<b>b</b>) bending moment and axial force of the pile at X = 3.75 m, respectively; (<b>c</b>,<b>d</b>) bending moment and axial force of the pile at X = 8.75 m, respectively.</p>
Full article ">Figure 12
<p>Evaluated inner forces of the pile at X = 3.75 m with different Young’s modulus at δb = 1.2 m: (<b>a</b>) bending moment distribution; (<b>b</b>) axial force distribution; (<b>c</b>) extreme bending moment; (<b>d</b>) extreme axial force.</p>
Full article ">Figure 13
<p>Evaluated inner forces of the pile at X = 8.75 m with different Young’s modulus: (<b>a</b>) bending moment distribution; (<b>b</b>) axial force distribution; (<b>c</b>) extreme bending moment; (<b>d</b>) extreme axial force.</p>
Full article ">Figure 14
<p>Evaluated inner forces of single piles with different pile stiffness: (<b>a</b>,<b>b</b>) bending moment distributions of single piles at X = 3.75 m and 8.75, respectively; (<b>c</b>,<b>d</b>) extreme bending moments at X = 3.75 m and 8.75, respectively.</p>
Full article ">Figure 15
<p>Evaluated inner forces of the pile at X = 3.75 m with sand densities: (<b>a</b>) bending moment distribution; (<b>b</b>) axial force distribution; (<b>c</b>) extreme bending moment; (<b>d</b>) extreme axial force.</p>
Full article ">Figure 16
<p>Evaluated inner forces of the pile at X = 8.75 m with sand densities: (<b>a</b>) bending moment distribution; (<b>b</b>) axial force distribution; (<b>c</b>) extreme bending moment; (<b>d</b>) extreme axial force.</p>
Full article ">
25 pages, 7034 KiB  
Article
Diagnosis of Reverse-Connection Defects in High-Voltage Cable Cross-Bonded Grounding System Based on ARO-SVM
by Yuhao Ai, Bin Song, Shaocheng Wu, Yongwen Li, Li Lu and Linong Wang
Sensors 2025, 25(2), 590; https://doi.org/10.3390/s25020590 - 20 Jan 2025
Viewed by 410
Abstract
High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath [...] Read more.
High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath grounding fault. However, there is a paucity of research on the defect of the reverse direction between the inner core and the outer shield of the coaxial cable. Firstly, this paper performed a theoretical analysis of the sheath current in the reversed-connection state and established a simulation model for verification. The outcomes of the simulation demonstrate that there are significant variations in the amplitudes of the sheath current under different reversed-connection conditions. Consequently, a feature vector was devised based on the amplitude of the sheath current. The support vector machine (SVM) was then applied to diagnose the reversed-connection defects in the HV cable cross-bonded grounding system. The artificial rabbits optimization (ARO) algorithm was adopted to optimize the SVM model, attaining an impressively high diagnostic accuracy rate of 99.35%. The effectiveness and feasibility of the proposed algorithm are confirmed through the analysis and validation of the practical example. Full article
Show Figures

Figure 1

Figure 1
<p>Model of cross-bonded grounding system for HV cables.</p>
Full article ">Figure 2
<p>Schematic diagram of the flow direction of the leakage current of the cable in minor section A<sub>1</sub>.</p>
Full article ">Figure 3
<p>Equivalent induced circuit of the metal sheath of the cross-bonded cable.</p>
Full article ">Figure 4
<p>Schematic diagram of coaxial cable joint.</p>
Full article ">Figure 5
<p>Equivalent circuit diagram when J<sub>A1</sub> is reversed.</p>
Full article ">Figure 6
<p>Equivalent circuit diagram when J<sub>A1</sub> and J<sub>B1</sub> are reversed.</p>
Full article ">Figure 7
<p>Equivalent circuit diagram when J<sub>A1</sub> and J<sub>A2</sub> are reversed.</p>
Full article ">Figure 8
<p>Equivalent circuit diagram when J<sub>A1</sub> and J<sub>B2</sub> are reversed.</p>
Full article ">Figure 9
<p>Equivalent circuit diagram when J<sub>A1</sub> and J<sub>C2</sub> are reversed.</p>
Full article ">Figure 10
<p>Simulation model.</p>
Full article ">Figure 11
<p>Waveform diagram of sheath current when the cable is in normal operation: (<b>a</b>) <span class="html-italic">I</span><sub>la1</sub> and <span class="html-italic">I</span><sub>lc2</sub>; (<b>b</b>) <span class="html-italic">I</span><sub>lb1</sub> and <span class="html-italic">I</span><sub>la2</sub>; and (<b>c</b>) <span class="html-italic">I</span><sub>lc1</sub> and <span class="html-italic">I</span><sub>lb2</sub>.</p>
Full article ">Figure 12
<p>Waveform diagram of sheath current when J<sub>A1</sub> is reversed: (<b>a</b>) <span class="html-italic">I</span><sub>la1</sub> and <span class="html-italic">I</span><sub>lc2</sub>; (<b>b</b>) <span class="html-italic">I</span><sub>lb1</sub> and <span class="html-italic">I</span><sub>la2</sub>; and (<b>c</b>) <span class="html-italic">I</span><sub>lc1</sub> and <span class="html-italic">I</span><sub>lb2</sub>.</p>
Full article ">Figure 13
<p>Waveform diagram of sheath current when the cable is in two-reversal operation: (<b>a</b>) J<sub>A1</sub> and J<sub>B1</sub> are reversed; (<b>b</b>) J<sub>A1</sub> and J<sub>A2</sub> are reversed; (<b>c</b>) J<sub>A1</sub> and J<sub>B2</sub> are reversed; and (<b>d</b>) J<sub>A1</sub> and J<sub>C2</sub> are reversed.</p>
Full article ">Figure 13 Cont.
<p>Waveform diagram of sheath current when the cable is in two-reversal operation: (<b>a</b>) J<sub>A1</sub> and J<sub>B1</sub> are reversed; (<b>b</b>) J<sub>A1</sub> and J<sub>A2</sub> are reversed; (<b>c</b>) J<sub>A1</sub> and J<sub>B2</sub> are reversed; and (<b>d</b>) J<sub>A1</sub> and J<sub>C2</sub> are reversed.</p>
Full article ">Figure 14
<p>Flowchart of ARO algorithm [<a href="#B26-sensors-25-00590" class="html-bibr">26</a>].</p>
Full article ">Figure 15
<p>Flowchart of the defect diagnosis model based on ARO-SVM.</p>
Full article ">Figure 16
<p>Actual wiring method.</p>
Full article ">
12 pages, 2538 KiB  
Article
A Fault Diagnosis Method for Turnout Switch Machines Based on Sound Signals
by Yong Li, Xinyi Tao and Yongkui Sun
Electronics 2024, 13(23), 4839; https://doi.org/10.3390/electronics13234839 - 7 Dec 2024
Viewed by 609
Abstract
The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, [...] Read more.
The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, ReliefF, and SVM), for effective monitoring and detection of turnout switch machines. The method employs Eigenmode Decomposition (EMD) to smooth the sound signal, reduce noise, and extract key statistical parameters of both the time and frequency domains. To address redundant information in high-dimensional features, the ReliefF algorithm is utilized for feature selection, dimension reduction, and fault classification based on weighted parameters. Subsequently, the selected feature parameters are used to train the Support Vector Machine (SVM). A comparison with results obtained without ReliefF feature selection demonstrates the necessity of this step. The results show that the fault diagnosis accuracy reaches 98% in the positioning work mode and 95.67% in the reversing work mode, verifying the method’s effectiveness and feasibility. Full article
Show Figures

Figure 1

Figure 1
<p>Execution process of ERS.</p>
Full article ">Figure 2
<p>Internal view of the turnout switch machine.</p>
Full article ">Figure 3
<p>Ten types of time domain waveforms in the positioning work. Note that the x-coordinate of all figures denotes the time (s).</p>
Full article ">Figure 4
<p>The first 16 IMFs of Type A sound signals.</p>
Full article ">Figure 5
<p>Confusion matrix (CM) for full-feature testing. Noted: Red numbers denote the accuracy, and the depth of color in the picture indicates the magnitude of the value. (<b>a</b>) Positioning work mode; (<b>b</b>) reversing work mode.</p>
Full article ">Figure 6
<p>Confusion matrix (CM) for dimensionality reduction feature testing. Noted: Red numbers denote the accuracy, and the depth of color in the picture indicates the magnitude of the value. (<b>a</b>) Positioning work mode; (<b>b</b>) reversing work mode.</p>
Full article ">
16 pages, 7193 KiB  
Article
Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
by Junyan Han, Yansong Bi, Benwei Hou, Wenle Zhao and Mohamed Hesham El Naggar
Appl. Sci. 2024, 14(23), 10896; https://doi.org/10.3390/app142310896 - 25 Nov 2024
Viewed by 648
Abstract
Corroded steel pipelines are particularly vulnerable to failure due to ground movement, which highlights the need to improve their seismic resistance through reinforcement methods. This paper establishes a three-dimensional finite element model of a corroded steel pipeline subjected to a reverse fault, which [...] Read more.
Corroded steel pipelines are particularly vulnerable to failure due to ground movement, which highlights the need to improve their seismic resistance through reinforcement methods. This paper establishes a three-dimensional finite element model of a corroded steel pipeline subjected to a reverse fault, which considers the effects of the corrosion position and depth, winding thickness, and length of carbon fiber-reinforced polymer (CFRP), to investigate the stress, strain, elliptic deformation, and failure modes of the pipeline before and after CFRP reinforcement. Results indicate that the main failure mode of the intact and corroded pipeline crossing the reverse fault is local buckling. Corrosion intensifies the response of the cross-fault pipeline, accelerates its failure occurrence, and promotes transformation from a single failure mode to multiple failure modes. For CFRP reinforcement, an increase in CFRP winding thickness can effectively inhibit the growth of the pipeline’s compressive strain, thus reducing the buckling potential. Each additional CFRP layer can further enhance the overall buckling resistance but at a decreasing rate. Similarly, longer CFRP winding improves buckling resistance though the effectiveness per meter decreases. Therefore, it is recommended that the thickness and length of CFRP winding on the pipeline should be optimized to obtain the best reinforcement at a reasonable cost. Full article
(This article belongs to the Special Issue Seismic Analysis and Design of Ocean and Underground Structures)
Show Figures

Figure 1

Figure 1
<p>Reverse fault-pipeline 3D finite element model.</p>
Full article ">Figure 2
<p>The Ramberg–Osgood constitutive model of X65 steel.</p>
Full article ">Figure 3
<p>Schematic diagram of pipeline mode with local corrosion. (<b>a</b>) Front cross-sectional view of the corroded pipeline. (<b>b</b>) Longitudinal section diagram of the corroded pipeline.</p>
Full article ">Figure 4
<p>A comparison of numerical calculation and Jalali’s test. (<b>a</b>) Local buckling of the steel pipeline. (<b>b</b>) The axial strain of the pipe’s top.</p>
Full article ">Figure 5
<p>The axial strain of the pipeline’s top with corrosion at the hanging wall. (<b>a</b>) The axial strain of the pipe’s top. (<b>b</b>) Enlarged image.</p>
Full article ">Figure 6
<p>Stress diagram of the pipeline with corrosion at the hanging wall.</p>
Full article ">Figure 7
<p>The axial strain of the pipeline’s top with corrosion at the footwall. (<b>a</b>) The axial strain of the pipe’s top. (<b>b</b>) Enlarged image.</p>
Full article ">Figure 8
<p>The stress diagram of the pipeline with corrosion at the footwall.</p>
Full article ">Figure 9
<p>The peak axial strain of the pipeline with corrosion at the hanging wall. (<b>a</b>) Hanging wall section. (<b>b</b>) Footwall section.</p>
Full article ">Figure 10
<p>Peak ellipticity of the pipeline with corrosion at the hanging wall.</p>
Full article ">Figure 11
<p>The peak axial strain of the pipeline with corrosion at the footwall. (<b>a</b>) Hanging wall section. (<b>b</b>) Footwall section.</p>
Full article ">Figure 12
<p>Peak ellipticity of the pipeline with corrosion at the footwall.</p>
Full article ">Figure 13
<p>A mechanical response of the pipeline with different CFRP thicknesses. (<b>a</b>) Peak axial strain. (<b>b</b>) Peak ellipticity.</p>
Full article ">Figure 14
<p>A mechanical response of the pipeline with different reinforcement lengths. (<b>a</b>) Peak axial strain. (<b>b</b>) Peak ellipticity.</p>
Full article ">Figure 15
<p>The stress diagram of the corroded pipeline before and after CFRP reinforcement.</p>
Full article ">Figure 16
<p>Cross-section deformation of the pipeline before and after CFRP reinforcement.</p>
Full article ">
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 581
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)
Show Figures

Figure 1

Figure 1
<p>Model’s presentation for normal and reverse fault disruption.</p>
Full article ">Figure 2
<p>Typical mesh and shear strain increments of the model (<b>a</b>) before and (<b>b</b>) after fault rupture propagation.</p>
Full article ">Figure 3
<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>
Full article ">Figure 4
<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>
Full article ">Figure 5
<p>Fragility curves of EmbA for (<b>a</b>) minor, (<b>b</b>) moderate, and (<b>c</b>) extensive damage state.</p>
Full article ">Figure 6
<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>
Full article ">Figure 7
<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>
Full article ">Figure 8
<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>
Full article ">
20 pages, 20361 KiB  
Article
The Seismic Surface Rupture Zone in the Western Segment of the Northern Margin Fault of the Hami Basin and Its Causal Interpretation, Eastern Tianshan
by Hao Sun, Daoyang Yuan, Ruihuan Su, Shuwu Li, Youlin Wang, Yameng Wen and Yanwen Chen
Remote Sens. 2024, 16(22), 4200; https://doi.org/10.3390/rs16224200 - 11 Nov 2024
Viewed by 800
Abstract
The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced [...] Read more.
The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M71/2. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M71/2 earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard. Full article
Show Figures

Figure 1

Figure 1
<p>Seismotectonic map of Eastern Tianshan: (<b>a</b>) the location of Eastern Tianshan; (<b>b</b>) primary active faults and earthquakes in Eastern Tianshan; (<b>c</b>) the distribution features of HMNF. TNF: The Northern Margin Fault of Turpan Basin; J-LF: Jianquanzi–Luobaoquan Fault; ZFF: Zhifang Fault; BSF: The Southern Margin Fault of Barkol Basin; BNF: The Northern Margin Fault of Barkol Basin; HMNF: The Northern Margin Fault of Hami Basin; K-YSF: The Southern Margin Fault of Kuisu-Yiwu Basin; KCF: The Central Fault of Karlik Mountains; WZXF: Weizixia Fault; XMYF: Xiamaya Fault; YWSF: The Southern Margin Fault of Yiwu Basin; GTSFS: Gobi–Tianshan Sinistral Strike-Slip Faults. The base map is based on 30 m DEM of USGS [<a href="#B19-remotesensing-16-04200" class="html-bibr">19</a>]. The fault data are modified from studies [<a href="#B9-remotesensing-16-04200" class="html-bibr">9</a>,<a href="#B11-remotesensing-16-04200" class="html-bibr">11</a>,<a href="#B13-remotesensing-16-04200" class="html-bibr">13</a>,<a href="#B20-remotesensing-16-04200" class="html-bibr">20</a>]. The earthquake dates are from the China Earthquake Catalogue (1831BC-1969AD) [<a href="#B21-remotesensing-16-04200" class="html-bibr">21</a>] and the National Earthquake Date Centre [<a href="#B22-remotesensing-16-04200" class="html-bibr">22</a>].</p>
Full article ">Figure 2
<p>Features of active tectonics, seismic rupture zone, and landform in Liushugou segment: (<b>a</b>) original geomorphic features showed by the Hillshade of DEM; (<b>b</b>) geomorphic surface in Liushugou; (<b>c</b>) the profile of P1.</p>
Full article ">Figure 3
<p>SFM photography schematic of UAV (UAV-DJ Phantom 4 Pro V2.0, GCP-Ground Control Point).</p>
Full article ">Figure 4
<p>The distribution characteristics of the Liushugou rupture zone and UAV aerial photography areas (displayed in Google Earth satellite imagery; the light gray-white thin stripes inside the yellow solid line box are the seismic surface rupture zones): (<b>A</b>) The distribution of the seismic surface rupture zone and its spatial relationship with the Xiongkuer Rupture Zone and the epicentral area around Barkol County of 1842 M7<sup>1</sup>/<sub>2</sub> earthquake; (<b>a</b>–<b>e</b>) The entire distribution of the seismic surface rupture zone; (<b>a</b>–<b>d</b>) The most obvious and typical phenomenon of the Liushugou rupture zone; (<b>a</b>,<b>c</b>–<b>e</b>) The UAV aerial survey areas.</p>
Full article ">Figure 5
<p>Features of seismic rupture zone in Kekejin segment: (<b>a</b>) the distribution of the surface rupture zone in the Kekejin segment; (<b>b</b>) the UAV aerial survey area and the original geomorphic features showed by the Hillshade of DEM; (<b>c</b>) geomorphic surface and distribution of the rupture zone; (<b>d</b>–<b>f</b>) typical photos of seismic rupture scarp (the red arrows indicate the seismic rupture scarp).</p>
Full article ">Figure 6
<p>Features of seismic rupture zone in Daipuseke Bulake segment: (<b>a</b>) original geomorphic features showed by the Hillshade of DEM; (<b>b</b>) geomorphic surfaces and the distribution of the rupture zone; (<b>c</b>–<b>f</b>) typical photos of seismic rupture scarp (the red arrows indicate the seismic rupture scarp).</p>
Full article ">Figure 7
<p>Distribution and features of seismic rupture zone in Liushugou segment: (<b>a</b>) geomorphic surfaces and the distribution of the rupture zone; (<b>b</b>) the profile of P0, showing Fan2 fold deformation and seismic rupture scarps on it; (<b>c</b>) the profile of the maximum offset.</p>
Full article ">Figure 8
<p>Typical photos of the seismic rupture zone in the Liushugou segment (the red arrows indicate the seismic rupture scarp): (<b>a</b>) the maximum vertical offset of the seismic rupture scarp; (<b>b</b>) the seismic rupture scarp west of the maximum vertical offset point; (<b>c</b>,<b>d</b>) the seismic rupture scarp on the fold; (<b>e</b>) the seismic rupture sca rp on Fan2; (<b>f</b>) the seismic rupture scarp on the Terrace1 of a gully on the west of Liushugou.</p>
Full article ">Figure 9
<p>Distribution features and typical photos of seismic rupture zone in Wuzun Bulake segment: (<b>a</b>) original geomorphic features showed by the Hillshade of DEM; (<b>b</b>) geomorphic surfaces and the distribution of the rupture zone; (<b>c</b>,<b>d</b>,<b>f</b>) the seismic rupture scarps in alluvial flat (the white dashed line represents the topography, the red dashed line indicates the fault, and the red arrow indicates the seismic rupture scarp); (<b>e</b>) the fault profile on the east sidewall of the gully (The red arrows indicate the motion of the reverse fault).</p>
Full article ">Figure 10
<p>Coseismic vertical offset distribution map of Liushugou rupture zone.</p>
Full article ">Figure 11
<p>Characteristics of seismic rupture zone and its surrounding geomorphic surfaces: (<b>a</b>,<b>b</b>) the features of the dirt roads; (<b>c</b>,<b>d</b>) features of the scarp of the seismic rupture zone on the forelimb of Liushugou Fan2 fold; (<b>e</b>) features of the older scarp in Liushugou.</p>
Full article ">Figure 12
<p>Isoseismal lines of 1842 and 1914 historical earthquakes near Barkol (modified from Gu et al. [<a href="#B21-remotesensing-16-04200" class="html-bibr">21</a>]): (<b>a</b>) the location index map of the study area.; (<b>b</b>) the distribution of isoseismal lines of two major historical earthquakes and the main active faults in the Eastern Tianshan.</p>
Full article ">Figure 13
<p>Comparison of the features between the Xiongkur and the Liushugou seismic rupture zone (the red arrows indicate the seismic rupture scarp): (<b>a</b>–<b>c</b>) the features of the Xiongkuer seismic rupture zone (Wu, 2016 [<a href="#B9-remotesensing-16-04200" class="html-bibr">9</a>]); (<b>d</b>–<b>f</b>) the features of the Liushugou seismic rupture zone.</p>
Full article ">Figure 14
<p>Formation model of Liushugou seismic rupture zone (Wu, 2016 [<a href="#B9-remotesensing-16-04200" class="html-bibr">9</a>]).</p>
Full article ">
16 pages, 5035 KiB  
Article
Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks
by Chengsheng Pan, Huangjie Lu, Huaifeng Shi, Yingzhi Wang and Lishang Qin
Electronics 2024, 13(22), 4332; https://doi.org/10.3390/electronics13224332 - 5 Nov 2024
Viewed by 677
Abstract
The current Internet of Things (IoT) network structure is evolving from small-scale distributed systems to a large-scale hierarchical collaboration between backbone and access networks. In this context, the dynamic changes in backbone node connections and the surge in service demands, coupled with sluggish [...] Read more.
The current Internet of Things (IoT) network structure is evolving from small-scale distributed systems to a large-scale hierarchical collaboration between backbone and access networks. In this context, the dynamic changes in backbone node connections and the surge in service demands, coupled with sluggish fault detection speeds, significantly shorten effective service transmission time. To address this issue, this paper proposes an inverse coupled simulated annealing for enhanced OSPF route convergence in IoT networks (OSPF-ICSA). Initially, the link state is derived from the statistical characteristics of Hello packets, while the aggregated characteristics of the link state are employed to characterize the node state, providing data support for the reverse coupled simulated annealing algorithm. Subsequently, the Hello packet is refined, and a mechanism is designed to synchronize OSPF intervals and transmit node states. This ensures that nodes within the same subnet synchronize their sending intervals and fault detection times while sharing their node states. Finally, building upon this foundation, the reverse coupled simulated annealing algorithm is introduced to jointly optimize the Hello packet sending interval and fault detection time. Compared to the traditional AODV protocol, OSPF-ICSA reduces the average fault detection time by over 37.38%, improves the average fault detection accuracy by more than 3.1%, decreases the average routing overhead by over 20%, and increases the average packet delivery rate by over 5.1%. Full article
(This article belongs to the Special Issue Featured Advances in Real-Time Networks)
Show Figures

Figure 1

Figure 1
<p>OSPF routing convergence time distribution diagram.</p>
Full article ">Figure 2
<p>OSPF-ICSA architecture diagram.</p>
Full article ">Figure 3
<p>Link state diagram.</p>
Full article ">Figure 4
<p>OSPF interval synchronization mechanism operating process diagram.</p>
Full article ">Figure 5
<p>Comparison of link failure count vs. fault detection time.</p>
Full article ">Figure 6
<p>Comparison of node failure count vs. fault detection time.</p>
Full article ">Figure 7
<p>Comparison of link failure count vs. fault detection accuracy.</p>
Full article ">Figure 8
<p>Comparison of node failure count vs. fault detection accuracy.</p>
Full article ">Figure 9
<p>Comparison of link failure count vs. routing overhead.</p>
Full article ">Figure 10
<p>Comparison of node failure count vs. routing overhead.</p>
Full article ">Figure 11
<p>Comparison of link failure count vs. packet delivery rate.</p>
Full article ">Figure 12
<p>Comparison of node failure count vs. packet delivery rate.</p>
Full article ">
16 pages, 14227 KiB  
Article
Westward Migration of the Chenghai–Jinsha Drainage Divide and Its Implication for the Initiation of the Chenghai Fault
by Shuang Bian, Xibin Tan, Yiduo Liu, Feng Shi and Junfeng Gong
Remote Sens. 2024, 16(18), 3471; https://doi.org/10.3390/rs16183471 - 19 Sep 2024
Cited by 2 | Viewed by 793
Abstract
The Chenghai Fault in the Chuan–Dian block terminates at the northwestern segment of the Red River Fault, and is a significant seismogenic structure. The kinematic evolution of this fault should be closely related to the regional tectonic deformation. However, it is difficult to [...] Read more.
The Chenghai Fault in the Chuan–Dian block terminates at the northwestern segment of the Red River Fault, and is a significant seismogenic structure. The kinematic evolution of this fault should be closely related to the regional tectonic deformation. However, it is difficult to obtain information on structural deformation of the Chenghai Fault due to the large amount of precipitation and well-developed vegetation. The Chenghai normal faulting may drive drainage reorganization in this region, which provides a new perspective for reconstructing and evaluating the tectonic history. High-resolution digital elevation models (DEM) obtained by remote sensing greatly facilitate the study of drainage evolution and active tectonics. We use two methods (χ-plot and Gilbert metrics) to measure the drainage divide stability based on the ALOS DEM (12.5 m resolution) and further reproduce the drainage evolution process in response to the asymmetric uplift by numerical modeling. The results show that the Chenghai–Jinsha drainage divide, hosted by the footwall block of the Chenghai Fault, is migrating westward (away from the Chenghai Fault) and will continue moving ~2.2–3.5 km to reach a steady state. Its migration is controlled by the Chenghai normal faulting. The Chenghai–Jinsha drainage divide formed close to the Chenghai Fault’s surface trace and continues to migrate westward in response to the asymmetric uplift. It only took a few million years for the Chenghai–Jinsha drainage divide to migrate to its current location based on the numerical modeling. The restoration of the drainage reorganization implies that the Chenghai Fault initiated in the Pliocene, which probably results from kinematic reversal along the Red River Fault. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) Schematic tectonic map of the Tibetan Plateau region (compiled from [<a href="#B4-remotesensing-16-03471" class="html-bibr">4</a>]). (<b>B</b>) Major faults in the eastern Tibetan Plateau (compiled from [<a href="#B44-remotesensing-16-03471" class="html-bibr">44</a>]). The light blue lines are the river systems. RRF, Red River Fault; CHF: Chenghai Fault; LMF: Longmenshan Fault; SGF: Sagaing Fault; XJF: Xiangjiang Fault; XSHF: Xianshuihe Fault.</p>
Full article ">Figure 2
<p>Perspective views and χ map of channels for the Chenghai–Jinsha drainage divide. The location is shown in <a href="#remotesensing-16-03471-f001" class="html-fig">Figure 1</a>B. (<b>A</b>) Perspective views of channels mapped with <span class="html-italic">k<sub>sn</sub></span>. Arrows indicate the migration results based on the Gilbert metrics method. (<b>B</b>) Map of χ and geology. Blue filling represents sedimentary rocks, yellow filling represents Quaternary sediments, and transparent filling represents igneous rocks. Arrows show the divide migration directions and cross-divide difference in normalized <span class="html-italic">k<sub>sn</sub></span>. (<b>C</b>) χ-plots for nine paired rivers across the divide. Numbers in the χ-plots are the average <span class="html-italic">k<sub>sn</sub></span> values. The results show that the Chenghai–Jinsha drainage divide is moving west.</p>
Full article ">Figure 3
<p>(<b>A</b>) Schematic of Gilbert’s (1877) [<a href="#B61-remotesensing-16-03471" class="html-bibr">61</a>] ‘Law of Unequal Declivities’. (<b>B</b>) Reference drainage area used in all metrics for calculating across divide differences (compiled from [<a href="#B33-remotesensing-16-03471" class="html-bibr">33</a>]).</p>
Full article ">Figure 4
<p>(<b>A</b>) Perspective views for a captured river on the Chenghai–Jinsha drainage divide. The Chenghai Drainage has just captured the headwaters of the Jinsha Drainage. The location is showed in the white box of the <a href="#remotesensing-16-03471-f002" class="html-fig">Figure 2</a>A. (<b>B</b>) χ-plots for the beheaded and captured channels. They are highlighted as bold lines in (<b>A</b>).</p>
Full article ">Figure 5
<p>Divide stability analysis of the Chenghai–Jinsha drainage divide using the Gilbert metrics method [<a href="#B33-remotesensing-16-03471" class="html-bibr">33</a>]. (<b>A</b>) Divide metric histograms for segments <span class="html-italic">ab</span>, <span class="html-italic">bc</span>, and <span class="html-italic">cd</span>. Histograms with black and red rectangles represent watersheds on the western and eastern side of the drainage divide, respectively. (<b>B</b>) Standardized delta plot for the sub-segment of the Chenghai–Jinsha drainage divide. Bars show the standard deviation at 1σ level. Locations of the letters <span class="html-italic">a–d</span> are shown in <a href="#remotesensing-16-03471-f002" class="html-fig">Figure 2</a>A.</p>
Full article ">Figure 6
<p>Prediction for the steady-state location of the Chenghai–Jinsha drainage divide. (<b>A</b>) Topography and drainage system. River segments highlighted in dark blue are measured and analyzed. The pink area is the predicted steady-state location. (<b>B</b>) The Hack’s coefficient and exponent (<span class="html-italic">k</span> and <span class="html-italic">b</span>). (<b>C</b>) The relationship diagram between the normalized drainage divide location (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>β</mi> </mrow> </msub> <mo>⁄</mo> <mo>(</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </msub> <mo>+</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>β</mi> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math>) and uplift rate ratio (<span class="html-italic">U</span><span class="html-italic"><sub>β</sub></span>/<span class="html-italic">U</span><span class="html-italic"><sub>α</sub></span>). (<b>D</b>) Swath profile A–A′ of topography across the divide. Location of the swath is marked by the yellow line in panel (<b>A</b>). The red arrow represents the direction of drainage divide migration.</p>
Full article ">Figure 7
<p>Topographic maps of divide migration in response to different asymmetric uplift patterns. The top illustrations show the setting of uplift rate. All the models show it will only take several million years for the divide to migrate for ~2.5 km (the location of the vertical dashed lines). The timescale decreases as the uplift rate on the eastern edge and the uplift radio increase. When each model was run for 23 Myr, the divide migration distances are greater than 5 km.</p>
Full article ">Figure 8
<p>Diagrams of the evolution process of the Chenghai–Jinsha drainage divide. (<b>A</b>) Before the activity of the Chenghai Fault, we inferred that the rivers flowed westward to the Jinsha Drainage. (<b>B</b>) When the normal faulting began, the Chenghai–Jinsha drainage divide first occurred at the eastern edge and migrated westward until reaching a steady state. (<b>C</b>) Schematic of the post-capture profile incision. The modern river profiles are extracted based on DEM, while paleo river profile is speculated. The red arrow represents the direction of drainage divide migration. (<b>D</b>) Elevation profile along the drainage divide (A–A′). The arrows indicate the locations of wind gaps.</p>
Full article ">Figure 9
<p>Tectonic evolution of the southeastern Tibetan Plateau (modified after [<a href="#B13-remotesensing-16-03471" class="html-bibr">13</a>]). (<b>A</b>) Affected by the continuous northward indentation of the Indian Plate, the southeastern Tibetan Plateau experienced large-scale lateral extrusion and the Red River Fault synchronously initiated sinistral shearing motion. (<b>B</b>) Fault systems have undergone extensive kinematic reversal. Right-lateral faulting on the Red River Fault and normal faulting in the northern Chuan–Dian block may be the consequences of the present extrusion phase.</p>
Full article ">
14 pages, 9837 KiB  
Article
Cenozoic Reactivation of the Penacova-Régua-Verin and Manteigas-Vilariça-Bragança Fault Systems (Iberian Peninsula): Implication in Their Seismogenic Potential
by Sandra González-Muñoz and Fidel Martín-González
Geosciences 2024, 14(9), 243; https://doi.org/10.3390/geosciences14090243 - 10 Sep 2024
Viewed by 815
Abstract
The Penacova-Régua-Verin (PRV) and the Manteigas-Vilariça-Bragança (MVB) are two of the longest faults of the Iberian Peninsula. These faults striking NNE–SSW, over lengths of >200 km, were developed during late-Variscan Orogeny and reactivated in response to the Alpine Cycle tectonics. Their tectonic evolution [...] Read more.
The Penacova-Régua-Verin (PRV) and the Manteigas-Vilariça-Bragança (MVB) are two of the longest faults of the Iberian Peninsula. These faults striking NNE–SSW, over lengths of >200 km, were developed during late-Variscan Orogeny and reactivated in response to the Alpine Cycle tectonics. Their tectonic evolution during Alpine compression (Cenozoic) and their implication in the active tectonic activity of Iberia are under discussion. Their recent tectonic activity is recorded in the vertical offset of geomorphological surfaces, in the associated pull-apart basins, and in M > 7 paleoseismic events. Based on the vertical surface offset of Pliocene surfaces (140–300 m for the MVB fault and 150–200 m for the PRV), together with the horizontal offset (1300–1600 m for MVBF fault and 600–1400 m for PRVF), we can conclude that they were reactivated as left-lateral strike-slip faults with a reverse component during the Pliocene (3.6 Ma)–present. These results indicate that these faults are not related to the strain transmission during the collision with Eurasia (Eocene–Oligocene). However, they are related to the intraplate strain of the southern collision with the African plate during the Upper Neogene. The estimated slip-rate is 0.2–0.5 mm/a for both faults. These slip-rates evidence important implications for the seismic hazard of this intraplate region. Full article
(This article belongs to the Section Structural Geology and Tectonics)
Show Figures

Figure 1

Figure 1
<p>Schematic geological map of the Iberian Massif and the location of the study area. Modified from [<a href="#B12-geosciences-14-00243" class="html-bibr">12</a>,<a href="#B29-geosciences-14-00243" class="html-bibr">29</a>].</p>
Full article ">Figure 2
<p>Tectonic models proposed for the PRV and MVB faults during the Cenozoic: (<b>a</b>) Model proposed by (e.g., [<a href="#B5-geosciences-14-00243" class="html-bibr">5</a>,<a href="#B9-geosciences-14-00243" class="html-bibr">9</a>]). (<b>b</b>) The model proposed by (e.g., [<a href="#B13-geosciences-14-00243" class="html-bibr">13</a>,<a href="#B14-geosciences-14-00243" class="html-bibr">14</a>]).</p>
Full article ">Figure 3
<p>(<b>a</b>) Panoramic view toward the south of the Vilariça pull-apart basin, showing the uplift of the western block across the fundamental surface. The number corresponds to the topographic profile in <a href="#geosciences-14-00243-f005" class="html-fig">Figure 5</a>. (<b>b</b>) Field picture of the pre-tectonic sediments (arkoses of Fm. Vilariça). (<b>c</b>) Field picture of the syn-tectonic sediments (conglomerates with blocks of quartzite and angular granites immersed in a sandy matrix, Fm. Bragança). (<b>d</b>) Field aspect of the fault breccia of the PRVF northern part. (<b>e</b>) Field picture of the Viana del Bollo basin and its syn-tectonic sediments, consisting mainly of sandy matrix conglomerates and quartzite cobbles. Note the 30° tilting, indicating the fault activity after its deposits. (<b>f</b>) Field picture of the syn-tectonic sediments in Viana do Bolo basin. Locations shown in <a href="#geosciences-14-00243-f004" class="html-fig">Figure 4</a>.</p>
Full article ">Figure 4
<p>(<b>a</b>) Geological map of the traces and associated basins of the PRV and MVB faults. (<b>b</b>) Detailed map of the Chaves, Vila Real, and Telões basins. (<b>c</b>) Detailed map of the Mórtagua basin. (<b>d</b>) Detailed map of the Vilariça basin. (<b>e</b>) Detailed map of the Longroiva basin. (GLM) Galaico-Leoneses Mountains; (PCS) Portugal Central System.</p>
Full article ">Figure 5
<p>Main surfaces in the study area, with the vertical offset value measured and the localization of the profiles in <a href="#geosciences-14-00243-f006" class="html-fig">Figure 6</a>. Modified and improved from [<a href="#B21-geosciences-14-00243" class="html-bibr">21</a>,<a href="#B33-geosciences-14-00243" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00243" class="html-bibr">34</a>].</p>
Full article ">Figure 6
<p>Profiles of surfaces through the PRV and MVB fault systems. The location of the profiles is shown in <a href="#geosciences-14-00243-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure 7
<p>(<b>a</b>) Schema of the excess area technique. Modified from [<a href="#B45-geosciences-14-00243" class="html-bibr">45</a>]. (<b>b</b>) Digital elevation model map with the localization of the profiles (yellow lines) used for the area restoration technique. (<b>c</b>) Profiles and total uplift area for the Cantabrian Mountain (A-A′) and Galaico-Leoneses Mountains (B-B′).</p>
Full article ">
21 pages, 24384 KiB  
Article
Analysis of Failure Mechanism of Medium-Steep Bedding Rock Slopes under Seismic Action
by Xiuhong Zheng, Qihua Zhao, Sheqin Peng, Longke Wu, Yanghao Dou and Kuangyu Chen
Sustainability 2024, 16(17), 7729; https://doi.org/10.3390/su16177729 - 5 Sep 2024
Viewed by 790
Abstract
Medium-steep bedding rock slopes (MBRSs) are generally considered relatively stable, because the dip angle of the rock layers (45–55°) is larger than the slope angle (40–45°). However, the stability of MBRSs was significantly impacted during the 1933 Diexi earthquake, leading to slope instability. [...] Read more.
Medium-steep bedding rock slopes (MBRSs) are generally considered relatively stable, because the dip angle of the rock layers (45–55°) is larger than the slope angle (40–45°). However, the stability of MBRSs was significantly impacted during the 1933 Diexi earthquake, leading to slope instability. Field investigations revealed that no continuous sliding surface was recognized in the failure slopes. Instead, the source areas of landslides present a “reverse steps” feature, where the step surfaces are perpendicular to the bedding surface, and their normal directions point towards the crest of the slopes. These orientations of “reverse steps” differ significantly from those of steps formed under static conditions, which makes it difficult to explain the phenomenon using traditional failure mechanism of the slope. Therefore, a large-scale shaking table test was conducted to replicate the deformation and failure processes of MBRSs under seismic action. The test revealed the elevation amplification effect, where the amplification factors of the acceleration increased with increasing elevation. As the amplitude of the input seismic wave increased, the acceleration amplification factor initially rose and subsequently decreased with the increase in the shear strain of the rock mass. The dynamic response of the slope under Z-direction seismic waves is stronger than that under X-direction seismic waves. The deformation and failure were mainly concentrated in the upper part of the slope, which was in good agreement with the field observations. Based on these findings, the deformation and failure mechanism of MBRSs was analyzed by considering both the spatial relationship between the seismogenic fault and the slope, and the propagation characteristics of seismic waves along the slope. The seismic failure mode of MBRSs in the study area was characterized as flexural–tensile failure. This work can provide a reference for post-earthquake disaster investigation, as well as disaster prevention and mitigation, in seismically active regions. Full article
(This article belongs to the Special Issue Sustainability in Natural Hazards Mitigation and Landslide Research)
Show Figures

Figure 1

Figure 1
<p>Geological overview map of the study area. The basic geological maps are sourced from the 1:200,000 geological maps [<a href="#B4-sustainability-16-07729" class="html-bibr">4</a>,<a href="#B5-sustainability-16-07729" class="html-bibr">5</a>]; the Songpinggou Fault is based on Zhao et al. [<a href="#B6-sustainability-16-07729" class="html-bibr">6</a>].</p>
Full article ">Figure 2
<p>Geological structures and historical earthquakes near study area. (Adapted from Ren et al. [<a href="#B25-sustainability-16-07729" class="html-bibr">25</a>] and Pei et al. [<a href="#B26-sustainability-16-07729" class="html-bibr">26</a>]).</p>
Full article ">Figure 3
<p>Schematic diagram of “reverse step” at the rear edge of landslide.</p>
Full article ">Figure 4
<p>“Reverse steps” at the rear edge of the SPVE landslide (A and B are the failure areas). (<b>a</b>–<b>c</b>) Aerial 3D images (0.2 m resolution) captured by an unmanned aerial vehicle (UAV). (<b>d</b>,<b>e</b>) Digital elevation model (DEM) images (Gauss–Krüger projection, 0.2 m resolution) captured by an UAV. These images are provided by Sichuan Metallurgical Geological Survey and Design Group Co., Ltd., Chendu, China.</p>
Full article ">Figure 5
<p>Small steps at the rear edge of the SPVE landslide. (<b>a</b>) Digital Orthophoto Map (DOM) (Gauss–Krüger projection, 0.2 m resolution). (<b>b</b>) Sketch drawing. (<b>c</b>) Strike rosette plot of steps. Aerial orthophoto image was provided by Sichuan Metallurgical Geological Survey and Design Group Co., Ltd., Chendu, China.</p>
Full article ">Figure 6
<p>Geological cross-section along line I-I′ of the SPVE landslide (A and B are the failure areas). Aerial 3D image (0.2 m resolution) was provided by Sichuan Metallurgical Geological Survey and Design Group Co., Ltd., Chendu, China.</p>
Full article ">Figure 7
<p>Schematic diagram of steps. (<b>a</b>) Steps formed under static conditions (red arrow indicates the direction of movement). (<b>b</b>) “Reverse steps” in the source areas of the SPVE landslide.</p>
Full article ">Figure 8
<p>Sketch of the model slope and layout of the accelerometers (unit: mm).</p>
Full article ">Figure 9
<p>Main process of making the model. (<b>a</b>) Sketching the outline of the model. (<b>b</b>) Scraping the surface. (<b>c</b>) Covering with plastic film.</p>
Full article ">Figure 10
<p>Lateral view of model slopes.</p>
Full article ">Figure 11
<p>Acceleration time history and Fourier spectrum of the <span class="html-italic">DX</span> wave in both the <span class="html-italic">SN</span> and <span class="html-italic">UD</span> directions.</p>
Full article ">Figure 12
<p>Location map of test model and data acquisition equipment.</p>
Full article ">Figure 13
<p>Illustration and photographs of slope deformation under the excitation of a 0.2 g sine wave (A–D indicate the numbered black discolorations).</p>
Full article ">Figure 14
<p>Curve of amplification factor with elevation under the excitation of seismic waves with amplitudes of 0.1 g and 0.2 g.</p>
Full article ">Figure 15
<p>Transfer function curve of the model slope under white noise excitations.</p>
Full article ">Figure 16
<p>Illustration and photographs of slope deformations under the excitation of DX waves with amplitudes ranging from 0.4 g to 0.6 g.</p>
Full article ">Figure 17
<p>Illustration and photographs of the slope deformations under the excitation of sine waves with amplitudes ranging from 0.3 g to 0.5 g.</p>
Full article ">Figure 18
<p>Curve of the amplification factor with elevation under the excitation of seismic waves with different amplitudes.</p>
Full article ">Figure 19
<p>Illustration and photographs of the slope deformation under the excitation of a 0.6 g sine wave. (<b>a</b>) Schematic diagram of the model. (<b>b</b>) Exposed bedding planes. (<b>c</b>) The scarp at the rear of the failure area. (<b>d</b>) Partial surface spalling of the model. (<b>e</b>) Landslide deposit.</p>
Full article ">Figure 20
<p>Contour maps of AAFs under the loading of <span class="html-italic">DX</span> waves in different directions. (<b>a</b>) Horizontal (<span class="html-italic">X</span>) direction. (<b>b</b>) Vertical (<span class="html-italic">Z</span>) direction.</p>
Full article ">Figure 21
<p>Deformation and failure process of the MBRSs under seismic action. (<b>a</b>) Bending of rock layers towards the free face. (<b>b</b>) Fracture of rock layers. (<b>c</b>) Slope failure (arrow indicates the direction of movement).</p>
Full article ">
18 pages, 84736 KiB  
Article
Newly Discovered NE-Striking Dextral Strike-Slip Holocene Active Caimashui Fault in the Central Part of the Sichuan-Yunnan Block and Its Tectonic Significance
by Xin Tan, Kuan Liang, Baoqi Ma and Zhongtai He
Remote Sens. 2024, 16(17), 3203; https://doi.org/10.3390/rs16173203 - 29 Aug 2024
Viewed by 689
Abstract
The Sichuan-Yunnan block is a tectonically active region in China, with frequent large earthquakes occurring in and around it. Despite most earthquakes being concentrated along boundary faults, intraplate faults also have the potential to generate damaging earthquakes. Remote sensing makes it possible to [...] Read more.
The Sichuan-Yunnan block is a tectonically active region in China, with frequent large earthquakes occurring in and around it. Despite most earthquakes being concentrated along boundary faults, intraplate faults also have the potential to generate damaging earthquakes. Remote sensing makes it possible to identify these potential earthquake source faults. During an active fault investigation in the Liangshan area, a distinct lithological boundary named Caimashui fault was found. The geometric distribution and kinematic parameter of the fault is crucial for assessing seismic hazards and understanding the deformation pattern within the Sichuan-Yunnan block. The Caimashui fault is mapped with remote sensing interpretation, a field survey, and UAV measurement. Through trenching and Quaternary dating, the Late Quaternary active characteristics of the fault are studied. The fault is a Holocene active dextral strike-slip fault with a reverse component, exhibiting a dextral strike-slip rate of ~0.70 ± 0.11 mm/a. Paleoseismic investigation shows that the last surface rupture event of the Caimashui fault occurred later than 4150 ± 30a BP, with a magnitude of M ≥ 7.0. The fault may act as a secondary splitting fault, absorbing the deformation caused by various sinistral strike-slip rates of the boundary faults and the potential energy from the counterclockwise rotation of the Central Yunnan micro-block. Full article
Show Figures

Figure 1

Figure 1
<p>Tectonic setting and distribution map of the southeast margin of the Tibetan Plateau. (<b>a</b>) Tectonic location of the study area. Black rectangle shows the study area. ATF: Altyn Tagh fault, QHF: Qilian-Haiyuan fault, KF: Kunlunshan fault, XF: Xianshuihe fault, XJF: Xiaojiang fault, RRF: Red River fault, JLF: Jiali fault, CF: Karakorum fault, HFT: Himalayan Frontal Thrust. (<b>b</b>) Main active tectonics in the study area. The fault locations are modified from [<a href="#B41-remotesensing-16-03203" class="html-bibr">41</a>]. Colored circles represent historically and instrumentally documented earthquakes, which are modified from [<a href="#B34-remotesensing-16-03203" class="html-bibr">34</a>,<a href="#B36-remotesensing-16-03203" class="html-bibr">36</a>,<a href="#B42-remotesensing-16-03203" class="html-bibr">42</a>]. XF: Xianshuihe fault, ANHF: Aninghe fault, ZMHF: Zemuhe fault, DLSF: Daliangshan fault, LMSFZ: Longmenshan fault zone, LJ-XJHF: Lijiang-Xiaojinhe fault, XGDF: Xigeda fault, YMF: Yuanmou fault, CMSF: Caimashui fault, QJF: Qujiang fault, SJF: Shiping-Jianshui fault.</p>
Full article ">Figure 2
<p>Geological features and fault distributions near the Caimashui fault. Lithological data are obtained from 1:200,000 geologic maps (<a href="https://www.ngac.org.cn" target="_blank">https://www.ngac.org.cn</a>).</p>
Full article ">Figure 3
<p>Tectonic landforms caused by the Caimashui fault near Huoshi Town (see location in <a href="#remotesensing-16-03203-f002" class="html-fig">Figure 2</a>). (<b>a</b>) Satellite image (from Google Earth) of the fault trace. (<b>b</b>) Tectonic landforms around the Sanjiaozhuang site. (<b>c</b>) Tectonic landforms around the Xiaochacun site. (<b>d</b>) Tectonic landforms around the Tangjiawan site. (<b>e</b>) Tectonic landforms around the Huoshi Town site. For locations, see <a href="#remotesensing-16-03203-f003" class="html-fig">Figure 3</a>a. Red arrows and red lines indicate the location of the fault.</p>
Full article ">Figure 4
<p>Tectonic landforms around the Xiaochacun trench; see location in <a href="#remotesensing-16-03203-f003" class="html-fig">Figure 3</a>c. (<b>a</b>) Shaded relief map (from UAV-derived DEM) and interpreted map, showing the fault scarp, fault trough, and offset terrace. (<b>b</b>) Aerial image showing the tectonic landforms along the fault. (<b>c</b>) Field photo of the offset terrace. (<b>d</b>) Field photo of the ground fissures. (<b>e</b>) Aerial photo of the Xiaochacun trench, with broken white lines indicating fault scarps. (<b>f</b>) Field photo of the fault scarp.</p>
Full article ">Figure 5
<p>(<b>a</b>) Photo mosaic and (<b>b</b>) interpreted map of the west wall of the Xiaochacun trench. (<b>c</b>) Photo mosaic and (<b>d</b>) interpreted map of the east wall of the Xiaochacun trench. Black lines indicate the stratigraphic contacts between units. Red lines indicate the fault planes. Black dots show the locations of the radiocarbon samples, labeled with their corresponding calibrated ages.</p>
Full article ">Figure 6
<p>Sketch maps showing the formation and evolution of sag ponds in Xiaochacun. (<b>a</b>,<b>b</b>) Ridges and gullies before the formation of the sag ponds; (<b>c</b>,<b>d</b>) show the fault displacing the ridges, leading to the formation of sag ponds 1 and 2; (<b>e</b>,<b>f</b>) show the gullies cutting through the sag ponds, resulting in the abandonment of the sag ponds and their subsequent displacement by ongoing fault activity.</p>
Full article ">Figure 7
<p>Geometrical and tectonic model of the Sichuan-Yunnan tectonic zone. (<b>a</b>) Fault slip rates and the distribution of strong earthquakes on the Sichuan-Yunnan block. (<b>b</b>) A cartoon model showing that the slip rate difference between the left-slip faults and right-slip faults inside the block leads to counterclockwise rotation (modified from [<a href="#B8-remotesensing-16-03203" class="html-bibr">8</a>]). WYMB: West Yunnan microblock; CYMB: Central Yunnan microblock; NCF: Nanhua-Chuxiong fault; JSJF: Jinshajiang fault; SJF: Shiping-Jianshui fault; QJF: Qujiang fault.</p>
Full article ">
23 pages, 23335 KiB  
Article
Refined 3D Numerical Simulation of In Situ Stress in Shale Reservoirs: Northern Mahu Sag, Junggar Basin, Northwest China
by Peng Chen, Huaning Qiu, Xinyu Chen and Chuanbo Shen
Appl. Sci. 2024, 14(17), 7644; https://doi.org/10.3390/app14177644 - 29 Aug 2024
Viewed by 726
Abstract
The shale oil reservoirs of the Lower Permian Fengcheng Formation in the northern Mahu Sag are promising targets. However, complex geology and strong heterogeneity in the area pose great difficulties in the numerical simulation of in situ stress fields, which have for a [...] Read more.
The shale oil reservoirs of the Lower Permian Fengcheng Formation in the northern Mahu Sag are promising targets. However, complex geology and strong heterogeneity in the area pose great difficulties in the numerical simulation of in situ stress fields, which have for a long time been poorly understood. This study provides a systematic and accurate 3D in situ stress numerical simulation workflow based on comprehensive data. In this research, optimized ant tracking was applied to construct refined geological models. Acoustic impedance is taken as what we refer to as “hard” data to reflect variations in geomechanical parameters. Logging and mechanical tests were taken as “soft” data to restrict the numerical range of the geomechanical parameters. With the integration of “hard” data and “soft” data, accurate 3D geomechanical models can be attained. The finite element method was ultimately utilized to simulate the 3D in situ stress field of the Fengcheng Formation. Numerical simulation results reveal that the stress state of the Fengcheng Formation is quite complicated. The magnitude of the horizontal principal stress, horizontal stress difference and horizontal stress difference coefficient are correlated with burial depth, faults, and geomechanical parameters to some degree. The parameter Aφ was introduced in this research to better analyze the stress regime, the result of which demonstrates that the main stress regime in the study region is the reverse faulting stress regime. By evaluating the fault stability, it was found that there is basically no possibility of slippage regarding the faults in northern Mahu Sag. The results of this research provide evidence for well deployment optimization, borehole stability, and so on, all of which are of great significance in hydrocarbon exploration and exploitation. Full article
(This article belongs to the Section Earth Sciences)
Show Figures

Figure 1

Figure 1
<p>Continental shale oil distribution in China. Reprinted with permission from ref. [<a href="#B7-applsci-14-07644" class="html-bibr">7</a>]. Copyright 2019 Elsevier.</p>
Full article ">Figure 2
<p>(<b>A</b>) Geographical location of the northern Mahu Sag. (<b>B</b>) Planar distribution of faults in the northern Mahu Sag (aa’, bb’, and cc’ are composite lines).</p>
Full article ">Figure 3
<p>Stratigraphic, lithological systems, and depositional environments in the study area [<a href="#B20-applsci-14-07644" class="html-bibr">20</a>].</p>
Full article ">Figure 4
<p>Cross-section of the northern Mahu Sag by applying the method of ant tracking. The location a-a’ is shown in <a href="#applsci-14-07644-f002" class="html-fig">Figure 2</a> (P<sub>1</sub>f, P<sub>2</sub>f, P<sub>2</sub>x, and P<sub>2</sub>w are members of the Permian period.).</p>
Full article ">Figure 5
<p>(<b>A</b>) Different stages of a cumulative acoustic emission curve. Region A−B represents the closing of cracks, and B−C the linear elastic deformation. C−D is the stable, and D−E the unstable fracture propagation. Reprinted with permission from ref. [<a href="#B30-applsci-14-07644" class="html-bibr">30</a>]. Copyright 2012 Elsevier. (<b>B</b>) Sketch diagram of core sampling. Reprinted with permission from ref. [<a href="#B33-applsci-14-07644" class="html-bibr">33</a>]. Copyright 2009 Springer Nature.</p>
Full article ">Figure 6
<p>Comprehensive interpretation of the in situ stress and mechanical parameters based on FMI and conventional logging of Ma54.</p>
Full article ">Figure 7
<p>Methodology and workflow of 3D in situ stress modeling.</p>
Full article ">Figure 8
<p>(<b>A</b>) Relationship between the dynamic Young’s modulus (E<sub>d</sub>) and static Young’s modulus E. (<b>B</b>) Relationship between the dynamic Poisson’s ratio (μ<sub>d</sub>) and static Poisson’s ratio (μ<sub>s</sub>).</p>
Full article ">Figure 9
<p>Variations in the horizontal principal stress with depth. (<b>A</b>) The relationship between burial depth and σ<sub>hmax</sub>. (<b>B</b>) The relationship between burial depth and σ<sub>hmin</sub>.</p>
Full article ">Figure 10
<p>(<b>A</b>) 3D geological model of the research area. (<b>B</b>) 3D fault model interpreted with the technology of ant tracking.</p>
Full article ">Figure 10 Cont.
<p>(<b>A</b>) 3D geological model of the research area. (<b>B</b>) 3D fault model interpreted with the technology of ant tracking.</p>
Full article ">Figure 11
<p>The distribution of acoustic impendence. (<b>A</b>) 3D acoustic impendence model. (<b>B</b>) Planar distribution of acoustic impendence of the P<sub>1</sub>f<sup>1</sup>.</p>
Full article ">Figure 12
<p>The distribution of Young’s modulus. (<b>A</b>) 3D Young’s modulus model. (<b>B</b>) Planar distribution of Young’s modulus of the P<sub>1</sub>f<sup>1</sup>.</p>
Full article ">Figure 13
<p>The distribution of Poisson’s ratio. (<b>A</b>) 3D Poisson’s ratio model. (<b>B</b>) Planar distribution of Poisson’s ratio of the P<sub>1</sub>f<sup>1</sup>.</p>
Full article ">Figure 14
<p>(<b>A</b>) DIFs in the resistivity image log. (<b>B</b>) Rose diagram of the strike of drilling induced fractures. (<b>C</b>) Rose diagram of the strike of wellbore collapse.</p>
Full article ">Figure 15
<p>Planar distribution of the horizontal principal stress of P<sub>1</sub>f<sup>1</sup>. (<b>A</b>) Minimum horizontal principal stress; (<b>B</b>) maximum horizontal principal stress.</p>
Full article ">Figure 16
<p>Frequency distribution of in situ stress. (<b>A</b>) Minimum horizontal principal stress; (<b>B</b>) maximum horizontal principal stress.</p>
Full article ">Figure 17
<p>The orientation of the horizontal principal stress of P<sub>1</sub>f<sup>1</sup>. (<b>A</b>) Minimum horizontal principal stress; (<b>B</b>) maximum horizontal principal stress.</p>
Full article ">Figure 18
<p>In situ stress, mechanical modulus and related parameters on a 2D cross-section. (<b>A</b>) Minimum horizontal principal stress; (<b>B</b>) maximum horizontal principal stress; (<b>C</b>) Young’s modulus; (<b>D</b>) Poisson’s ratio; (<b>E</b>) Stress difference; (<b>F</b>). Stress difference coefficient. The location is shown in <a href="#applsci-14-07644-f002" class="html-fig">Figure 2</a>(b–b’).</p>
Full article ">Figure 18 Cont.
<p>In situ stress, mechanical modulus and related parameters on a 2D cross-section. (<b>A</b>) Minimum horizontal principal stress; (<b>B</b>) maximum horizontal principal stress; (<b>C</b>) Young’s modulus; (<b>D</b>) Poisson’s ratio; (<b>E</b>) Stress difference; (<b>F</b>). Stress difference coefficient. The location is shown in <a href="#applsci-14-07644-f002" class="html-fig">Figure 2</a>(b–b’).</p>
Full article ">Figure 19
<p>Planar distribution of dip azimuth in the research area.</p>
Full article ">Figure 20
<p>Planar distribution of horizontal stress difference (Δσ) of different layers (<b>A</b>) Δσ of P<sub>1</sub>f<sup>2</sup>; (<b>B</b>) Δσ of P<sub>1</sub>f<sup>3</sup>.</p>
Full article ">Figure 21
<p>Planar distribution of Poisson’s ratio of different layers. (<b>A</b>) Poisson’s ratio of P<sub>1</sub>f<sup>2</sup>; (<b>B</b>) Poisson’s ratio of P<sub>1</sub>f<sup>3</sup>.</p>
Full article ">Figure 21 Cont.
<p>Planar distribution of Poisson’s ratio of different layers. (<b>A</b>) Poisson’s ratio of P<sub>1</sub>f<sup>2</sup>; (<b>B</b>) Poisson’s ratio of P<sub>1</sub>f<sup>3</sup>.</p>
Full article ">Figure 22
<p>Planar distribution of Young’s modulus of different layers. (<b>A</b>) Young’s modulus of P<sub>1</sub>f<sup>2</sup>; (<b>B</b>) Young’s modulus of P<sub>1</sub>f<sup>3</sup>.</p>
Full article ">Figure 23
<p>Planar distribution of stress difference coefficient (K<sub>h</sub>) of different layers. (<b>A</b>) Young’s modulus of P<sub>1</sub>f<sup>2</sup>; (<b>B</b>) Young’s modulus of P<sub>1</sub>f<sup>3</sup>.</p>
Full article ">Figure 24
<p>Planar distribution of Simpson ratio (A<sub>φ</sub>).</p>
Full article ">Figure 25
<p>(<b>A</b>) 2D cross-section of A<sub>φ</sub>; (<b>B</b>) 2D cross-section of K<sub>h</sub>. The location is shown in <a href="#applsci-14-07644-f002" class="html-fig">Figure 2</a>(c–c’).</p>
Full article ">Figure 26
<p>The stability analysis on F1 fault and F2 fault (the location is shown in <a href="#applsci-14-07644-f010" class="html-fig">Figure 10</a>). (<b>A</b>) The stability analysis on F1 fault; (<b>B</b>) The stability analysis on F2 fault.</p>
Full article ">
32 pages, 26241 KiB  
Review
A Study on the Impact of Different PV Model Parameters and Various DC Faults on the Characteristics and Performance of the Photovoltaic Arrays
by Khaled Ibrahim Baradieh, Muhammad Ammirrul Atiqi Mohd Zainuri, Nor Azwan Mohamed Kamari, Huda Abdullah, Yushaizad Yusof, Mohd Asyraf Zulkifley and Mohsin Ali Koondhar
Inventions 2024, 9(5), 93; https://doi.org/10.3390/inventions9050093 - 27 Aug 2024
Cited by 2 | Viewed by 1484
Abstract
PV systems play a vital role in the global renewable energy sector, and they require accurate modeling and reliable performance to maximize the output power. This research presents a thorough analysis and discussions on the effects of different PV models’ parameters and certain [...] Read more.
PV systems play a vital role in the global renewable energy sector, and they require accurate modeling and reliable performance to maximize the output power. This research presents a thorough analysis and discussions on the effects of different PV models’ parameters and certain specific faults on the performance and behavior of the photovoltaic systems under different temperature and irradiation conditions. It provides a detailed analysis of how several parameters affect the performance of the PV arrays, for instance, the series resistance, shunt resistance, photocurrent, reverse saturation current, and the diode ideality factor. These parameters were extracted mathematically and verified with the help of wide-ranging simulations and practical experiments. Additionally, the investigation of the effect of DC faults, including line-to-line, line-to-ground, partial shading, and complete shading faults on PV arrays, provides important fundamentals for fault detection and classification, thus improving the efficiency and protection of PV systems. It can, therefore, be stated that the outcomes of this research will assist in the enhancement of PV systems in terms of design, operation, and maintainability of photovoltaic plants, as well as contribute positively to the advancement of sustainable solar energy technology. Full article
Show Figures

Figure 1

Figure 1
<p>Electrical representation of the PV cell: (<b>a</b>) single-diode model, (<b>b</b>) double-diode model.</p>
Full article ">Figure 2
<p>The I-V curve of the module (KC130GT) using the extracted parameters under different irradiance levels.</p>
Full article ">Figure 3
<p>The P-V curve of the module (KC130GT) using the extracted parameters under different irradiance levels.</p>
Full article ">Figure 4
<p>The reference I-V curve from the (KC130GT) datasheet.</p>
Full article ">Figure 5
<p>Experimental setup.</p>
Full article ">Figure 6
<p>A 100 kΩ resistor connected in series with the voltage sensor module.</p>
Full article ">Figure 7
<p>The block diagram of the hardware prototype.</p>
Full article ">Figure 8
<p>A comparison between the simulation and experimental I-V curves of KC130GT at STC.</p>
Full article ">Figure 9
<p>A comparison between the simulation and experimental P-V curves of KC130GT at STC.</p>
Full article ">Figure 10
<p>Fill factor of a PV device representation (KC130GT).</p>
Full article ">Figure 11
<p>The effect of the irradiance level on the IV and PV curves.</p>
Full article ">Figure 12
<p>The effect of the irradiance level on the open circuit voltage (logarithmic scale).</p>
Full article ">Figure 13
<p>The effect of the irradiance level on the efficiency and FF of the PV module.</p>
Full article ">Figure 14
<p>The effect of the irradiance level on the ideality factor.</p>
Full article ">Figure 15
<p>The effect of the irradiance level on the series and shunt resistances.</p>
Full article ">Figure 16
<p>The effect of the temperature variation in the IV and PV curves of the PV module KC130GT.</p>
Full article ">Figure 17
<p>The effect of the temperature variation on the open circuit voltage and short circuit current of the PV module KC130GT.</p>
Full article ">Figure 18
<p>The effect of the temperature variation in the fill factor and efficiency of the PV module KC130GT.</p>
Full article ">Figure 19
<p>The effect of the variation in series resistance on the current and power curves of the PV module (KC130GT).</p>
Full article ">Figure 20
<p>The effect of the variation in series resistance on the PV module FF and efficiency.</p>
Full article ">Figure 21
<p>The effect of the variation in shunt resistance on the I-V and P-V curves of the photovoltaic module.</p>
Full article ">Figure 22
<p>The effect of the variation in reverse saturation current on the I-V and P-V curves of the photovoltaic module.</p>
Full article ">Figure 23
<p>The effect of the temperature variation on the diode’s reverse saturation current.</p>
Full article ">Figure 24
<p>The impact of the change in the ideality factor on the I-V and P-V characteristics of the PV module (KC130GT).</p>
Full article ">Figure 25
<p>The relationship between the irradiance level and the ideality factor.</p>
Full article ">Figure 26
<p>Fault types in the PV system.</p>
Full article ">Figure 27
<p>The effect of the LL fault resistance on the I-V curve of the PV array with no blocking diodes at STC.</p>
Full article ">Figure 28
<p>The effect of the LL fault resistance on the I-V curve of the PV array with blocking diodes at STC.</p>
Full article ">Figure 29
<p>I-V curve of the PV array under lower and upper ground fault.</p>
Full article ">Figure 30
<p>The upper ground fault at different resistance levels in the presence of blocking diodes.</p>
Full article ">Figure 31
<p>The PV curve of the PV array under different shading scenarios.</p>
Full article ">
21 pages, 17400 KiB  
Article
Forward Simulation and Complex Signal Analysis of Concrete Crack Depth Detection Using Tracer Electromagnetic Method
by Yulei Wang, Shengxing Zhang, Yu Jia, Lei Tang, Jin Tao and Hui Tian
Buildings 2024, 14(9), 2644; https://doi.org/10.3390/buildings14092644 - 26 Aug 2024
Viewed by 657
Abstract
Cracks are the most typical faults of concrete structures, and their extension can lead to structural fracture. However, when cracks develop inside a structure, the most important depth information is invisible and difficult to measure. The tracer electromagnetic method is an effective technique [...] Read more.
Cracks are the most typical faults of concrete structures, and their extension can lead to structural fracture. However, when cracks develop inside a structure, the most important depth information is invisible and difficult to measure. The tracer electromagnetic method is an effective technique for detecting the depth of concrete cracks, but since concrete is a multiphase stochastic composite material, its complex internal structure often interferes with the radar detection results, making the conventional radar interpretation technique difficult. In this study, the detection results for concrete crack depth detection based on the tracer electromagnetic method were comprehensively analyzed by combining the complex signal analysis technique, using transient information such as amplitude, phase, and frequency in order to improve the precision and accuracy of radar signal interpretation. In this study, a numerical model was established to determine whether typical cracks such as vertical cracks and diagonal cracks contain indicators or not, and the ground-penetrating radar forward simulation software was used to perform forward simulation of the numerical model and analyze the forward results. The complex signal analysis technique was used to obtain the response characteristics of typical cracks when they did and did not contain the indicator, and the complex signal was finally analyzed by combining it with the actual crack depth detection data. The results show that the tracer electromagnetic method can significantly improve the crack bottom’s reflection ability for radar signals, and when the crack bottom contains an indicator, the amplitude of the reflected signal at the bottom of the crack is enhanced, the phase is reversed, and the frequency is reduced. The distribution of the crack morphology and the location of the crack bottom can be analyzed more conveniently by using the complex signal analysis technique. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
Show Figures

Figure 1

Figure 1
<p>Concrete cracks affect structural safety.</p>
Full article ">Figure 2
<p>The propagation path of electromagnetic wave.</p>
Full article ">Figure 3
<p>Schematic diagram of the working principle of tracer electromagnetic method.</p>
Full article ">Figure 4
<p>Vertical crack calculation model (<b>a</b>) Crack model without indicator (<b>b</b>) Crack model with indicator.</p>
Full article ">Figure 4 Cont.
<p>Vertical crack calculation model (<b>a</b>) Crack model without indicator (<b>b</b>) Crack model with indicator.</p>
Full article ">Figure 5
<p>Detection result of vertical crack model without indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 5 Cont.
<p>Detection result of vertical crack model without indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 6
<p>Detection result of vertical crack model with indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 6 Cont.
<p>Detection result of vertical crack model with indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 7
<p>Vertical crack calculation model (<b>a</b>) Crack model without indicator (<b>b</b>) Crack model with indicator.</p>
Full article ">Figure 8
<p>Detection results of oblique crack model without indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 8 Cont.
<p>Detection results of oblique crack model without indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 9
<p>Detection results of oblique crack model with indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 9 Cont.
<p>Detection results of oblique crack model with indicator (<b>a</b>) Forward modeling result (<b>b</b>) Instantaneous amplitude (<b>c</b>) Instantaneous phase (<b>d</b>) Instantaneous frequency.</p>
Full article ">Figure 10
<p>Indicator mother liquor and dispersions.</p>
Full article ">Figure 11
<p>Indicator.</p>
Full article ">Figure 12
<p>The indicator infusion device.</p>
Full article ">Figure 13
<p>The ground-penetrating radar.</p>
Full article ">Figure 14
<p>Schematic diagram of depth detection for vertical and oblique cracks (<b>a</b>) Vertical cracks to be tested and indicator infusion (<b>b</b>) Oblique cracks to be tested and indicator infusion.</p>
Full article ">Figure 15
<p>The result of the vertical crack depth detection.</p>
Full article ">Figure 16
<p>The result of the oblique crack depth detection.</p>
Full article ">
23 pages, 25451 KiB  
Article
Impacts and Countermeasures of Present-Day Stress State and Geological Conditions on Coal Reservoir Development in Shizhuang South Block, Qinshui Basin
by Xinyang Men, Shu Tao, Shida Chen, Heng Wu and Bin Zhang
Energies 2024, 17(17), 4221; https://doi.org/10.3390/en17174221 - 23 Aug 2024
Viewed by 789
Abstract
This study investigates the reservoir physical properties, present-day stress, hydraulic fracturing, and production capacity of No. 3 coal in the Shizhuang south block, Qinshui Basin. It analyzes the control of in situ stress on permeability and hydraulic fracturing, as well as the influence [...] Read more.
This study investigates the reservoir physical properties, present-day stress, hydraulic fracturing, and production capacity of No. 3 coal in the Shizhuang south block, Qinshui Basin. It analyzes the control of in situ stress on permeability and hydraulic fracturing, as well as the influence of geo-engineering parameters on coalbed methane (CBM) production capacity. Presently, the direction of maximum horizontal stress is northeast–southwest, with local variations. The stress magnitude increases with burial depth, while the stress gradient decreases. The stress field of strike-slip faults is dominant and vertically continuous. The stress field of normal faults is mostly found at depths greater than 800 m, whereas the stress field of reverse faults is typically found at depths shallower than 700 m. Permeability, ranging from 0.003 to 1.08 mD, is controlled by in situ stress and coal texture, both of which vary significantly with tectonics. Hydraulic fracturing design should consider variations in stress conditions, pre-existing fractures, depth, structural trends, and coal texture, rather than employing generic schemes. At greater depths, higher pumping rates and treatment pressures are required to reduce fracture complexity and enhance proppant filling efficiency. The Shizhuang south block is divided into five zones based on in situ stress characteristics. Zones III and IV exhibit favorable geological conditions, including high porosity, permeability, and gas content. These zones also benefit from shorter gas breakthrough times, relatively higher gas breakthrough pressures, lower daily water production, and a higher ratio of critical desorption pressure to initial reservoir pressure. Tailored fracturing fluid and proppant programs are proposed for different zones to optimize subsequent CBM development. Full article
Show Figures

Figure 1

Figure 1
<p>Comprehensive geologic map of Shizhuang south block in Qinshui Basin. (<b>a</b>) Qingshui Basin, China, and the location of the Shizhuang south block; (<b>b</b>) Tectonic outline map of Shizhuang south block; (<b>c</b>) Stratigraphic histogram of Shizhuang south block.</p>
Full article ">Figure 2
<p>Maximum horizontal stress trajectory in China, based on data from the World Stress Map (WSM) Database [<a href="#B45-energies-17-04221" class="html-bibr">45</a>]. The stress map was generated using data from WSM available at <a href="http://www.world-stress-map.org/casmo/" target="_blank">http://www.world-stress-map.org/casmo/</a> (accessed on 10 May 2024).</p>
Full article ">Figure 3
<p>Variation law of tectonic strain with depth in the Shizhuang south block.</p>
Full article ">Figure 4
<p>Model evaluating coal reservoir permeability. (<b>a</b>) Sheet model; (<b>b</b>) Matchstick model; (<b>c</b>) Cube model.</p>
Full article ">Figure 5
<p>Scatter plot of in situ stress vs. depth. (<b>a</b>) Stress magnitudes vs. depth; (<b>b</b>) Stress gradient magnitudes vs. depth; (<b>c</b>) Stress regimes vs. depth; (<b>d</b>) <span class="html-italic">σ<sub>H</sub></span>/<span class="html-italic">σ<sub>v</sub></span> vs. depth; (<b>e</b>) <span class="html-italic">σ<sub>h</sub></span>/<span class="html-italic">σ<sub>v</sub></span> vs. depth; (<b>f</b>) Lateral pressure coefficient vs. depth.</p>
Full article ">Figure 6
<p>(<b>a</b>) The horizontal stress differential vs. depth; (<b>b</b>) Plot of horizontal stress difference vs. lateral pressure coefficient for different stress regimes.</p>
Full article ">Figure 7
<p>Map of the distribution of fault stress types in Shizhuang south block.</p>
Full article ">Figure 8
<p>Pie chart of the percentage of fault stress types in the Shizhuang south block. (<b>a</b>) In situ stress zone I; (<b>b</b>) In situ stress zone II; (<b>c</b>) In situ stress zone III; (<b>d</b>) In situ stress zone IV; (<b>e</b>) In situ stress zone V.</p>
Full article ">Figure 9
<p>Spatial distribution and measurement points of permeability and porosity. (<b>a</b>) Spatial distribution of permeability (mD); (<b>b</b>) Spatial distribution of porosity (%); (<b>c</b>) Measurement points of permeability; (<b>d</b>) Measurement points of porosity.</p>
Full article ">Figure 10
<p>Map of coal texture distribution. (<b>a</b>) Undeformed coal; (<b>b</b>) Granulated coal; (<b>c</b>) Percentage of coal texture in the in-situ stress zones; (<b>d</b>) Coal texture vs. depth.</p>
Full article ">Figure 11
<p>Map of effective stresses vs. permeability.</p>
Full article ">Figure 12
<p>Three-dimensional visualization of the geological formations of coal seam No. 3 within the Shizhuang south block.</p>
Full article ">Figure 13
<p>The cross-sectional diagram and parameters (permeability, coal quality, burial depth, and stress magnitude) of test wells in Shizhuang south block.</p>
Full article ">Figure 14
<p>Vertical distribution of well profiles and in situ stresses in the slope zone connecting wells in the Shizhuang south block.</p>
Full article ">Figure 15
<p>Vertical distribution of microtectonic connecting well profiles and in situ stresses in the Shizhuang block.</p>
Full article ">Figure 16
<p>Well section and vertical distribution of in situ stress in the Shizhuang south block.</p>
Full article ">Figure 17
<p>Fracturing construction parameters vs. depth. (<b>a</b>) Fracturing fluid volume; (<b>b</b>) Sand volume; (<b>c</b>) Sand concentration; (<b>d</b>) Breakdown pressure; (<b>e</b>) Shut-in pressure.</p>
Full article ">Figure 18
<p>Statistical map of micro-seismic events for parameter wells. (<b>a</b>) TS-8; (<b>b</b>) TS-9; (<b>c</b>) TS-13; (<b>d</b>) TS-14; (<b>e</b>) TS-15.</p>
Full article ">Figure 19
<p>The cross-sectional diagram and micro-seismic parameters of test wells in Shizhuang south block.</p>
Full article ">Figure 20
<p>Average daily gas production versus depth for different types of wells in the Shizhuang south block.</p>
Full article ">Figure 21
<p>Statistical map of engineering parameters of production wells within the tectonic unit. (<b>a</b>) Average daily gas production; (<b>b</b>) Gas breakthrough time; (<b>c</b>) Average daily water production; (<b>d</b>) well bottom pressure at gas breakthrough; (<b>e</b>) The ratio of critical desorption pressure to initial reservoir pressure.</p>
Full article ">Figure 22
<p>Statistical map of geological parameters of production wells within the tectonic unit. (<b>a</b>) Permeability; (<b>b</b>) Gas content; (<b>c</b>) Porosity; (<b>d</b>) Reservoir pressure; (<b>e</b>) Coal texture.</p>
Full article ">
Back to TopTop