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16 pages, 3973 KiB  
Article
Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index
by Jun Dong, Qingqing Chen, Guangxiang Yuan and Kaiyan Xie
Appl. Sci. 2024, 14(3), 1230; https://doi.org/10.3390/app14031230 - 1 Feb 2024
Cited by 1 | Viewed by 1058
Abstract
In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially [...] Read more.
In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures. Full article
(This article belongs to the Section Earth Sciences)
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Figure 1
<p>Engineering geological map of the study area.</p>
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<p>Joint layout of flat cave CPD1.</p>
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<p>Joint rose diagram of CPD1 cave rock mass.</p>
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<p>Joint pole isodensity diagram of CPD1 cave rock mass.</p>
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<p>Grouping distribution map of CPD1 cave rock mass joint.</p>
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<p>Fitting curve of joint dip direction.</p>
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<p>Fitting curve of joint dip angle.</p>
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<p>Fitting curve of joint trace length.</p>
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<p>Schematic diagram of 3D joint network model.</p>
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<p>Measured window trace diagram.</p>
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<p>Network simulation window trace diagram.</p>
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<p>Intersection relationship diagram between survey lines and joints.</p>
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25 pages, 8210 KiB  
Essay
A Numerical Method for Evaluating the Collapse of High-Steep Scarp Slopes Based on the Bonded Block Model–Discrete Fracture Network Model
by Zening Sun, Shili Qiu, Siquan Yan and Zaiquan Wang
Sustainability 2023, 15(21), 15672; https://doi.org/10.3390/su152115672 - 6 Nov 2023
Cited by 2 | Viewed by 1276
Abstract
Geotechnical engineering works in deep-incised valleys or open-pit mining areas often encounter high-steep scarp slopes with a slope angle greater than 75°. This type of slope directly threatens the safety of construction personnel, so assessing their stability is essential to ensure construction safety. [...] Read more.
Geotechnical engineering works in deep-incised valleys or open-pit mining areas often encounter high-steep scarp slopes with a slope angle greater than 75°. This type of slope directly threatens the safety of construction personnel, so assessing their stability is essential to ensure construction safety. The natural geometry of high-steep scarp slopes possesses complexity in terms of geometric morphology, structural features of rock mass, and occurrence mechanisms of collapse. There is little research and less emphasis on the evaluation of the collapse risk of high-steep scarp slopes. In particular, the fracture of intact rock or rock bridges is generally ignored in the analysis of collapse processes. A bonded block model (BBM)–discrete fracture network (DFN) coupling characterization model for the high-steep scarp slope is proposed based on a high-steep scarp slope containing dominant joint sets on the left bank of the dam site of the Huangzangsi Water Conservancy Project (Qinghai Province, China). By using the model, the complex geometric forms of the surface of the high-steep scarp slope are quantified, and the fracture process of falling rock masses as well as the controlling effect of dominant joints on the collapse of the scarp slope are revealed. A strength reduction method based on the BBM–DFN model is constructed, and the safety factor of the collapse-prone scarp slope is evaluated. The research results show that (1) the BBM–DFN model can be used to describe the local collapse process; (2) the occurrence of dominant joints plays an important part in controlling the collapse process; (3) there are differences in the safety factor of the scarp slope with different coupling methods; the collapse and failure modes also differ. For safety considerations, the safety factor of the scarp slope on the left bank of the dam site area is determined to be 1.85. The research findings can be used to guide the safety assessment of high-steep scarp slopes and the formulation of both collapse risk prevention and control measures to ensure construction safety in high-steep scarp slope areas. Full article
(This article belongs to the Special Issue Deep Mining Engineering in Sustainability)
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<p>Schematic diagrams of the coupling process of the BBM-DFN model. (<b>a</b>) Establishment of the overall model; (<b>b</b>) Generating the BBM model consisting of tetrahedrons; (<b>c</b>) generating the random DFN model; (<b>d</b>) Generating the definitive DFN model; (<b>e</b>) Generating the BBM-DFN coupling model.</p>
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<p>Sampling map of the CV of the displacement at the monitoring points.</p>
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<p>Geological condition and dominant joint distribution in the high-steep scarp on the left bank of the dam site of Huangzangsi Water Conservancy Project.</p>
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<p>High-steep scarp slope model on the left bank of the dam site of Huangzangsi Water Conservancy Project and boundary conditions. (<b>a</b>) Local model for the high-steep scarp slope. (<b>b</b>) Boundary conditions of the Planar model of the high-steep scarp slope.</p>
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<p>High-steep scarp slope model on the left bank of the dam site of Huangzangsi Water Conservancy Project and boundary conditions. (<b>a</b>) Local model for the high-steep scarp slope. (<b>b</b>) Boundary conditions of the model of the high-steep scarp slope.</p>
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<p>Distribution of monitoring points on the scarp model on the left bank of the dam site of the Huangzangsi Water Conservancy Project.</p>
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<p>Variation trends in crack numbers under three different reduction methods. (<b>a</b>) Reduction curves of the scarp slope in the CF-RM process. (<b>b</b>) Reduction curves of the scarp slope in the CFT-RM process. (<b>c</b>) Reduction curves of the scarp slope in the CFD-RM process.</p>
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<p>Distribution of cracks in the high-steep scarp slope in the CFD-RM reduction method. (<b>a</b>) The initiation of crack development (5000 steps). (<b>b</b>) The appearance of transverse cracks (8000 steps). (<b>c</b>) Transverse crack propagation (10,000 steps). (<b>d</b>) Finally, longitudinal crack propagation (13,000 steps).</p>
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<p>Block velocity(m/s) of the high-steep scarp slope in the CFD-RM reduction method. (<b>a</b>) The initiation of crack development (5000 steps). (<b>b</b>) The appearance of transverse cracks (8000 steps). (<b>c</b>) Transverse crack propagation (10,000 steps). (<b>d</b>) Finally, longitudinal crack propagation (13,000 steps).</p>
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<p>Distribution of cracks in the high-steep scarp slope in the CF-RM reduction method. (<b>a</b>) The initiation of crack development (8000 steps). (<b>b</b>) The appearance of transverse cracks (12,000 steps). (<b>c</b>) Transverse crack propagation (14,000 steps). (<b>d</b>) Finally, longitudinal crack propagation (20,000 steps).</p>
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<p>Block velocity(m/s) of the high-steep scarp slope in the CF-RM reduction method. (<b>a</b>) The initiation of crack development (8000 steps). (<b>b</b>) The appearance of transverse cracks (12,000 steps). (<b>c</b>) Transverse crack propagation (14,000 steps). (<b>d</b>) Finally, longitudinal crack propagation (20,000 steps).</p>
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<p>Distribution of cracks in the high-steep scarp slope in the CFT-RM reduction method. (<b>a</b>) The initiation of crack development (8000 steps). (<b>b</b>) The appearance of transverse cracks (13,000 steps). (<b>c</b>) Transverse crack propagation (14,000 steps). (<b>d</b>) Finally, longitudinal crack propagation (23,000 steps).</p>
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<p>Block velocity(m/s) of the high-steep scarp slope in the CFT-RM reduction method. (<b>a</b>) The initiation of crack development (8000 steps). (<b>b</b>) The appearance of transverse cracks (13,000 steps). (<b>c</b>) Transverse crack propagation (14,000 steps). (<b>d</b>) Finally, longitudinal crack propagation (23,000 steps).</p>
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<p>Coupling effects of the internal friction angle and cohesion of mesoscopic joints between blocks on the stability of the scarp slope. (<b>a</b>) Reduction curves of the scarp slope in the CF-RM process. (<b>b</b>) Changes of the CV in the CF-RM process. (<b>c</b>) Changes in the number of cracks in the scarp slope in the CF-RM process. (<b>d</b>) Changes in the number of cracks during failure in the CF-RM process. (<b>e</b>) Distribution of cracks in the scarp slope before failure in the CF-RM process. (<b>f</b>) Distribution of cracks in the scarp slope at failure in the CF-RM process.</p>
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<p>Coupling effects of the internal friction angle and cohesion of mesoscopic joints between blocks on the stability of the scarp slope. (<b>a</b>) Reduction curves of the scarp slope in the CF-RM process. (<b>b</b>) Changes of the CV in the CF-RM process. (<b>c</b>) Changes in the number of cracks in the scarp slope in the CF-RM process. (<b>d</b>) Changes in the number of cracks during failure in the CF-RM process. (<b>e</b>) Distribution of cracks in the scarp slope before failure in the CF-RM process. (<b>f</b>) Distribution of cracks in the scarp slope at failure in the CF-RM process.</p>
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<p>Coupling effects of the internal friction angle, cohesion, and tensile strength of mesoscopic joints between blocks on the stability of the scarp slope. (<b>a</b>) Reduction curves of the scarp slope in the CFT-RM process. (<b>b</b>) Changes of the CV in the CFT-RM process. (<b>c</b>) Changes in the number of cracks in the scarp slope in the CFT-RM process. (<b>d</b>) Changes in the number of cracks during failure in the CFT-RM process. (<b>e</b>) Distribution of cracks in the scarp slope before failure in the CFT-RM process. (<b>f</b>) Distribution of cracks in the scarp slope at failure in the CFT-RM process.</p>
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<p>Coupling effects of the internal friction angle, cohesion, and deformation parameters of mesoscopic joints between blocks on the stability of the scarp slope. (<b>a</b>) Reduction curves of the scarp slope in the CFD-RM process. (<b>b</b>) Changes of the CV in the CFD-RM process. (<b>c</b>) Changes in the number of cracks in the scarp slope in the CFD-RM process. (<b>d</b>) Changes in the number of cracks during failure in the CFD-RM process. (<b>e</b>) Distribution of cracks in the scarp slope before failure in the CFD-RM process. (<b>f</b>) Distribution of cracks in the scarp slope at failure in the CFD-RM process.</p>
Full article ">Figure 16 Cont.
<p>Coupling effects of the internal friction angle, cohesion, and deformation parameters of mesoscopic joints between blocks on the stability of the scarp slope. (<b>a</b>) Reduction curves of the scarp slope in the CFD-RM process. (<b>b</b>) Changes of the CV in the CFD-RM process. (<b>c</b>) Changes in the number of cracks in the scarp slope in the CFD-RM process. (<b>d</b>) Changes in the number of cracks during failure in the CFD-RM process. (<b>e</b>) Distribution of cracks in the scarp slope before failure in the CFD-RM process. (<b>f</b>) Distribution of cracks in the scarp slope at failure in the CFD-RM process.</p>
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<p>High-steep scarp slope model on the left bank of the dam site of the Huangzangsi Water Conservancy Project and boundary conditions. (<b>a</b>) The complete model. (<b>b</b>) The model in the article.</p>
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<p>Comparison of computational results between the new model and the model presented in the article. (<b>a</b>) The complete model diagram when it is disrupted. (<b>b</b>) Z-displacement of the model when it is disrupted. (<b>c</b>) Distribution of cracks in the scarp slope at failure in the new model. (<b>d</b>) Distribution of cracks in the scarp slope at failure in the article. (<b>e</b>) Reduction curves of the scarp slope in the new model. (<b>f</b>) Reduction curves of the scarp slope in the article.</p>
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<p>Comparison of computational results between the new model and the model presented in the article. (<b>a</b>) The complete model diagram when it is disrupted. (<b>b</b>) Z-displacement of the model when it is disrupted. (<b>c</b>) Distribution of cracks in the scarp slope at failure in the new model. (<b>d</b>) Distribution of cracks in the scarp slope at failure in the article. (<b>e</b>) Reduction curves of the scarp slope in the new model. (<b>f</b>) Reduction curves of the scarp slope in the article.</p>
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20 pages, 12919 KiB  
Article
A Slope Structural Plane Extraction Method Based on Geo-AINet Ensemble Learning with UAV Images
by Rongchun Zhang, Shang Shi, Xuefeng Yi, Lanfa Liu, Chenyang Zhang, Meiru Jing and Junhui Li
Remote Sens. 2023, 15(5), 1441; https://doi.org/10.3390/rs15051441 - 4 Mar 2023
Cited by 1 | Viewed by 1725
Abstract
In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. [...] Read more.
In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the slope are first used to generate a dense point cloud through a pipeline of SfM and PMVS; then, the multiple geological semantics, including color and texture from the image and local geological occurrence and surface roughness from the dense point cloud, are integrated with Geo-AINet for ensemble learning to obtain a set of semantic blocks; finally, the accurate extraction of structural planes is achieved through a multi-semantic hierarchical clustering strategy. Experimental results show that the structural planes extracted by the proposed method perform better integrity and edge adherence than that extracted by the AINet algorithm. In comparison with the results from the laser point cloud, the geological occurrence differences are less than three degrees, which proves the reliability of the results. This study widens the scope for surveying and mapping using remote sensing in engineering geological applications. Full article
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<p>The flowchart of the proposed Geo-AINet rock mass structural plane extraction method.</p>
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<p>The schematic diagram of the geometric relationship between the 3D space coordinate system of the dense point cloud and the 2D projected plane coordinate system.</p>
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<p>The basic principal framework of the AINet superpixel segmentation algorithm [<a href="#B35-remotesensing-15-01441" class="html-bibr">35</a>].</p>
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<p>The network architecture of Geo-AINet ensemble learning.</p>
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<p>The detailed calculation process of the soft association map <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">Q</mi> <mrow> <mi>Fusion</mi> </mrow> </msub> </mrow> </semantics></math> on the pixel <math display="inline"><semantics> <mrow> <mi>p</mi> <mfenced> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The schematic diagram of the semantic blocks’ clustering: (<b>a</b>) the segmented semantic blocks; (<b>b</b>) RAG; (<b>c</b>) NNG; (<b>d</b>) the merging results.</p>
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<p>The experimental areas of the quarry slope in Australia.</p>
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<p>The visualization of the sparse point cloud and cameras.</p>
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<p>An example of the original image and the corresponding depth map: (<b>a</b>) the RGB image; (<b>b</b>) the depth image.</p>
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<p>The visualization of the dense point cloud.</p>
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<p>Multi-feature semantic association projection images: (<b>a</b>) the RGB projection image; (<b>b</b>) the dip projection image; (<b>c</b>) the dip direction projection image.</p>
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<p>Visualizations of the semantic block segmentation results with the Geo-AINet method proposed in this study: (<b>a</b>) the segmented semantic block overlays on the RGB projection image, and five regions marked by red dashed boxes (numbered I-V) perform relative evident differences of geological features; (<b>b</b>) the segmented semantic block overlays on the dip projection image; (<b>c</b>) the segmented semantic block overlays on the dip direction projection image.</p>
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<p>A visualization of the AINet-based segmentation results.</p>
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<p>The local detail comparisons of the segmentation results from the two methods: the first row shows the segmentation results from the AINet-based method overlaying the RGB projection image. The last three rows show the segmentation results from the Geo-AINet-based method overlaying on RGB, dip, and dip direction projection images, respectively. The black curves represent the ground truth of the structural plane boundaries. The red solid curves marked in the first row, and the red dotted line marked in the second row, represent the segmented labels obtained by the two methods, respectively.</p>
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<p>The results from semantic block merging under different perspectives: (<b>a</b>) the left view; (<b>b</b>) the right view.</p>
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<p>Visualization of the structural plane extraction results: (<b>a</b>) the whole results; (<b>b</b>) and (<b>c</b>): distributions of some structural planes in images.</p>
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19 pages, 7328 KiB  
Article
Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
by Dragan Rakić, Milan Stojković, Damjan Ivetić, Miroslav Živković and Nikola Milivojević
Appl. Sci. 2022, 12(2), 558; https://doi.org/10.3390/app12020558 - 6 Jan 2022
Cited by 6 | Viewed by 2756
Abstract
The paper presents a functionality investigation of the key dam elements based on finite element analysis. A detailed analysis of filtration processes, dam strength, and the surrounding rock mass was conducted. Dam elements whose potential damage could jeopardize the normal functioning of the [...] Read more.
The paper presents a functionality investigation of the key dam elements based on finite element analysis. A detailed analysis of filtration processes, dam strength, and the surrounding rock mass was conducted. Dam elements whose potential damage could jeopardize the normal functioning of the embankment dam have been identified. A particular emphasis was placed on the analysis of dam elements that have been identified as weak points. A numerical analysis of the impact of individual grout curtain zone failure on leakage under the dam body, a strength analysis of the overflow section, as well as the analysis of the slope stability that can compromise the functioning of the spillway have been performed. To analyze the partial stability of individual structural elements, a new measure of local stability was introduced as the remaining load-bearing capacity. As a case study, the Zavoj dam, which is a part of the Pirot reservoir system in the Republic of Serbia, was used. Investigation revealed that local damage to the grout curtain will not significantly increase leakage under the dam body, the overflow section is one of the most robust elements of the dam, but the slope above the spillway can compromise the functioning of the overflow and thus the safety of the entire dam. Based on the analysis of the results of the remaining load-bearing capacity, the dependence of the spillway capacity on earthquake intensity has been defined. The established relationship represents a surrogate model for further assessment of dynamic resilience of the complex multipurpose reservoir system, within the scope of the advanced reservoir system management. Full article
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<p>Remaining load-bearing capacity definition.</p>
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<p>Zavoj dam cross-section with quasi-homogeneous zones.</p>
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<p>Zavoj dam—photo of the construction site.</p>
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<p>Finite element model of the Zavoj dam.</p>
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<p>Grout curtain regions whose damage was analyzed—downstream view.</p>
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<p>Discretized spillway of the Zavoj dam.</p>
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<p>Segments of the slope used in analysis of spillway capacity reduction.</p>
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<p>Total potential <math display="inline"><semantics> <mi>φ</mi> </semantics></math> (m asl) for different reservoir water levels: (<b>a</b>) rwl = 568 m asl, (<b>b</b>) rwl = 580 m asl, (<b>c</b>) rwl = 590 m asl, (<b>d</b>) rwl = 600 m asl, (<b>e</b>) rwl = 610 m asl, (<b>f</b>) rwl = 615.9 m asl.</p>
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<p>Seismic acceleration a<sub>x</sub> = 0.19 g: (<b>a</b>) total translation <math display="inline"><semantics> <mi>t</mi> </semantics></math> (m) and (<b>b</b>) equivalent plastic strain <math display="inline"><semantics> <mrow> <msup> <mi>e</mi> <mi>P</mi> </msup> </mrow> </semantics></math> (−).</p>
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<p>Seismic acceleration a<sub>y</sub> = 0.19 g: (<b>a</b>) total translation <math display="inline"><semantics> <mi>t</mi> </semantics></math> (m) and (<b>b</b>) equivalent plastic strain <math display="inline"><semantics> <mrow> <msup> <mi>e</mi> <mi>P</mi> </msup> </mrow> </semantics></math> (−).</p>
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<p>Total velocity through grout curtain <math display="inline"><semantics> <mi>v</mi> </semantics></math> (m/s) for rwl = 615.9 m asl and damaged: (<b>a</b>) region r1, (<b>b</b>) region r2, (<b>c</b>) region r3, (<b>d</b>) region r4, (<b>e</b>) region r5, (<b>f</b>) region r6.</p>
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<p>Flow through grout curtain for different damaged zones.</p>
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<p>Overflow section for a<sub>x</sub> = 0.19 g: (<b>a</b>) total translation <math display="inline"><semantics> <mi>t</mi> </semantics></math> (m), (<b>b</b>) remaining load-bearing capacity <span class="html-italic">RC</span> (%).</p>
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<p>Overflow section for a<sub>y</sub> = 0.19 g: (<b>a</b>) total translation <math display="inline"><semantics> <mi>t</mi> </semantics></math> (m), (<b>b</b>) remaining load-bearing capacity <span class="html-italic">RC</span> (%).</p>
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<p>Remaining load-bearing capacity <span class="html-italic">RC</span> (%) for a<sub>x</sub> = 0.19 g: (<b>a</b>) whole model and (<b>b</b>) slope above spillway.</p>
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<p>Remaining load-bearing capacity <span class="html-italic">RC</span> (%) for a<sub>y</sub> = 0.19 g: (<b>a</b>) whole model and (<b>b</b>) slope above spillway.</p>
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<p>Spillway capacity <math display="inline"><semantics> <mi>C</mi> </semantics></math> (%) vs. seismic acceleration in: (<b>a</b>) x direction, (<b>b</b>) y direction.</p>
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<p>Effective spillway capacity <math display="inline"><semantics> <mi>C</mi> </semantics></math> (%) vs. seismic acceleration.</p>
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21 pages, 15471 KiB  
Article
A Large Old Landslide in Sichuan Province, China: Surface Displacement Monitoring and Potential Instability Assessment
by Siyuan Ma, Chong Xu, Xiaoyi Shao, Xiwei Xu and Aichun Liu
Remote Sens. 2021, 13(13), 2552; https://doi.org/10.3390/rs13132552 - 29 Jun 2021
Cited by 17 | Viewed by 3759
Abstract
Using advanced Differential Interferometric Synthetic Aperture Radar (InSAR) with small baseline subsets (SBAS) and Permanent Scatter Interferometry (PSI) techniques and C-band Sentinel-1A data, this research monitored the surface displacement of a large old landslide at Xuecheng town, Lixian County, Sichuan Province, China. Based [...] Read more.
Using advanced Differential Interferometric Synthetic Aperture Radar (InSAR) with small baseline subsets (SBAS) and Permanent Scatter Interferometry (PSI) techniques and C-band Sentinel-1A data, this research monitored the surface displacement of a large old landslide at Xuecheng town, Lixian County, Sichuan Province, China. Based on the MassMov2D model, the effect of the dynamic process and deposit thickness of the potentially unstable rock mass (deformation rate < −70 mm/year) on this landslide body were numerically simulated. Combined with terrain data and images generated by an Unmanned Aerial Vehicle (UAV), the driving factors of this old landslide were analyzed. The InSAR results show that the motion rate in the middle part of the landslide body is the largest, with a range of −55 to −80 mm/year on average, whereas those of the upper part and toe area were small, with a range of −5 to −20 mm/year. Our research suggests that there is a correlation between the LOS (line of sight) deformation rate and rainfall. In rainy seasons, particularly from May to July, the deformation rate is relatively high. In addition, the analysis suggests that SBAS can provide smoother displacement time series, even in areas with vegetation and the steepest sectors of the landslide. The simulation results show that the unstable rock mass may collapse and form a barrier dam with a maximum thickness of about 16 m at the Zagunao river in the future. This study demonstrates that combining temporal UAV measurements and InSAR techniques from Sentinel-1A SAR data allows early recognition and deformation monitoring of old landslide reactivation in complex mountainous areas. In addition, the information provided by InSAR can increase understanding of the deformation process of old landslides in this area, which would enhance urban safety and assist in disaster mitigation. Full article
(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)
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Graphical abstract

Graphical abstract
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<p>Map showing the tectonic setting of the study area (box) and historical earthquakes. MJF: Minjiang fault, HYF: Huya fault, BYF: Beichuan-Yingxiu fault, WMF: Wenchuan-Maoxian fault, JGF: Jiangyou-Guanxian fault, LQSF: Longquan Shan fault. The active faults are from Deng (2007) [<a href="#B26-remotesensing-13-02552" class="html-bibr">26</a>].</p>
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<p>(<b>a</b>) Elevation and drainages in Lixian County. The black box indicates the scope of <a href="#remotesensing-13-02552-f002" class="html-fig">Figure 2</a>b. (<b>b</b>) Elevation and rivers around Xuecheng area. The black box indicates the scope of <a href="#remotesensing-13-02552-f002" class="html-fig">Figure 2</a>c. (<b>c</b>) The lithology distribution near the XC landslide (denoted by the yellow star).</p>
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<p>Diagram showing the three-dimensional morphology of the Xuecheng (XC) landslide from UAV images obtained by FEIMA D2000. The UAV datasets were collected on 23, 24, and 27 September 2020, with a flight altitude of about 160 m.</p>
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<p>Spatial and temporal baselines of MT-InSAR analysis. (<b>a</b>) PSI method; (<b>b</b>) SBAS method.</p>
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<p>Workflow of the MT-InSAR processing and numerical simulation.</p>
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<p>LOS deformation rate from the PSI method (<b>a</b>) and cumulative displacement at six locations (<b>b</b>).</p>
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<p>LOS motion rate based on the SBAS method (<b>a</b>) and cumulative displacement at six different locations (<b>b</b>).</p>
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<p>SBAS-based time series of LOS displacement on the landslide from 22 March 2018 to 28 April 2020. The reference SAR image was taken on 22 March 2018.</p>
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<p>Longitudinal profile along A–A’. Red dots are LOS deformation rate. The profile position is shown in <a href="#remotesensing-13-02552-f007" class="html-fig">Figure 7</a>a.</p>
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<p>LOS cumulative displacement time-series along longitudinal profile A–A’. The profile position is shown in <a href="#remotesensing-13-02552-f007" class="html-fig">Figure 7</a>a.</p>
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<p>Deformation rates along three profiles. The profile positions are shown in <a href="#remotesensing-13-02552-f006" class="html-fig">Figure 6</a>a.</p>
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<p>Simulated sliding velocity of the unstable rock mass at (<b>a</b>) t = 10 s, (<b>b</b>) t = 30 s, (<b>c</b>) t = 40 s, (<b>d</b>) t = 60 s, (<b>e</b>) t = 80 s, and (<b>f</b>) t = 100 s.</p>
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<p>LOS motion rates along the Zagunao river. White circles represent the areas with large deformations.</p>
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<p>LOS deformation rate versus precipitation for representative pixels near point 6 (locations shown in <a href="#remotesensing-13-02552-f006" class="html-fig">Figure 6</a>a).</p>
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<p>Collapse body thickness in the hypothesized scenario. (<b>a</b>) Distribution of the simulated deposit thickness; (<b>b</b>) deposit thickness along longitudinal profile A–A’.</p>
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27 pages, 10405 KiB  
Article
Engineering-Geological Features Supporting a Seismic-Driven Multi-Hazard Scenario in the Lake Campotosto Area (L’Aquila, Italy)
by Benedetta Antonielli, Francesca Bozzano, Matteo Fiorucci, Salomon Hailemikael, Roberto Iannucci, Salvatore Martino, Stefano Rivellino and Gabriele Scarascia Mugnozza
Geosciences 2021, 11(3), 107; https://doi.org/10.3390/geosciences11030107 - 27 Feb 2021
Cited by 4 | Viewed by 2541
Abstract
This paper aims to describe the seismic-driven multi-hazard scenario of the Lake Campotosto artificial basin (Abruzzo Region, Central Italy), and it can represent a preparatory study for a quantitative multi-hazard analysis. A comprehensive multi-hazard scenario considers all the effects that can occur following [...] Read more.
This paper aims to describe the seismic-driven multi-hazard scenario of the Lake Campotosto artificial basin (Abruzzo Region, Central Italy), and it can represent a preparatory study for a quantitative multi-hazard analysis. A comprehensive multi-hazard scenario considers all the effects that can occur following the base ground shaking, providing a holistic approach to assessing the real hazard potential and helping to improve management of disaster mitigation. The study area might be affected by a complex earthquake-induced chain of geologic hazards, such as the seismic shaking, the surface faulting of the Gorzano Mt. Fault, which is very close to one of the three dams that form the Lake Campotosto, and by the earthquake-triggered landslides of different sizes and typologies. These hazards were individually and qualitatively analyzed, using data from an engineering-geological survey and a geomechanical classification of the rock mass. With regard to the seismic shaking, a quantitative evaluation of the seismic response of the Poggio Cancelli valley, in the northern part of Lake Campotosto, was performed, highlighting different seismic amplification phenomena due to morphologic and stratigraphic features. Some insights about the possible multi-hazard approaches are also discussed. Full article
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<p>Satellite view of the Lake Campotosto area; the location of the following maps and photographs is also shown.</p>
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<p>Panel of photographs of the study area: (<b>a</b>) Panoramic view of Lake Campotosto with, on the right, the Sella Pedicate dam and, in background, the Gorzano Mt. fault system; (<b>b</b>) view of the Gorzano Mt., where the triangular facets roughly indicate the Gorzano Mt. fault trend; (<b>c</b>) lowered ground surface indicating a main trench in the upper part the deep-seated gravitational slope deformations DsGSD (ID.9) of Figures 4 and 5; (<b>d</b>) seismic ambient noise measurement carried out in the Poggio Cancelli valley with, in background, the Poggio Cancelli dam.</p>
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<p>(<b>a</b>) Main tectonic lineaments of the Laga Mts. and Lake Campotosto basin; active extensional faults in red epicenters and focal mechanisms of events with M<sub>w</sub> &gt; 5.0 are also shown (modified after [<a href="#B68-geosciences-11-00107" class="html-bibr">68</a>], reproduced with the permission of Elsevier, Journal of Geodynamics 2020); (<b>b</b>) geological cross-sections across the Gorzano Mt. area (from [<a href="#B69-geosciences-11-00107" class="html-bibr">69</a>], reproduced with the permission of Elsevier, Tectonophysics 2017); section trace A–A’ is shown in <a href="#geosciences-11-00107-f003" class="html-fig">Figure 3</a>a.</p>
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<p>Geological and geomorphological map of the Lake Campotosto basin; section A–A’ across the active Campotosto fault system in the bottom panel.</p>
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<p>(<b>a</b>,<b>b</b>) Earthflow ID.1 represented in the aerial photographs “IGMI-G.A.I. 1954–1955” and “Abruzzo Region 2004–2005” respectively; (<b>c</b>) DsGSD ID.9 represented in the aerial photograph “Volo Base 1954”, with perimeter provided by Saroli and Moro [<a href="#B74-geosciences-11-00107" class="html-bibr">74</a>]; (<b>d</b>) satellite view and (<b>e</b>) 3D view of the DsGSD ID.8 and ID.9 (from Google Earth).</p>
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<p>Landslides ID.5 and ID.7: (<b>a</b>) satellite view; (<b>b</b>,<b>c</b>) photographs of the landslide ID.7 (from [<a href="#B75-geosciences-11-00107" class="html-bibr">75</a>]).</p>
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<p>Map of the geomechanical features of the Lake Campotosto basin; section A–A’ across the active fault system in the bottom panel.</p>
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<p>Engineering-geological map with geophysical investigations of the Poggio Cancelli valley. Legend: GM: alluvial fans; SW: eluvial-colluvial deposits; SWDF: debris deposits; SM: tilled soil and anthropic material; ALS: Laga Fm. flysch.</p>
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<p>Engineering-geological cross-sections A–A’ and F–F’ produced for the Poggio Cancelli valley; vertical axis with 5x exaggeration with respect to horizontal axis.</p>
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<p>Examples of fast Fourier transform (FFT) spectra, horizontal-to-vertical spectral ratio (HVSR) function (the dashed black lines show the standard deviation of the curve), and HVSR rotate plot at the Poggio Cancelli valley obtained on fluviolacustrine deposits (<b>left panel</b>), alluvial fan (<b>middle panel</b>), and Laga Fm. flysch (<b>right panel</b>).</p>
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<p>Multichannel analysis of surface waves (MASW) survey results: (<b>a</b>) shot-gather with source 15 offset; (<b>b</b>) dispersion image in the f–c domain with identification of both fundamental and first higher mode of Rayleigh wave; (<b>c</b>) best model Vs profile resulting from Rayleigh wave dispersion curve inversion.</p>
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<p>Maximum horizontal acceleration obtained by the numerical modeling of the cross-sections A–A’ and F–F’ (see <a href="#geosciences-11-00107-f008" class="html-fig">Figure 8</a> and <a href="#geosciences-11-00107-f009" class="html-fig">Figure 9</a>) at the Poggio Cancelli valley.</p>
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<p>Map showing the amplification factors (AF) value distribution for the period 0.7–1.1 s in the Poggio Cancelli area.</p>
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<p>Map of the main seismically-induced hazards identified in the Lake Campotosto area.</p>
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9529 KiB  
Article
Study of Anti-Sliding Stability of a Dam Foundation Based on the Fracture Flow Method with 3D Discrete Element Code
by Chong Shi, Wenkun Yang, Weijiang Chu, Junliang Shen and Yang Kong
Energies 2017, 10(10), 1544; https://doi.org/10.3390/en10101544 - 6 Oct 2017
Cited by 5 | Viewed by 4185
Abstract
Fractured seepage is an important factor affecting the interface stability of rock mass. It is closely related to fracture properties and hydraulic conditions. In this study, the law of seepage in a single fracture surface based on modified cubic law is described, and [...] Read more.
Fractured seepage is an important factor affecting the interface stability of rock mass. It is closely related to fracture properties and hydraulic conditions. In this study, the law of seepage in a single fracture surface based on modified cubic law is described, and the three-dimensional discrete element method is used to simulate the dam foundation structure of the Capulin San Pablo (Costa Rica) hydropower station. The effect of construction joints and developed structure on dam stability is studied, and its permeability law and sliding stability are also evaluated. It is found that the hydraulic-mechanical coupling with strength reduction method in DEM is more appropriate to use to study the seepage-related problems of fractured rock mass, which considers practical conditions, such as the roughness of and the width of fracture. The strength reduction method provides a more accurate safety factor of dam when considering the deformation coordination with bedrocks. It is an important method with which to study the stability of seepage conditions in complex structures. The discrete method also provided an effective and reasonable way of determining seepage control measures. Full article
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<p>In situ verification test of fracture seepage (<b>a</b>) Seepage-stress coupling test site (France); (<b>b</b>) 3DEC simulation results</p>
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<p>The solution process of safety factor in hydraulic-mechanical coupling with strength reduction method in the discrete-element method (DEM). n, the number of mechanical step per each fluid step and the number of fluid step per each mechanical step can be set by the users. R, the unbalanced force ratio (10<sup>−5</sup> by default): The ratio of unbalanced force to the mean absolute value of force exerted by each surrounding zone, the unbalanced force is the net force acting on a grid point. Nr, characteristic response time, a representative number of steps (maximum limit of 50,000) that characterizes the response time of the system.</p>
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<p>(<b>a</b>) Three-dimensional (3D) model of dam at Capulin San Pablo power station (<b>b</b>) Model profile of the Capulin San Pablo dam.</p>
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<p>(<b>a</b>) Three-dimensional (3D) model of dam at Capulin San Pablo power station (<b>b</b>) Model profile of the Capulin San Pablo dam.</p>
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<p>Concrete-rock interface and the seams between Capulin San Pablo dam sections.</p>
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<p>Layout diagrams of upstream and downstream Capulin San Pablo dam sites and monitoring points A and B.</p>
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<p>Schematic of the hydrostatic pressure on the upstream and downstream Capulin San Pablo dams.</p>
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<p>(<b>a</b>) Deformation curve of dam monitoring points (Examples monitoring points in <a href="#energies-10-01544-f005" class="html-fig">Figure 5</a>a); (<b>b</b>) Deformation curve of dam monitoring point A, B (UG7).</p>
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<p>(<b>a</b>) Distribution characteristics of deformation of dam body with displacement during flood stage (<b>b</b>) Deformation distribution of dam foundation and section 1-1 (strength reduction is 3.00).</p>
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<p>Flood water pressure on the main structural surface of the dam foundation in flood water condition.</p>
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1589 KiB  
Article
Characterization of Seepage Velocity beneath a Complex Rock Mass Dam Based on Entropy Theory
by Xixi Chen, Jiansheng Chen, Tao Wang, Huaidong Zhou and Linghua Liu
Entropy 2016, 18(8), 293; https://doi.org/10.3390/e18080293 - 11 Aug 2016
Cited by 4 | Viewed by 5491
Abstract
Owing to the randomness in the fracture flow system, the seepage system beneath a complex rock mass dam is inherently complex and highly uncertain, an investigation of the dam leakage by estimating the spatial distribution of the seepage field by conventional methods is [...] Read more.
Owing to the randomness in the fracture flow system, the seepage system beneath a complex rock mass dam is inherently complex and highly uncertain, an investigation of the dam leakage by estimating the spatial distribution of the seepage field by conventional methods is quite difficult. In this paper, the entropy theory, as a relation between the definiteness and probability, is used to probabilistically analyze the characteristics of the seepage system in a complex rock mass dam. Based on the principle of maximum entropy, an equation for the vertical distribution of the seepage velocity in a dam borehole is derived. The achieved distribution is tested and compared with actual field data, and the results show good agreement. According to the entropy of flow velocity in boreholes, the rupture degree of a dam bedrock has been successfully estimated. Moreover, a new sampling scheme is presented. The sampling frequency has a negative correlation with the distance to the site of the minimum velocity, which is preferable to the traditional one. This paper demonstrates the significant advantage of applying the entropy theory for seepage velocity analysis in a complex rock mass dam. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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Graphical abstract
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<p>Model diagram of seepage velocity in a borehole.</p>
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<p>Cross-section geological map of the dam site.</p>
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<p>Comparison between the computed seepage velocity distribution and the measured data in (<b>a</b>) Bo+125, (<b>b</b>) Bo+275, and (<b>c</b>) Bo+425.</p>
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<p>Deviation of the computed seepage velocity distribution from the measured data at each measured point in (<b>a</b>) Bo+125, (<b>b</b>) Bo+275, and (<b>c</b>) Bo+425.</p>
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