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19 pages, 9739 KiB  
Article
Rockfall Hazard Evaluation in a Cultural Heritage Site: Case Study of Agia Paraskevi Monastery, Monodendri, Greece
by Spyros Papaioannou, George Papathanassiou and Vassilis Marinos
Geosciences 2025, 15(3), 92; https://doi.org/10.3390/geosciences15030092 (registering DOI) - 7 Mar 2025
Abstract
Rockfall is considered the main geohazard in mountainous areas with steep morphology. The main objective of this study is to assess the rockfall hazard in the cultural heritage site of the Monastery of Agia Paraskevi, Monodendri, in northern Greece, where a recent rockfall [...] Read more.
Rockfall is considered the main geohazard in mountainous areas with steep morphology. The main objective of this study is to assess the rockfall hazard in the cultural heritage site of the Monastery of Agia Paraskevi, Monodendri, in northern Greece, where a recent rockfall event occurred, destroying a small house and the protective fence constructed to protect the Monastery of Agia Paraskevi. To evaluate the rockfall potential, engineering geological-oriented activities were carried out, such as geostructurally oriented field measurements, aiming to simulate the rockfall path and to compute the kinetic energy and the runout distance. In addition, using remote sensing tools such as Unmanned Aerial Vehicles (UAVs), we were able to inspect the entire slope face and detect the locations of detached blocks by measuring their volume. As a result, it was concluded that the average volume of the expected detached blocks is around 1.2 m3, while the maximum kinetic energy along a rockfall trajectory ranges from 1850 to 2830 kJ, depending on the starting point (source). Furthermore, we discussed the level of similarity between the outcomes arising from the data obtained by the traditional field survey and the UAV campaigns regarding the structural analysis of discontinuity sets. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Map showing the location of the study area in the northwestern part of Greece and the two earthquake epicenters (red and orange stars for the Mw 5.4 and Mw 4.6 earthquakes, respectively); (<b>b</b>) photo showing the Agia Paraskevi Monastery and the steep slope of limestone (photo taken on 21 October, 2021; (<b>c</b>) morphology of the Vikos Gorge showing the location of the Agia Paraskevi Monastery (produced from Google Maps, 2025).</p>
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<p>(<b>a</b>,<b>b</b>) Rockfalls at a distance of 20 and 60 m, respectively, from the Monastery of Agia Paraskevi. The image below (<b>a</b>) shows the destroyed rockfall protection netting from the rockfall event in October 2021.</p>
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<p>Flowchart of the methodology applied for the purposes of this study. (SfM: structure from motion; UAV: Unmanned Aerial Vehicle; DSE: Discontinuity Set Extractor; DS: Discontinuity Set).</p>
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<p>Rock slope of the study area delineating the sites where traditional (site D) and UAV-based structural analysis (sites A, B, and C) were carried out. In addition, the location of the Monastery of Agia Paraskevi and the entrance to Vikos Gorge are shown.</p>
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<p>Stereographic projection (lower hemisphere, equal area) of discontinuity poles and main sets resulting from geological compass-derived data (site D).</p>
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<p>Site A. Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE).</p>
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<p>Site B. (<b>a</b>) Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE). (<b>b</b>) Three-dimensional view of the discontinuity sets defined by the DSE; bedding is shown in blue, J1 in red, and J2 in green. It should be noted that the export of the DSE provides a chromatic identification of the discontinuity sets without indicating the orientation of each joint.</p>
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<p>Site C. Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE).</p>
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<p>Stereographic projection (lower hemisphere, equal area) of discontinuity poles and main sets based on geological field survey (site D) and UAV-derived data (Sites A, B, and C). The green color indicates the bedding, the red color indicates J1, and the orange color indicates J2.</p>
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<p>Kinematic analysis for (<b>a</b>) planar sliding, (<b>b</b>) wedge failure, (<b>c</b>) toppling phenomena, and (<b>d</b>) toppling for vertical joints of similar strike with a slope face that dips into the slope.</p>
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<p>Overview of the areas from where rock blocks had been detached (source areas), depicted with red color.</p>
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<p>Traces of the trajectories (tr1, tr2, and tr3) used to determine the parameters of the rockfall. The path is shown as yellow dashed line and trajectories as red.</p>
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<p>Two-dimensional rockfall trajectories, tr1, tr2, and tr3, in the area between the Monastery of Agia Paraskevi (northeast of Monodendri village) and the entrance to the Vikos Gorge. Yellow color refers to the clean hard bedrock,green color to bedrock with little soil or vegetation and red color to rockfall trajectories. The location of suggested barriers is also shown as a black-color line segment.</p>
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27 pages, 11254 KiB  
Article
Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
by Shikun Xie, Zhen Yang, Mingxuan Wang, Guilong Xu and Shuming Bai
Appl. Sci. 2025, 15(5), 2688; https://doi.org/10.3390/app15052688 - 3 Mar 2025
Viewed by 261
Abstract
Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced [...] Read more.
Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced resilience strategies. This study develops a dynamic resilience assessment framework using a two-layer topological model to analyze and optimize the resilience of such networks. The model incorporates trunk and local layers to capture dynamic changes during disasters, and it is validated using the road network in Tibet. The findings demonstrate that critical nodes, including tunnels, bridges, and interchanges, play a decisive role in maintaining network performance. Resilience is influenced by disaster type, duration, and traffic capacity, with collapse events showing moderate resilience and debris flows exhibiting rapid recovery but low survivability. Notably, half-width traffic interruptions achieve the highest overall resilience (0.7294), emphasizing the importance of partial traffic restoration. This study concludes that protecting critical nodes, optimizing resource allocation, and implementing adaptive management strategies are essential for mitigating disaster impacts and enhancing recovery. The proposed framework offers a practical tool for decision-makers to improve transportation resilience in high-risk geological disaster areas. Full article
(This article belongs to the Special Issue Future Transportation Systems: Efficiency and Reliability)
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Figure 1
<p>Research framework.</p>
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<p>Sparse road network with two layers.</p>
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<p>Main layer network topology.</p>
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<p>The local topological structure of the nodes.</p>
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<p>The local topological structure of the segments.</p>
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<p>The schematic diagram of the resilience recovery process of sparse road network.</p>
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<p>Study area and road network distribution.</p>
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<p>Traffic flow data collection diagram.</p>
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<p>Frequency of geological hazards.</p>
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<p>Level of geological hazards.</p>
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<p>The degree of disruption for geological disasters.</p>
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<p>Average debris clearance duration of geological disasters.</p>
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<p>Four types of geological disaster classification examples.</p>
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<p>Simulation process for road network resilience assessment under disaster events.</p>
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<p>The two-layer structure of the regional sparse road network.</p>
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<p>Simplified topology of sparse road network.</p>
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<p>The resilience curve of the road network after a random node is broken.</p>
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<p>Resilience curve after random failures of critical nodes.</p>
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<p>Road network resilience values under varying disaster durations.</p>
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<p>Road network resilience with different residual capacities.</p>
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44 pages, 35373 KiB  
Article
Quantitative Rockfall Hazard Assessment of the Norwegian Road Network and Residences at an Indicative Level from Simulated Trajectories
by François Noël and Synnøve Flugekvam Nordang
Remote Sens. 2025, 17(5), 819; https://doi.org/10.3390/rs17050819 - 26 Feb 2025
Viewed by 258
Abstract
Field observations provide valuable information for rockfall assessments, but estimating physical and statistical quantities related to rockfall propagation directly is challenging. Simulations are commonly used to infer these quantities, but their subjectivity can result in varying hazard land use zonation extents for different [...] Read more.
Field observations provide valuable information for rockfall assessments, but estimating physical and statistical quantities related to rockfall propagation directly is challenging. Simulations are commonly used to infer these quantities, but their subjectivity can result in varying hazard land use zonation extents for different projects. This paper focuses on the application of simulated trajectories for rockfall hazard assessments, with an emphasis on reducing subjectivity. A quantitative guiding rockfall hazard methodology based on earlier concepts is presented and put in the context of legislated requirements. It details how the temporal hazard component, related to the likelihood of failure, can be distributed spatially using simulated trajectories. The method can be applied with results from any process-based software and combined with various prediction methods of the temporal aspect, although this aspect is not the primary focus. Applied examples for static objects and moving objects, such as houses and vehicles, are shown to illustrate the important effect of the object size. For that purpose, the methodology was applied at an indicative level over Norway utilizing its 1 m detailed digital terrain model (DTM) acquired from airborne LiDAR. Potential rockfall sources were distributed in 3D where slopes are steeper than 50°, as most rockfall events in the national landslide database (NSDB) occurred in such areas. This threshold considerably shifts toward gentler slopes when repeating the analysis with coarser DTMs. Simulated trajectories were produced with an adapted version of the simulation model stnParabel. Comparing the number of trajectories reaching the road network to the numerous related registered rockfall events of the NSDB, an indicative averaged yearly frequency of released rock fragments of 1/25 per 10,000 m2 of cliff was obtained for Norway. This average frequency can serve as a starting point for hazard assessments and should be adjusted to better match local conditions. Full article
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Graphical abstract

Graphical abstract
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<p>As a study area increases, the amount of spatial data to consider increases. The situation is exacerbated with the use of detailed terrain models, since they provide a lot of spatial data with their high fidelity. Still, the tradeoff can be rewarding if it comes with a potential gain in predictive accuracy of the simulations. Earlier novel studies simply did not have a choice, since detailed DTMs were not available (e.g., Guzzetti et al. [<a href="#B60-remotesensing-17-00819" class="html-bibr">60</a>]; Frattini et al. [<a href="#B45-remotesensing-17-00819" class="html-bibr">45</a>]; or Derron et al. [<a href="#B64-remotesensing-17-00819" class="html-bibr">64</a>] work delivered in 2009). With the advances in remote sensing technology and computing power, it is now possible to push the bar beyond considering 100,000 Megapixels. This increases the amount of information to process by several orders of magnitude.</p>
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<p>Effect of the loss of precision on the interpretations related to the rockfall sources when using coarser DTMs. At a cell size of 1 m, most mapped sources have a cliff steeper than 50° in their vicinity. With coarse DTMs, the cumulative curves are artificially shifted toward gentler maximal slopes.</p>
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<p>Conceptual representation of the possible source distribution spatial bias and correcting strategies. One thousand points are shown in each example square. The squares thus represent about 120 × 120 m of cliff surface for the chosen number of simulated trajectories per cliff surface of this application case.</p>
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<p>Overview of the study area and the intermediate states of the methodology, visualized in QGIS. The national 1 m DTM is subdivided into 2033 15 × 15 km tiles (<b>a</b>). An area of about half of one 15 × 15 km tile is shown in (<b>b</b>), subdivided into 35 1.9 × 1.9 km sub-tiles (out of 64 per tile). The sources shown in orange in (<b>c</b>) are distributed proportionally to the cliff surfaces. The 3D-simulated trajectories and their resulting rasterized paths pass seamlessly across sub-tile boundaries. This way, the temporal information is seamlessly distributed spatially following realistic possible rockfall propagations. The rockfall hazard was estimated locally with moving averages using a circular floating window of the size of hypothetical exposed objects. The indicative hazard zones (<b>d</b>) were produced for the boundaries of the hypothetical objects. Elevation and imagery data in (<b>a</b>) are from the ©Mapzen (MIT license) and ©OpenStreetMap contributors (ODbL license), respectively. Elevation and imagery data in (<b>b</b>–<b>d</b>) are from ©hoydedata.no and ©norgeskart.no, respectively (both with a Norwegian license for open government data—NLOD license). EPSG: 25833.</p>
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<p>Qualitative overview of the considered portion of the Norwegian road network (county and national roads). Attributed information such as the speed limits and regulated <span class="html-italic">P<sub>Hazard_max</sub></span> threshold based on the yearly averaged daily traffic volume (AADT) is shown in (<b>a</b>,<b>b</b>). Since <span class="html-italic">λ</span> is subject to change based on local conditions, the results were also expressed in terms of the local maximal tolerable frequency of block fragments (<span class="html-italic">λ<sub>max</sub></span>). The obtained <span class="html-italic">λ<sub>max</sub></span> values are shown in (<b>c</b>,<b>d</b>) for an exposed object length of 1000 m and for a vehicle length combined with its stopping distance, respectively. Elevation, imagery, and road network data are from the ©Mapzen (MIT license) and ©OpenStreetMap contributors (ODbL license) and ©vegdata.no (NLOD license), respectively. EPSG: 25833.</p>
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<p>Compiled overview of precisely mapped average and maximal runout distances for several rockfall events and experiments with known source heights. Given the wide range of site geometries and scales, the data are shown in terms of reach angles (source-rock line-of-sight) over a hypothetical slope profile for comparison purposes. Note the wide variety of observed maximal runout distances (<b>a</b>) around a median value of 31.1°, or (<b>b</b>), around a median of 34.6° for the average runout distances (center of mass of the mapped deposits). (Mapped data from: Caviezel et al. [<a href="#B78-remotesensing-17-00819" class="html-bibr">78</a>]; Hibert et al. [<a href="#B79-remotesensing-17-00819" class="html-bibr">79</a>]; Bourrier et al. [<a href="#B13-remotesensing-17-00819" class="html-bibr">13</a>]; Noël et al. [<a href="#B16-remotesensing-17-00819" class="html-bibr">16</a>]; Pettersen [<a href="#B80-remotesensing-17-00819" class="html-bibr">80</a>]; Midtun [<a href="#B81-remotesensing-17-00819" class="html-bibr">81</a>]; Domaas [<a href="#B82-remotesensing-17-00819" class="html-bibr">82</a>]; Erickson [<a href="#B83-remotesensing-17-00819" class="html-bibr">83</a>]; Lévy and Verly [<a href="#B84-remotesensing-17-00819" class="html-bibr">84</a>]; Barstad [<a href="#B85-remotesensing-17-00819" class="html-bibr">85</a>]; Volkwein et al. [<a href="#B86-remotesensing-17-00819" class="html-bibr">86</a>]).</p>
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<p>Cumulative curves showing the ability of the simulations to properly reach the mapped deposited rock fragments for different sites. Perfect or over-conservative predictions would lay on the red curve. The further a cumulative curve deviates from the red one (observations), the more mapped rock fragments it misses due to predicting too short runout distances compared to the observations. (Mapped data from: Caviezel et al. [<a href="#B78-remotesensing-17-00819" class="html-bibr">78</a>]; Hibert et al. [<a href="#B79-remotesensing-17-00819" class="html-bibr">79</a>]; Bourrier et al. [<a href="#B13-remotesensing-17-00819" class="html-bibr">13</a>]; Noël et al. [<a href="#B16-remotesensing-17-00819" class="html-bibr">16</a>]; Pettersen [<a href="#B80-remotesensing-17-00819" class="html-bibr">80</a>]; Midtun [<a href="#B81-remotesensing-17-00819" class="html-bibr">81</a>]; Domaas [<a href="#B82-remotesensing-17-00819" class="html-bibr">82</a>]; Erickson [<a href="#B83-remotesensing-17-00819" class="html-bibr">83</a>]; Lévy and Verly [<a href="#B84-remotesensing-17-00819" class="html-bibr">84</a>]; Barstad, [<a href="#B85-remotesensing-17-00819" class="html-bibr">85</a>]; Volkwein et al. [<a href="#B86-remotesensing-17-00819" class="html-bibr">86</a>]).</p>
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<p>Example of the results produced over Norway are shown here in (<b>a</b>,<b>b</b>) in the Lom municipality, Innlandet county, Norway. For comparison with the indicative hazard zones, the existing hazard zones dominated by rockfall processes in this area are shown in (<b>c</b>). The hazard assessment project with less constraining results in (<b>c</b>) supported the chosen zones with RAMMS::ROCKFALL simulations (Andresen et al. [<a href="#B89-remotesensing-17-00819" class="html-bibr">89</a>]). Elevation, imagery, road network, and existing hazard zones data are from ©hoydedata.no, ©norgeskart.no, ©vegdata.no, and ©kartkatalog.nve.no, respectively (all with a NLOD license). EPSG: 25833.</p>
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<p>Examples of the results produced over Norway with a focus on their application to linear infrastructures. In (<b>a</b>,<b>b</b>), the results are shown around the Mannheller ferry infrastructure, as well as for a portion of the Lærdal valley in (<b>c</b>,<b>d</b>), Vestland county, Norway. Elevation, imagery, road network, and rockfall records data are from ©hoydedata.no, ©norgeskart.no, ©vegdata.no, and ©kartkatalog.nve.no, respectively (all with a NLOD license). EPSG: 25833.</p>
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<p>Conceptual representation of the distinctions between susceptibility and hazard concepts. If an exposed object can be reached by potential rockfalls, it is considered susceptible (e.g., the yellow and orange houses). Despite being susceptible, it can have a tolerable level of hazard if it is not reached frequently (e.g., the yellow houses). In such a situation, the object is constrained by susceptibility zones (any zones within the dashed black limits), but not by restrictive hazard zones (orange zones).</p>
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<p>Ratio of residences constrained by the susceptibility zones (black limits) and the indicative hazard zones (orange-red limits). Note that the indicative hazard zones can expand or contract depending on the expected <span class="html-italic">λ</span>. The ratios thus vary depending on the chosen <span class="html-italic">λ</span>. All residences were considered without differentiating their respective safety class. * Like for stnParabel’s process-based results, the quantitative hazard methodology could also be applied to Rockyfor3D’s results.</p>
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<p>Ratio of county and national roads constrained by the susceptibility zones (black limits) and the indicative hazard zones (orange-red limits). Note that the indicative hazard zones can expand or contract depending on the expected <span class="html-italic">λ</span>. The ratios thus vary depending on the chosen <span class="html-italic">λ</span>. In (<b>a</b>), all roads were considered without differentiating their respective safety class. In (<b>b</b>,<b>c</b>), the ratio of road segments exceeding their respective <span class="html-italic">P<sub>Hazard_max</sub></span> threshold is given per individualized safety class. * Like for stnParabel’s process-based results, the quantitative hazard methodology could also be applied to Rockyfor3D’s results.</p>
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<p>The Mel de la Niva Mountain overlooking the Evolène village in Val d’Hérens, Valais, Switzerland. The mapped rockfall history of the site (Noël et al. [<a href="#B16-remotesensing-17-00819" class="html-bibr">16</a>]) and rock deposits (BEG) overlays the orthophotos and elevation data from ©swisstopo. Also, the coarse contours of the alpine glaciers retreat simulation by Seguinot et al. [<a href="#B95-remotesensing-17-00819" class="html-bibr">95</a>] are shown as a rough temporal reference. EPSG: 21781.</p>
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<p>Comparison of the simulated results to the mapped rockfall paths, deposited block fragments, and to the reconstructed trajectories of the 2015 event. The simulated paths and envelopes for the three scenarios are shown in yellow, green, and blue in (<b>a</b>). The simulated number of passing trajectories from the combined scenarios is shown in shades of blue in (<b>b</b>). The simulated translational velocities of the three small set of five trajectories are shown in (<b>c</b>) without the near vertical lines associated with the velocity drops at impacts. The cumulative curves of the mapped deposited block fragments from the BEG SA and of the simulated deposited block fragments in relation with their corresponding reach angles are shown in (<b>d</b>). Elevation data are from ©swisstopo. EPSG: 21781.</p>
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<p>Distinction in between (<b>a</b>) the number of passing trajectories and (<b>b</b>) the number of reaching trajectories for exposed objects with a width of 30 m perpendicular to the rockfall paths. Elevation data from ©swisstopo. EPSG: 21781.</p>
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<p>Contours of the same constant hazard probabilities (1/300) based on a frequency of 13 bl./40 yr. for the same simulation results but obtained considering different widths around the simulated paths in (<b>a</b>). In (<b>b</b>), the considered object width remains constant but contours of different hazard probabilities are shown. One would obtain similar probability contours in (<b>b</b>) if using the lower average frequency since deglaciation of 1 bl./9 yr., but with the classes shifted by one category, i.e., the 1/100 contour would become the 1/300 contour. The cumulative contours in red consider all mapped blocks (n = 449). Otherwise, the 50, 60, 70, 80 and 90% contours, respectively, become ~70, ~75, ~78, ~80 and ~85% when neglecting the block fragments that could alternatively be glacial erratics. For this site, the values and related zones are expressed for the center of the hypothetical exposed objects for continuity with (Noël et al. [<a href="#B15-remotesensing-17-00819" class="html-bibr">15</a>]). Elevation and imagery data from ©swisstopo. EPSG: 21781.</p>
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<p>Ratio of mapped deposited rock fragments constrained by the susceptibility zones (black limits) and the indicative hazard zones (orange-red limits). Note that the indicative hazard zones can expand or contract depending on the expected <span class="html-italic">λ</span>. The ratios thus vary depending on the chosen <span class="html-italic">λ</span>. * Like for stnParabel’s process-based results, the quantitative hazard methodology could also be applied with Rockyfor3D’s results.</p>
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26 pages, 12995 KiB  
Article
Geohazard Plugin: A QGIS Plugin for the Preliminary Analysis of Landslides at Medium–Small Scale
by Marta Castelli, Andrea Filipello, Claudio Fasciano, Giulia Torsello, Stefano Campus and Rocco Pispico
Land 2025, 14(2), 290; https://doi.org/10.3390/land14020290 - 30 Jan 2025
Viewed by 1150
Abstract
Landslides are a major global threat, endangering lives, infrastructure, and economies. This paper introduces the Geohazard plugin, an open-source tool for QGIS, designed to support medium–small-scale landslide analysis and management. The plugin integrates several algorithms, including the Groundmotion–C index for evaluating SAR data [...] Read more.
Landslides are a major global threat, endangering lives, infrastructure, and economies. This paper introduces the Geohazard plugin, an open-source tool for QGIS, designed to support medium–small-scale landslide analysis and management. The plugin integrates several algorithms, including the Groundmotion–C index for evaluating SAR data reliability, Landslide–Shalstab for assessing shallow landslide susceptibility, and Rockfall–Droka for estimating rockfall invasion areas and the rockfall relative (spatial) hazard. An application example is provided for each module to facilitate validation and discussion. A case study from the Western Italian Alps highlights the practical application of the Rockfall–Droka modules, showcasing their potential to identify critical zones by integrating the results on affected areas, process intensity, and preferential paths. Emphasis is given to the calibration of model parameters, a critical aspect of the analysis, achieved through a back-analysis of a rockfall event that occurred in June 2024. The Geohazard plugin streamlines geohazard assessments, providing land managers with actionable insights for decision-making and risk mitigation strategies. This user-friendly GIS tool contributes to enhancing resilience in landslide-prone regions. Full article
(This article belongs to the Section Land – Observation and Monitoring)
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Figure 1
<p>Diagram showing the relationship between LOS parameters of satellite, terrain aspect, and SAR imaging geometry [<a href="#B33-land-14-00290" class="html-bibr">33</a>]: (<b>a</b>) β is the aspect of the local ground surface (terrain), and φ is the aspect of the LOS; and (<b>b</b>) α is the inclination of the ground, θ is the incidence angle of the LOS, and γ is the angle between the ground and the LOS.</p>
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<p>Example of application of the <span class="html-italic">Groundomotion–C index</span> algorithm (DTM 25 m, Sentinel-1): (<b>a</b>) <span class="html-italic">C index</span> map in ascending orbit; (<b>b</b>) <span class="html-italic">C index</span> map in descending orbit; (<b>c</b>) class of visibility map in ascending orbit; and (<b>d</b>) class of visibility map in descending orbit.</p>
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<p>The hydro-mechanical model underlying the <span class="html-italic">Shalstab</span> module [<a href="#B39-land-14-00290" class="html-bibr">39</a>]: (<b>a</b>) infinite slope model, where z is the thickness of the shallow layer, h is the height of the saturated layer above the failure surface, and θ is the inclination of the slope; and (<b>b</b>) geometry of the catchment and flow path of water in the hydrological model, where a is the contributing upslope area draining across the contour length b.</p>
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<p>Results of the application of the <span class="html-italic">Shalstab</span> module to the Dego area: (<b>a</b>) critical infiltrated rainfall q<sub>cr</sub> computed across the entire area using a DTM 10 m, with the zoomed-in region and validation points indicated (refer to <a href="#land-14-00290-t006" class="html-table">Table 6</a> and <a href="#land-14-00290-t007" class="html-table">Table 7</a>); and (<b>b</b>) zoomed-in area, highlighting the shallow landslides triggered during the November 1994 heavy rainfall event, as well as profile 1 and profile 2 used to compare the results obtained with different DTM resolutions (<a href="#land-14-00290-f005" class="html-fig">Figure 5</a>).</p>
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<p>Comparison between the critical infiltrated rainfall q<sub>cr</sub> computed along profile 1 and profile 2 as depicted in <a href="#land-14-00290-f004" class="html-fig">Figure 4</a>b, analyzed using different DTM resolutions.</p>
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<p>Invasion zone definition and associated geometrical components in the <span class="html-italic">Droka</span> modules: (<b>a</b>) <span class="html-italic">Droka_Basic</span> representation of the cone with the apex in the source point S(x<sub>0</sub>,y<sub>0</sub>) and geometry defined by the angles θ, α, and φ<sub>p</sub> (in light grey). The cone is sectioned by a vertical plane passing through the generic topographic point P(x,y). The orange line represents the energy line. The horizontal dark green line refers to the total energy of the block. (<b>b</b>) <span class="html-italic">Droka_Flow</span> representation of the rockfall path (blue line) originated from the source point S, sectioned by a vertical plane passing through the generic topographic point P(x,y).</p>
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<p>Invasion area and block kinetic energy for the synthetic slope obtained with <span class="html-italic">Droka_Basic</span> and <span class="html-italic">Droka_Flow</span>. Calibrated parameters are φ<sub>p</sub>: 45°, α: 10°, and standard deviation: 0.1 m.</p>
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<p>Bobbio Pellice rockfall event, 24 June 2024: (<b>a</b>) destroyed building; (<b>b</b>) involved area and some stopped blocks on the slope, in red—assumed source area in yellow; and (<b>c</b>) block path reaching the destroyed building, after [<a href="#B44-land-14-00290" class="html-bibr">44</a>].</p>
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<p><span class="html-italic">Droka_Basic</span> results for the kinetic energy of the back-analysis of the rockfall event. Calibrated parameters are φ<sub>p</sub>: 37° and α: 22°.</p>
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<p><span class="html-italic">Droka_Flow</span> results of the back-analysis of the rockfall event, influence of standard deviation: (<b>a</b>) standard deviation: 0.1 m; (<b>b</b>) standard deviation: 0.5 m; (<b>c</b>) standard deviation: 1 m; and (<b>d</b>) standard deviation: 5 m.</p>
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<p><span class="html-italic">Droka_Basic</span> results of the back-analysis of the rockfall event, influence of DTM cell size: (<b>a</b>) cell size: 1 m; (<b>b</b>) cell size: 5 m; and (<b>c</b>) cell size: 10 m.</p>
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<p><span class="html-italic">Droka_Flow</span> results of the back-analysis of the rockfall event, influence of DTM cell size: (<b>a</b>) cell size: 1 m; (<b>b</b>) cell size: 5 m; and (<b>c</b>) cell size: 10 m.</p>
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<p>Susceptibility analysis for the Bobbio Pellice case study: (<b>a</b>) source points distribution in white and location of the buildings in magenta—the white envelope represents the invasion area of the 24 June 2024 rockfall event; (<b>b</b>) runout areas by <span class="html-italic">Droka_Basic</span> (yellow) and <span class="html-italic">Droka_Flow</span> (blue); (<b>c</b>) mean kinetic energy, <span class="html-italic">Droka_Basic</span>; and (<b>d</b>) mean kinetic energy, <span class="html-italic">Droka_Flow</span>.</p>
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<p>Susceptibility analysis for the Bobbio Pellice case study: (<b>a</b>) rockfall frequency; and (<b>b</b>) relative (spatial) hazard. Yellow dots represent source points.</p>
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19 pages, 6972 KiB  
Article
Blasting of Unstable Rock Elements on Steep Slopes
by Marco Casale, Giovanna Antonella Dino and Claudio Oggeri
Appl. Sci. 2025, 15(2), 712; https://doi.org/10.3390/app15020712 - 13 Jan 2025
Viewed by 623
Abstract
The improvement of safety conditions on hazardous rock slopes in civil work, mining and quarrying, and urban environments can be achieved through the use of explosives for the removal of unstable rock elements and final profiling. This technique is often applied because, in [...] Read more.
The improvement of safety conditions on hazardous rock slopes in civil work, mining and quarrying, and urban environments can be achieved through the use of explosives for the removal of unstable rock elements and final profiling. This technique is often applied because, in most cases, drill and blast operations, where they can be used, are cheaper and faster than other techniques and require fewer subsequent maintenance interventions. Blasting represents a suitable and effective solution in terms of different geometries, rock formation types, access to site, safety, and the long-term durability of results. The primary purpose of this approach is the improvement of the safety conditions of sites, depending on their local features, as well as the safety of workers, so that the blasting scheme, geometry, and firing can be carefully adapted, thus imposing relevant limitations on the operating techniques. All these constraints associated with complex logistics make it difficult to standardize the demolition technique, due to different situations in terms of extension, location, fracturing state, and associated traffic risk. Considering the significant number of influencing factors for both the rock mass features and for the topography, the present research has been necessarily validated through the analysis of several case histories, thus on an experiential basis focusing on some simple control parameters to help engineers and practitioners regarding the first design and control of blasting schemes. Full article
(This article belongs to the Special Issue Advanced Blasting Technology for Mining)
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<p>Scheme of mechanism of explosive cut-action according to quasi-static approach for new fracture formation between adjacent blastholes—modified from [<a href="#B16-applsci-15-00712" class="html-bibr">16</a>].</p>
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<p>The case of Meiringen in Switzerland. (<b>a</b>) Plan view of the blasting project. Z1 area to be evacuated; (<b>b</b>) detailed plan with the subdivision of the block to be blasted in 4 rounds; (<b>c</b>) lateral section of the drill hole pattern from the firing plan of the second round; (<b>d</b>) view of the muck fan after the first round (modified after [<a href="#B18-applsci-15-00712" class="html-bibr">18</a>]).</p>
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<p>The case of Quebec highway 155. (<b>a</b>) Plan view of the blasting project; (<b>b</b>) perspective view of the dihedral to be blasted; (<b>c</b>) lateral section of the drill hole pattern from the firing plan (modified after [<a href="#B19-applsci-15-00712" class="html-bibr">19</a>]).</p>
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<p>The case of the provincial road of Val Mastallone in the northwest of the Alpine range in Italy. (<b>a</b>) Firing plan (non-electric ignition has been adopted); (<b>b</b>) schematic cross section; (<b>c</b>) result of the blasting, with rock fragments accumulated in a regular shape on the road at the base of the slope (paving was protected from impacts with a granular debris cover) (modified after [<a href="#B20-applsci-15-00712" class="html-bibr">20</a>]).</p>
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<p>The case of the provincial road Gattinara-Borgosesia in the northwest of the Alpine range in Italy. (<b>a</b>) Firing plan designed by the authors; (<b>b</b>) cross section with measures in m; (<b>c</b>) the ignition of the round (non-electric ignition has been adopted); (<b>d</b>) result of the blasting in terms of fragments size on the road (paving was protected from impacts with a granular debris cover) at the base of the subvertical slab (modified after [<a href="#B20-applsci-15-00712" class="html-bibr">20</a>]).</p>
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<p>The case of provincial road 169 in Val Germanasca in the northwest of the Alpine range in Italy. (<b>a</b>) Firing plan; (<b>b</b>) overall view of the block set; (<b>c</b>) the ignition phase of round; (<b>d</b>) result of the blasting resulting in large blocks. Consider that the difference in elevation of the claimed volume and the base road was about 130 m.</p>
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<p>The gneiss quarry in Northern Italy. (<b>a</b>) Firing plan (top view) with the locations of blastholes drilled vertically, as designed by the authors. (<b>b</b>) Cross section with drilled holes; in red, joints and fractures, in orange blastholes, in blue rear fracture; the bench is about 25 m high. (<b>c</b>) Result of the blasting: in the upper part of the quarry face, the ‘clean’ and regular residual surfaces are visible, as is the blasted material in the quarry yard at the base.</p>
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<p>Normalized powder factor (PF) vs. ratio spacing/borehole drilling diameter.</p>
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19 pages, 26960 KiB  
Article
The Northern Giona Fault Zone, a Major Active Structure Through Central Greece
by Leonidas Gouliotis and Dimitrios Papanikolaou
GeoHazards 2024, 5(4), 1370-1388; https://doi.org/10.3390/geohazards5040065 - 18 Dec 2024
Viewed by 740
Abstract
The steep northern slopes of Giona Mt in central continental Greece are the result of an E-W normal fault dipping 35–45° to the north, extending from the Mornos River in the west to the village of Gravia in the east. This fault creates [...] Read more.
The steep northern slopes of Giona Mt in central continental Greece are the result of an E-W normal fault dipping 35–45° to the north, extending from the Mornos River in the west to the village of Gravia in the east. This fault creates a significant elevation difference of approximately 1500 m between the northern Giona footwall and the southern Iti hanging wall. The footwall comprises imbricated Mesozoic carbonates of the Parnassos unit, which exhibit large-scale drag folding near and parallel to the fault. The hanging wall comprises deformed sedimentary rocks of the Beotian unit and tectonic klippen of the Eastern Greece unit, forming a southward-tilted neotectonic block with subsidence near the Northern Giona Fault and uplift near the Ypati fault to the north. These two E-W faults represent younger structures disrupting the older NNW-trending tectonic framework. Fault scarps are observed all along the 14 km length of the Northern Giona fault accompanied by cataclastic zones, separating the carbonate formations of the Parnassos Unit from thick scree, slide blocks, boulders and olistholites. Inversion of fault-slip data has shown a mean slip vector of 45°, N004°E, which aligns with the current regional extensional deformation of the area, as confirmed by focal mechanism solutions. Based on the general asymmetry of the alpine units in the hanging wall, we interpret a listric fault geometry at depth using slip-line analysis and we forward modelled a disrupted fault-propagation fold using kinematic trishear algorithms, estimating a total displacement of 6500 m and a throw of approximately 2000 m. Seismic activity in the area of the Northern Giona Fault includes a magnitude 6.1 earthquake in 1852, which caused casualties, rockfalls and extensive damage, as well as a magnitude 5.1 event in 1983. The expected seismic magnitude is deterministically estimated between 6.2 and 6.7, depending on the potential westward continuation of the Northern Giona Fault beyond the Mornos River to the Northern Vardoussia saddle. The seismic hazard zone includes several villages located near the fault, particularly on the hanging wall, where intense landslide activity during seismic events could result in severe damage to regional infrastructure. The neotectonic development of the Northern Giona Fault highlights the importance of extending seismotectonic research into the mountainous regions of central Greece within the alpine formations, beyond the post-orogenic sedimentary basins. Full article
(This article belongs to the Special Issue Active Faulting and Seismicity—2nd Edition)
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<p>Morphological map of the mountainous region of central Greece between the Corinth Gulf and the Sperchios Valley/Maliakos Gulf. The northern boundary is defined by the northern slopes of Iti Mt, where the Ypati Fault (YF) creates a significant topographic difference of 2000 m, separating the mountainous area from the Sperchios Valley. To the south, the northern slopes of Giona Mt. align with the Northern Giona Fault (NGF), marking a topographic difference of 1500 m between Giona Mt and Iti Mt.</p>
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<p>Three-dimensional perspective of the studied area with view from the east-northeast (<b>top</b>) and view from the west (<b>bottom</b>). In both views, the NGF and YF are indicated along the abrupt northern slopes of Giona and Iti Mts, respectively. These two subparallel faults have shaped the landscape, creating high mountain peaks at their northern edges on the uplifted footwalls and subsidence to the southern edges.</p>
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<p>Map illustrating the distribution of seismic epicenters for instrumental (solid circles) and historical earthquakes (purple triangles), along with focal mechanisms for M &gt; 4 (NOA—<a href="http://emsc-csem.org" target="_blank">http://emsc-csem.org</a>), GPS velocity vectors [<a href="#B7-geohazards-05-00065" class="html-bibr">7</a>] and neotectonic faults (black lines). Central Greece’s active deformation results primarily from displacements on E-W-trending faults and some NE-SW strike-slip events.</p>
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<p>(<b>Top</b>) Geotectonic map of the mountainous region of central Greece between the Sperchios valley to the north and the Corinth gulf to the south. Yellow dashed rectangle shows the extent of the detailed geological map along the NGF of Figure 6. (<b>Middle</b>) NNW—SSE cross section from the Orthrys Mt to the Corinth Gulf showing the geometry of the alpine units and the major neotectonic boundaries, including the YF and the NGF. Dashed blue line indicates the top carbonate of the Parnassos unit. (<b>Bottom</b>) 2D forward model across the NGF showing the deformation of a 10 km layer-cake model with the top horizon corresponding to the pre-Pliocene tectonic framework as built in Figure 11. The Mw 5.1 19 September 1983 earthquake is plotted on the profile alongside the NGF. 1: Pindos Unit, 2: Penteoria unit, 3: Vardoussia unit, 4: Parnassos unit, 5: Beotian unit, 6: Eastern Greece unit, 7: Late Oligocene–Miocene molassic sediments, 8: Late Miocene-Quaternary sediments, 9: Neotectonic and active fault, 10: Miocene Extensional Detachment, including the Itea-Amfissa detachment (IAD) 11: major thrust fault.</p>
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<p>Panoramic view looking SSW of the NGF (red dashed line) along the northern slopes of Giona Mt. The high-elevated area of northern Giona belonging to the Parnassos unit occur in the footwall whereas the ophiolites and related sediments of the uppermost Eastern Greece unit occur in the hanging wall.</p>
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<p>Geological map and cross section of the northern Giona region. 1: alluvial deposits, 2: scree deposits, 3: Neogene deposits, 4: Molassic sediments of Oligocene—Middle Miocene, 5: Triassic—Jurassic carbonates of the SubPelagonian unit, 6: Jurassic ophiolites, 7: Beotian Unit with Jurassic—Cretaceous pelagic limestones, 8: Eocene flysch of the Parnassos unit, 9: Mesozoic Carbonate platform of the Parnassos unit, with an older b2 (black) and a younger b3 (red) bauxite horizons in the cross section, 10: Eocene flysch of the Vardousssia unit, 11: Olistholites mainly of Carbonate rocks, 12: Neotectonic and active normal fault, 13: IAD, Itea-Amfissa Detachment, Miocene extensional Detachment, 14: Overthrust, 15: thrust, 16. Base of gravity slide. FW1, HW1: Imbricated tectonic units of the Parnassos nappe. FW, HW: NGF’s footwall and hanging wall.</p>
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<p>View to the west of the footwall at the central part of the NGF, directly east of the Vrayla peak, at 1800–2000 m altitude. The Parnassos carbonate sequence is characterized by two members in this site: a lower one of Late Jurassic age (Js) and an upper one of Late Jurassic-Early Cretaceous age (J13-K6) separated by a bauxite horizon (b2—[<a href="#B24-geohazards-05-00065" class="html-bibr">24</a>]). In the sketch, solid lines indicate W-dipping strata, while dashed lines indicate N-dipping strata. This change in dip azimuth is characteristic of a kilometric scale normal drag developed near the NGF.</p>
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<p>View from the east of the NGF. Two notable sites (<b>A</b>,<b>B</b>) where the grooved and striated fault surface is exposed and measurable, showing top-to-N movement.</p>
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<p>Outcrops of the fault surfaces along the northern Giona slopes. (<b>A</b>) Fault surface (red arrows) at the central part of the NGF. (<b>B</b>) Close view of a north-dipping fault plane along the high slopes at the western side of the NGF. (<b>C</b>) Characteristic cross-section of the fault zone in the eastern part of the NGF, showing a well-developed damage zone that grade to thick fault core (red arrow). (<b>D</b>) Curved fault surface at the eastern termination of the NGF close to the Gravia village.</p>
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<p>Fault-slip data and calculation of stress tensor with the method of direct inversion for the NGF. Lower hemisphere, equal-area stereographic projections. <b>Left pane</b> shows fault-slip data and the calculated stress axes (σ1, σ2, σ3). <b>Middle pane</b> is a fluctuation histogram of the deviation angle (angle between measured and calculated slip vectors) and stress ratio R(σ2 − σ3)/(σ1 − σ3). <b>Right pane</b> shows the P–T axes.</p>
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<p>Construction of a 10 km-layer cake model illustrating a five-stage progressive deformation of the NGF footwall and hanging wall through the application of a trishear kinematic model with increasing displacements of 1300 m, 2600 m, 3900 m, 5200 &amp; 6500 m. Details of the trishear model are provided within text. The top layer represents the regional pre-tectonic level, corresponding to the pre-Pliocene deformed state, which includes early orogenic Late Eocene thrust faults (solid lines with triangles) overlying the Parnassos flysch. The Parnassos flysch comprises two members: the lower red pelites and the upper pelitic-sandstone, separated by a dashed line. Unconformably overlying these units are late-orogenic Miocene molasse deposits (wavy brown line) within the Iti and northern Giona fault blocks. The northward-dipping listric geometry of the NGF at depth is based on slip-line analysis [<a href="#B45-geohazards-05-00065" class="html-bibr">45</a>].</p>
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<p>Map showing the spatial distribution of seismic intensity recorded by various catastrophic phenomena associated with the two significant earthquakes in the region. The solid ellipse represents the macroseismic intensities from the 14 July 1852 earthquake, which had a magnitude of 6.1 (yellow star). The dashed ellipse outlines the area affected by the microseismicity (magnitudes between 2.0 and 4.2) that followed the 19 September 1983, earthquake, which had a magnitude of 5.1 and a fault plane solution of an ENE-WSW normal fault.</p>
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24 pages, 5327 KiB  
Article
Case Study on the Evaluation of Rock Cut Stability for Highways in Egypt: Implications for Transportation Infrastructure and Safety
by Wael R. Abdellah, Stephen D. Butt, Ahmed Rushdy Towfeek, Abd El-Samea W. Hassan, Mahmoud M. Abozaied, Faisal A. Ali, Mahrous A. M. Ali and Abdullah Omar M. Bamousa
Geosciences 2024, 14(12), 342; https://doi.org/10.3390/geosciences14120342 - 12 Dec 2024
Viewed by 893
Abstract
This study addresses critical stability concerns along a key segment of the Egyptian highway linking Aswan and Cairo, focusing on a one-kilometer rock-cut section that is vital for transportation and commerce. Recent evaluations have highlighted significant rockfall and slope instability risks in this [...] Read more.
This study addresses critical stability concerns along a key segment of the Egyptian highway linking Aswan and Cairo, focusing on a one-kilometer rock-cut section that is vital for transportation and commerce. Recent evaluations have highlighted significant rockfall and slope instability risks in this area, posing serious safety challenges. The primary objective is to identify and analyze the factors contributing to slope instability, assess potential rockfall hazards, and recommend effective mitigation strategies. To achieve this, this study employs a comprehensive, multi-faceted methodology. Key variables influencing slope stability are first identified, followed by a detailed analysis of discontinuity data using stereographic projection based on joint surveys. Rockfall propagation distances are then modeled through specialized software, while the Plaxis 2D tool 2023.2(V23.2.0.1059) is applied for advanced numerical modeling of slope behavior. The results indicate a pressing need for mitigation measures to address ongoing instability issues, including planar and wedge failures and raveling rockfalls, which pose considerable safety risks to road users. This study highlights the necessity of a robust and comprehensive mitigation strategy to ensure road safety and support uninterrupted commercial activity along this essential highway. Full article
(This article belongs to the Section Geomechanics)
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<p>Geological map of Wadi Qena area [<a href="#B33-geosciences-14-00342" class="html-bibr">33</a>,<a href="#B36-geosciences-14-00342" class="html-bibr">36</a>,<a href="#B37-geosciences-14-00342" class="html-bibr">37</a>].</p>
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<p>Configuration of the 2 km long rock-cut section and highway layout.</p>
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<p>Short roadside and uneven rock edge (<b>left</b>) with wedge collapse and rock debris defining a road section (<b>right</b>).</p>
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<p>Sinkhole filled with varied-sized boulders cemented with weak reddish materials.</p>
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<p>The working face alongside a roadway.</p>
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<p>Mohr’s envelope for location Al-Ahaywa (1), limestone, Sohag.</p>
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<p>The Intensity–Duration–Frequency (IDF) curve.</p>
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<p>In situ representation of Geometry #1.</p>
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<p>The safety factor for the slope corresponding to Geometry #1.</p>
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<p>The redesign of the slope resulted in a 2.5-fold increase in the Factor of Safety (FOS), reflecting a substantial improvement over the original configuration.</p>
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<p>The in situ configuration of Geometry 2.</p>
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<p>The Factor of Safety for the slope related to the in-situ configuration of Geometry #2.</p>
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<p>The achievement of an enhanced Factor of Safety (FOS) resulting from the threefold redesign of the slope in comparison to its original configuration.</p>
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<p>The configuration of Geometry # 3 as observed on site.</p>
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<p>The Factor of Safety for the slope associated with Geometry #3.</p>
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<p>The enhanced Factor of Safety (FOS) attained following the threefold redesign of the slope in contrast to its initial configuration.</p>
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<p>An overview of the essential factors contributing to slope instability in a stone-cutting region.</p>
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18 pages, 16746 KiB  
Article
Estimation Model of Rockfall Trajectory Lateral Dispersion on Slopes with Loose Granular Cushion Layer Based on Three-Dimensional Discrete Element Method Simulations
by Tingbin Qian, Wei Luo, Baojing Zheng, Yixin Feng and Desheng Yin
Processes 2024, 12(12), 2788; https://doi.org/10.3390/pr12122788 - 6 Dec 2024
Viewed by 611
Abstract
Rockfall is a typical successive hazard with a high incidence rate following primary geological disasters such as landslides, rock avalanches, and debris flows. The lateral dispersion of rockfall is significantly affected by the loose granular cushion layer deposited on slopes. This paper aims [...] Read more.
Rockfall is a typical successive hazard with a high incidence rate following primary geological disasters such as landslides, rock avalanches, and debris flows. The lateral dispersion of rockfall is significantly affected by the loose granular cushion layer deposited on slopes. This paper aims to develop a quick estimation model for this effect based on the 3D-DEM (discrete element method) numerical simulations. The DEM model employs particles with different bonding properties to create a modeling double-layer granular slope. The present model is also verified by comparing the data from the antecedent large-scale outdoor rockfall experiment with the numerical simulations. Accordingly, the influences of four factors: the initial horizontal release velocity, the size of the rock mass, the granular cushion thickness, and the slope angle on the lateral dispersion of the rockfall trajectory are analyzed, and the underlying physical mechanism is discussed thoroughly. Ultimately, we identify a nondimensional parameter that demonstrates a strong correlation with the evolution of the lateral dispersion ratio of the rockfall trajectory. Based on this insight, we propose an estimation model for predicting the lateral dispersion of the rockfall trajectory. This model can assist engineering and construction personnel in rapidly determining the lateral dispersion range of the rockfall. Full article
(This article belongs to the Section Particle Processes)
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<p>The Hertz–Mindlin (no slip) contact model schematic.</p>
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<p>Schematic diagram of parallel bonding bonds.</p>
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<p>The DEM model diagram of the double granular layer slope rockfall and different motion state diagram of spherical rockfall in the simulation.</p>
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<p>Rockfall blocks of different shapes built in the DEM model. (<b>a</b>) Cuboid; (<b>b</b>) cube; and (<b>c</b>) regular octahedron.</p>
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<p>(<b>a</b>) Lateral deviations and resting positions at the slope foot; (<b>b</b>) slope model constructed in the DEM model (refer to experimental slope B).</p>
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<p>Rockfall motion characteristics on slope B: (<b>a</b>) lateral deviation−time curves; (<b>b</b>) kinetic energy−time curves; (<b>c</b>) jumping heights; and (<b>d</b>) resting positions at the slope toe.</p>
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<p>The effect of static friction coefficients on the velocity magnitude vs. the time curves of rockfall under different initial horizontal release velocities. (<b>a</b>) The initial horizontal velocity is 1 m/s; (<b>b</b>) The initial horizontal velocity is 4 m/s; (<b>c</b>) The initial horizontal velocity is 8 m/s.</p>
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<p>The effect of rolling friction coefficients on the velocity magnitude vs. the time curves of rockfall under different initial horizontal release velocities. (<b>a</b>) The initial horizontal velocity is 1 m/s; (<b>b</b>) The initial horizontal velocity is 4 m/s; (<b>c</b>) The initial horizontal velocity is 8 m/s.</p>
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<p>The effect of recovery coefficients on the velocity magnitude vs. the time curves of rockfall under different initial horizontal release velocities. (<b>a</b>) The initial horizontal velocity is 1 m/s; (<b>b</b>) The initial horizontal velocity is 4 m/s; (<b>c</b>) The initial horizontal velocity is 8 m/s.</p>
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<p>(<b>a</b>) Typical slope diagram (front view and side view) of Azzoni et al. [<a href="#B28-processes-12-02788" class="html-bibr">28</a>] for rockfall experiment; (<b>b</b>) schematic diagram of typical double granular layer slope rockfall after disaster (front view and side view).</p>
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<p>(<b>a</b>) Top view of the rockfall motion trajectory on the double granular layer slope simulated under different horizontal velocity conditions; (<b>b</b>) the mean value, standard deviation, maximum value, minimum value, and the change curve of the mean value of the rockfall lateral dispersion ratio.</p>
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<p>(<b>a</b>) Top view of the double granular layer slope rockfall motion trajectory simulated under different granular cushion thickness; (<b>b</b>) the mean value, standard deviation, maximum value, minimum value, and the change curve of the mean value of the rockfall lateral dispersion ratio.</p>
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<p>(<b>a</b>) Top view of the double granular layer slope rockfall motion trajectory simulated under different block sizes; (<b>b</b>) the mean value, standard deviation, maximum value, minimum value, and the change curve of the mean value of the double granular layer slope rockfall lateral dispersion ratio.</p>
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<p>(<b>a</b>) Top view of the double granular layer slope rockfall motion trajectory simulated under different slope angles; (<b>b</b>) the mean value, standard deviation, maximum value, minimum value, and the change curve of the mean value of the double granular layer slope rockfall lateral dispersion ratio.</p>
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<p>Scatter diagram, fitting curve, and envelope curve of the relation between the lateral dispersion ratio and <span class="html-italic">ξ</span>.</p>
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<p>The rockfall movement is divided into two stages, of which the first stage is the rockfall movement from the source area of the dangerous rock mass to the double granular layer slope, and the second stage is the rockfall movement on the double granular layer slope.</p>
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15 pages, 3509 KiB  
Article
Damage Characteristics of Blasting Surrounding Rock in Mountain Tunnel in Fault Fracture Zones Based on the Johnson–Holmquist-2 Model
by Lizhi Cheng, Zhiquan Yang, Ping Zhao and Fengting Li
Buildings 2024, 14(11), 3682; https://doi.org/10.3390/buildings14113682 - 19 Nov 2024
Viewed by 776
Abstract
Blasting is a widely employed technique for tunnel construction in mountainous regions; however, it often causes damage to the surrounding rock mass, particularly in fault fracture zones, which can lead to hazards such as rockfalls and collapses. This study examines the characteristics of [...] Read more.
Blasting is a widely employed technique for tunnel construction in mountainous regions; however, it often causes damage to the surrounding rock mass, particularly in fault fracture zones, which can lead to hazards such as rockfalls and collapses. This study examines the characteristics of damage to surrounding rock due to tunnel blasting through fault fracture zones. Based on an actual tunnel blasting construction project, we conducted a finite element analysis using the JH-2 material model, taking into account the width of the fault fracture zone. Results indicate that as the width of the fault fracture zone increases, the disturbance effect of tunnel blasting on the surrounding rock becomes more pronounced. Compared to the arch bottom and arch waist of the tunnel, the tunnel vault primarily absorbs the slip deformation and compressive forces resulting from blasting disturbances in the fault fracture zone. The findings of this paper contribute a valuable methodology for analyzing the mechanical mechanisms in mountain tunnel blasting and provide essential theoretical parameters to inform the design and construction of tunnel blasting projects. Full article
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<p>Schematic diagram of the distribution of fault fracture zones of Lianfeng Tunnel.</p>
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<p>JH-2 material intrinsic model’s strength model.</p>
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<p>Numerical calculation model.</p>
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<p>Stress distribution of surrounding rock induced by tunnel blasting in fracture area.</p>
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<p>Stress distribution of surrounding rock induced by tunnel blasting in fracture area.</p>
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<p>Strain distribution of surrounding rock induced by tunnel blasting in fracture area.</p>
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<p>Strain distribution of surrounding rock induced by tunnel blasting in fracture area.</p>
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<p>Shear stress cloud of X-Y section.</p>
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<p>Stress diagram of lining structure.</p>
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<p>Circumferential strain curves of the tunnel blast lining structure with the distance of 1 m, 10 m, and 20 m from the palm face under the condition of 20 m fault width.</p>
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<p>Characteristics of damage and destruction of the vault caused by tunnel blasting under the condition of fault width of 20 m.</p>
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16 pages, 32403 KiB  
Article
Integrated Analysis of Rockfalls and Floods in the Jiului Gorge, Romania: Impacts on Road and Rail Traffic
by Marian Puie and Bogdan-Andrei Mihai
Appl. Sci. 2024, 14(22), 10270; https://doi.org/10.3390/app142210270 - 8 Nov 2024
Viewed by 1110
Abstract
This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed [...] Read more.
This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed susceptibility to both rockfalls and floods. The primary aim was to enhance public safety for traffic participants by providing accurate hazard mapping. Our study focuses on the area from Bumbești-Jiu to Petroșani, traversing the Southern Carpathians. The results demonstrate the utility of integrating remote sensing with machine learning to improve hazard management and inform more effective traffic planning. These findings contribute to safer, more resilient infrastructure in areas vulnerable to natural hazards. Full article
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<p>Geographical location of the study area.</p>
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<p>Sentinel 1 GRD images of study area from descending orbit (left, 20 January 2023), from ascending orbit (middle, 20 January 2023), RGB interferogram and processing software workflow.</p>
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<p>ESA SNAP software workflow image samples for rockfall detection from Sentinel-1 SLC product.</p>
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<p>Flood map of the Jiului Gorge region, illustrating the extent and severity of flood events based on Sentinel-1 GRD images.</p>
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<p>Rockfall map displaying incidents along National Road 66 and surrounding slopes for specified dates.</p>
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<p>Rockfall susceptibility map showing areas highly susceptible to rockfall, with a notable prevalence in the upper section of the gorge.</p>
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<p>Rockfall susceptibility map combined with affected areas from radar images, highlighting the upper part of the gorge with high susceptibility.</p>
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<p>Floods susceptibility map combining DEM-derived slope classifications, land cover types, rainfall, and proximity to water bodies, showing higher susceptibility in wider parts of the gorge.</p>
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<p>Floods susceptibility map combined with radar-detected flood areas, illustrating increased susceptibility in the central and southern parts of the gorge.</p>
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<p>Train detection and recognition using YOLO models, illustrating detection from a significant distance with reduced visibility.</p>
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<p>Road traffic element detection with greater precision due to closer camera proximity.</p>
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<p>Training results from YOLOv9 model, showcasing classes obtained after training.</p>
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<p>Detection results including several classes, highlighting various rockfall types.</p>
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<p>Detection results focusing on a single class, illustrating detailed rockfall identification.</p>
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31 pages, 114861 KiB  
Article
Multitemporal Monitoring of Rocky Walls Using Robotic Total Station Surveying and Persistent Scatterer Interferometry
by Luisa Beltramone, Andrea Rindinella, Claudio Vanneschi and Riccardo Salvini
Remote Sens. 2024, 16(20), 3848; https://doi.org/10.3390/rs16203848 - 16 Oct 2024
Viewed by 1073
Abstract
Rockfall phenomena are considered highly dangerous due to their rapid evolution and difficult prediction without applying preventive monitoring and mitigation actions. This research investigates a hazardous site in the Municipality of Vecchiano (Province of Pisa, Italy), characterized by vertical rock walls prone to [...] Read more.
Rockfall phenomena are considered highly dangerous due to their rapid evolution and difficult prediction without applying preventive monitoring and mitigation actions. This research investigates a hazardous site in the Municipality of Vecchiano (Province of Pisa, Italy), characterized by vertical rock walls prone to instability due to heavy fracturing and karst phenomena. The presence of anthropical structures and a public road at the bottom of the slopes increases the vulnerability of the site and the site’s risk. To create a comprehensive geological model of the area, Unmanned Aircraft System (UAS) photogrammetric surveys were conducted to create a 3D model useful in photointerpretation. In accessible and safe areas for personnel, engineering–geological surveys were carried out to characterize the rock mass and to define the portion of rock walls to be monitored. Results from nine multitemporal Robotic Total Station (RTS) measurement campaigns show that no monitoring prisms recorded significant displacement trends, both on the horizontal and vertical plane and in differential slope distance. Additionally, satellite Persistent Scatterer Interferometry (PSI) analysis indicates that the slopes were stable over the two years of study. The integration of these analysis techniques has proven to be an efficient solution for assessing slope stability in this specific rockfall-prone area. Full article
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<p>Geological framework of the study site. (<b>A</b>) The site location in Italy; (<b>B</b>) the regional geological framework (Sheet n.273 “Pisa”) modified from [<a href="#B54-remotesensing-16-03848" class="html-bibr">54</a>]; (<b>C</b>) a subset of the geological map n.273 “Pisa” [<a href="#B54-remotesensing-16-03848" class="html-bibr">54</a>]; the red star in (<b>C</b>) indicates the precise location of the study area.</p>
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<p>Overall methodology flowchart.</p>
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<p>Stages of the GNSS (<b>A</b>) and RTS surveys (<b>B</b>).</p>
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<p>Photos in (<b>A</b>,<b>B</b>) show some examples of outcrops selected for the in situ engineering–geological survey; the yellow dashed lines in (<b>C</b>) indicate the location of scanlines along the slope; the red boxes in (<b>C</b>) show the areas where discontinuity sampling was performed through CloudCompare software (version 2).</p>
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<p>Robotic Total Station (RTS) on the iron plate anchored to a reinforced concrete curb (<b>A</b>); view of the rock walls to be monitored from the RTS position (<b>B</b>); macroprism and microprism utilized for the RTS multitemporal surveys (<b>C</b>).</p>
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<p>Location of RTS base (in yellow) and prisms; green colors indicate the reference prisms utilized to orient the monitoring system; red colors indicate the monitoring prisms periodically measured during the nine surveys.</p>
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<p>Example of error ellipse for the B4 monitoring prism.</p>
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<p>Metallic corner reflectors installed on the top edge above the rocky walls: location of corner reflectors in the study area (<b>A</b>). Photos in <b>CR1</b>, <b>CR2,</b> and <b>CR3</b> show detailed images of the corner reflectors.</p>
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<p>Satellite imagery covering the area of interest (<b>A</b>); satellite imagery spatially corresponding to the same area of interest (<b>B</b>). The yellow square shows the area of interest.</p>
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<p>Perspective view of the georeferenced and scaled 3D point cloud of the rocky walls; the scale bar only applies to (<b>A</b>). Georeferenced orthophotomosaic of the rocky walls and the alluvial plain (<b>B</b>).</p>
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<p>Stereographic projection (Schmidt equal-area method—lower hemisphere) of data collected during the in situ engineering–geological survey.</p>
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<p>Stereographic projection (Schmidt equal-area method—lower hemisphere) of data interpreted on the 3D point cloud.</p>
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<p>Slopes considered for the application of the SMR method and the following statistical kinematic stability analysis (<a href="#sec4dot4-remotesensing-16-03848" class="html-sec">Section 4.4</a>). The stereographic projections (Wulff equal-angle method—lower hemisphere) show an example of the executed kinematic analysis (ex., wedge sliding).</p>
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<p>Planimetric representation of multitemporal monitoring results (monitoring prisms from B1 to B15). The error ellipses for the monitoring prisms are indicated in orange. The points, differentiated by color, indicate the 9 multitemporal surveys.</p>
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<p>Planimetric representation of multitemporal monitoring results (monitoring prisms from B16 to B30). The error ellipses for the monitoring prisms are indicated in orange. The points, differentiated by color, indicate the 9 multitemporal surveys.</p>
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<p>Differential slope distance (<b>A</b>) and elevation displacement (<b>B</b>) of each prism measured in all the RTS surveys. The uncertainty thresholds for each prism are indicated by the red vertical bars.</p>
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<p>Differential slope distance (<b>A</b>) and elevation displacement (<b>B</b>) of each prism as computed with respect to R4. The uncertainty thresholds for each prism are indicated by the red vertical bars.</p>
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<p>Results of the PSI analysis in terms of LOS velocities (mm/yr) for the Sentinel-1A (<b>A</b>) and Sentinel-1B data (<b>B</b>). The yellow squares represent the position of the artificial corner reflectors installed at the top of the rocky slopes in this work. The blue triangle indicates the building where the RTS is installed.</p>
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<p>PSs identified by the regional LaMMa interferometric service for the study area. The red circle identifies the point FV6XKKY located near the RTS. The diagram at the bottom of the map shows the trend of this PS LOS velocity (mm/yr) considering the same interval of the RTS monitoring time span. The light blue ellipses indicate the acquisition dates of the 7th and 8th RTS surveys.</p>
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<p>Differential slope distance (<b>A</b>) and elevation displacement (<b>B</b>) of each prism as measured during the survey carried out on 4 March 2024.</p>
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30 pages, 6012 KiB  
Article
A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru
by Cassidy L. Grady, Paul M. Santi, Gabriel Walton, Carlos Luza, Guido Salas, Pablo Meza and Segundo Percy Colque Riega
Remote Sens. 2024, 16(19), 3732; https://doi.org/10.3390/rs16193732 - 8 Oct 2024
Viewed by 1187
Abstract
Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the [...] Read more.
Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R2 value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R2 of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts. Full article
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<p>A map of the Arequipa region of Peru with the locations of the three study sites, Chaparra, Aplao, and Chivay, indicated by red circles. The validation site of Achoma is indicated by a blue square.</p>
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<p>Photographs of the three sites and the validation site, Achoma. These photos were taken by the first author, except for that of Chaparra, which was taken by a member of the research team.</p>
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<p>Part of the rockfall inventory for Aplao showing marked rocks, runout zones, and source zones.</p>
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<p>Methodology flowchart for creating a rockfall hazard map split into “generation” and “transport” processes to determine source zone and runout zone hazard ratings that are input into the final hazard map.</p>
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<p>Schematic of how the program identifies the bottom of a source zone with multiple source zone polygons. The red and green arrows are bearing lines extending in the direction of slope aspect from each source zone boundary point. The red arrows indicate bearing lines that have more than one intersection, and their origin points are therefore not considered the bottom of the source zone points. The green arrows indicate bearing lines that only have one intersection point and their origin points are considered the bottom of the source zone points, which are represented by the yellow circles. The cyan line that connects the yellow circles is the bottom of the source zone line that would result from using the “Points to Line” tool in ArcGIS Pro. This schematic does not show every bearing line that would be created to maintain the simplicity of the diagram.</p>
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<p>Drawings of joint orientation and continuity types included in <a href="#remotesensing-16-03732-t003" class="html-table">Table 3</a>.</p>
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<p>Schematic of the runout zone hazard based on Equation (2) where for each cell in the runout zone, the source score is multiplied by the corresponding rockfall passing percentage and the sum of rockfall trajectory paths, which results in the final scores zoned into high, medium, and low categories.</p>
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<p>Percentage of rockfall passing a distance from the bottom of the source zone for the sites of Aplao (blue), Chaparra (orange), and Chivay (gray), with an inset (green box) showing the sparseness of data points in the distances that are farther from the source zone.</p>
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<p>Rockfall hazard map for Chivay showing the area of interest that encompasses the source zone (purple) and runout zone hazard ratings of low (yellow), medium (orange), and high (red).</p>
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<p>Rockfall hazard map for Aplao showing the area of interest that encompasses the source zone (purple) and runout zone hazard ratings of low (yellow), medium (orange), and high (red).</p>
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<p>Map of Aplao showing rockfall inventory density compared to the runout zone hazard, which is depicted as yellow (low), orange (medium), and red (high) lines that indicate the extent of each runout zone.</p>
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<p>Rockfall hazard map for Achoma that incorporates important community landmarks (the Colca River and the Majes Canal), as well as a line with teeth for the top of the source zone to indicate which direction is downslope.</p>
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22 pages, 19530 KiB  
Article
Cascading Landslide: Kinematic and Finite Element Method Analysis through Remote Sensing Techniques
by Claudia Zito, Massimo Mangifesta, Mirko Francioni, Luigi Guerriero, Diego Di Martire, Domenico Calcaterra and Nicola Sciarra
Remote Sens. 2024, 16(18), 3423; https://doi.org/10.3390/rs16183423 - 14 Sep 2024
Cited by 1 | Viewed by 1183
Abstract
Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the [...] Read more.
Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated. Full article
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<p>Aerial and field images of the Morino-Rendinara landslide that are representative of the impact of the landslide on the environment. (<b>a</b>) Overview of phenomenon taken from Google Earth [<a href="#B16-remotesensing-16-03423" class="html-bibr">16</a>] satellite images of 13 June 2022, from the upper sector near Morino Hamlet to the lower sector, Liri River, and deep-seated rotational slide; (<b>b</b>) Details of rockfall/avalanches sector; (<b>c</b>) Debris flow source area; (<b>d</b>) Debris flow transit zone; (<b>e</b>) Lowest debris flow transit zone; (<b>f</b>) Liri River dam; and (<b>g</b>) Effect on Liri River dam.</p>
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<p>Geographical location of Morino-Rendinara. Green lines indicate the regional boundaries; red lines indicate the municipality of Morino, Castronovo, and San Vincenzo Valle Roveto composing the involved municipality; the light blue square indicates the landslide and the study area.</p>
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<p>Geological map extract from the CARG (Geological CARtography Map n.220 Sora) Project [<a href="#B22-remotesensing-16-03423" class="html-bibr">22</a>], with indications of the geological formations and tectonic processes present in the area.</p>
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<p>Maps of the survey and debritic cover layer reconstruction using cross-sections to empathize the heterogeneity of deposits covering the substrate. (a) The section develops on maximum slope line. (b) The section develops on perpendicular direction.</p>
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<p>Conceptual flow chart of the work phases.</p>
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<p>Spatiotemporal baseline map of SBAS-InSAR interferometric data of ascending track (<b>a</b>) and descending track (<b>b</b>).</p>
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<p>The inventory map was drawn using the results of the study. The image identifies three main mechanisms: a rockfall in the upper part, a deep-seated rotational slide in the central part, and a debris flow in the lower part.</p>
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<p>Details of the landslide inventory map. The various fillings show the different landslide types identified in the study area: the rockfall in the upper part, the deep-seated rotational slide in the central part, and the debris flow in the lower part of the slope.</p>
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<p>PS velocity along the ascending (<b>a</b>) and descending (<b>b</b>) geometries from 2020 to 2023. Red dots indicate major velocity trends and instability, green and blue dots indicate minor velocity and stable sectors.</p>
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<p>Selected time series in ascending geometry (<b>a</b>) and descending geometry (<b>b</b>). The analyzed time series illustrates a very unstable sector represented by reflectors P106_60-61 and 102_57 in ascending geometry and P_70_141-141-136 in descending geometry. Additionally, some stable sectors are represented, such as P_83_135-143, P_85_68, and P_86_69.</p>
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<p>Time series vs. rainfall analyses. (<b>a</b>) Monthly cumulative rainfall for analysis period vs. one ascending and descending representative time series; (<b>b</b>) Daily cumulative rainfall for analysis period vs. one ascending and descending representative time series; (<b>c</b>) Cumulative rainfall for analysis period vs. one ascending and descending representative time series.</p>
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<p>Shear strain index results of the 2D numerical modelling with different pore pressure conditions. (<b>a</b>) Analysis without pore pressure; (<b>b</b>) Analysis with water table 0.5 m from ground level; (<b>c</b>) Analysis with water table 2.0 m from ground level; (<b>d</b>) Analysis with water table 6.0 m from ground level.</p>
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18 pages, 7423 KiB  
Article
Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
by Kieu Anh Nguyen, Yi-Jia Jiang, Chiao-Shin Huang, Meng-Hsun Kuo and Walter Chen
Future Internet 2024, 16(8), 299; https://doi.org/10.3390/fi16080299 - 21 Aug 2024
Viewed by 1146
Abstract
Over three-quarters of Taiwan’s landmass consists of mountainous slopes with steep gradients, leading to frequent rockfall hazards that obstruct traffic and cause injuries and fatalities. This study used Google Alerts to compile internet news on rockfall incidents along Taiwan’s highway system from April [...] Read more.
Over three-quarters of Taiwan’s landmass consists of mountainous slopes with steep gradients, leading to frequent rockfall hazards that obstruct traffic and cause injuries and fatalities. This study used Google Alerts to compile internet news on rockfall incidents along Taiwan’s highway system from April 2019 to February 2024. The locations of these rockfalls were geolocated using Google Earth and integrated with geographical, topographical, environmental, geological, and socioeconomic variables. Employing machine learning algorithms, particularly the Random Forest algorithm, we analyzed the potential for rockfall hazards along roadside slopes. The model achieved an overall accuracy of 0.8514 on the test dataset, with a sensitivity of 0.8378, correctly identifying 83.8% of rockfall locations. Shapley Additive Explanations (SHAP) analysis highlighted that factors such as slope angle and distance to geologically sensitive areas are pivotal in determining rockfall locations. The study underscores the utility of internet-based data collection in providing comprehensive coverage of Taiwan’s highway system, and enabled the first broad analysis of rockfall hazard susceptibility for the entire highway network. The consistent importance of topographical and geographical features suggests that integrating detailed spatial data could further enhance predictive performance. The combined use of Random Forest and SHAP analyses offers a robust framework for understanding and improving predictive models, aiding in the development of effective strategies for risk management and mitigation in rockfall-prone areas, ultimately contributing to safer and more reliable transportation networks in mountainous regions. Full article
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<p>The highway system in Taiwan, highlighting the locations of rockfall and non-rockfall events.</p>
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<p>Predictive variables used for rockfall analysis: (<b>a</b>) elevation, (<b>b</b>) slope angle in degrees, (<b>c</b>) NDVI, (<b>d</b>) river system, (<b>e</b>) urban areas, (<b>f</b>) fault lines, (<b>g</b>) geologically sensitive areas, (<b>h</b>) potential debris flow areas, (<b>i</b>) average yearly rainfall, (<b>j</b>) average monthly temperature, (<b>k</b>) average monthly wind speed, (<b>l</b>) average monthly humidity, (<b>m</b>) average number of rainy days, (<b>n</b>) strata, (<b>o</b>) epoch, (<b>p</b>) population density.</p>
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<p>Predictive variables used for rockfall analysis: (<b>a</b>) elevation, (<b>b</b>) slope angle in degrees, (<b>c</b>) NDVI, (<b>d</b>) river system, (<b>e</b>) urban areas, (<b>f</b>) fault lines, (<b>g</b>) geologically sensitive areas, (<b>h</b>) potential debris flow areas, (<b>i</b>) average yearly rainfall, (<b>j</b>) average monthly temperature, (<b>k</b>) average monthly wind speed, (<b>l</b>) average monthly humidity, (<b>m</b>) average number of rainy days, (<b>n</b>) strata, (<b>o</b>) epoch, (<b>p</b>) population density.</p>
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<p>The ROC curve of the Random Forest model, highlighting the AUC value of 0.9317, which indicates excellent model performance.</p>
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<p>Rockfall susceptibility map of a section of Provincial Highway 7, showing the classification into five susceptibility levels. Black dots indicate the locations of past rockfalls.</p>
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<p>Variable importance derived from the Random Forest model, highlighting the significant predictors across geographical, topographical, environmental, geological, and socioeconomic categories.</p>
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<p>Mean SHAP values indicating the average impact of each feature on model predictions. Features are ranked by importance, with <tt>slope_deg</tt> and <tt>dist_geo_sens_area</tt> showing the highest impact.</p>
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<p>SHAP values illustrating the impact of each feature on the model’s output. Red dots represent high feature values, while blue dots represent low feature values.</p>
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21 pages, 34311 KiB  
Case Report
Drone-Borne LiDAR and Photogrammetry Together with Historical Data for Studying a Paleo-Landslide Reactivated by Road-Cutting and Barrier Construction outside Jerusalem
by Yaniv Darvasi, Ben Laugomer, Ido Shicht, John K. Hall, Eli Ram and Amotz Agnon
Geotechnics 2024, 4(3), 786-806; https://doi.org/10.3390/geotechnics4030041 - 9 Aug 2024
Viewed by 981
Abstract
Assessment of landslide hazards often depends on the ability to track possible changes in natural slopes. To that end, historical air photos can be useful, particularly when slope stability is compromised by visible cracking. Undocumented landsliding rejuvenates a paleo-landslide on a busy motorway [...] Read more.
Assessment of landslide hazards often depends on the ability to track possible changes in natural slopes. To that end, historical air photos can be useful, particularly when slope stability is compromised by visible cracking. Undocumented landsliding rejuvenates a paleo-landslide on a busy motorway connecting Jerusalem to a small Jewish settlement. Recently, a plan for broadening the motorway was approved, and we were asked to study the hazards of the road by Israeli NGOs and Palestinian residents of the area. We captured high-resolution topography around the unstable slope using drone-borne photogrammetry and LiDAR surveys. The modern data allow us to analyze historic air photos and topo maps to assess the level of sliding prior to and during modern landscaping. Our results indicate horizontal offsets of ~0.9–1.8 m and vertical offsets of 1.54–2.95 m at selected sites. We next assess the possible role of anthropogenic versus natural factors in compromising slope stability. We analyze monthly rain records together with seismic catalogs spanning several decades. Shortly after the motorway construction in 1995, a January 1996 rainstorm triggered a massive rockfall. The rockfall blocked traffic with up to 4 m-diameter boulders. We found that while a certain level of rain is a necessary condition for mobilizing the rock mass, it is the anthropogenic intervention that caused the rockfall in this site. We conclude that the recent plan for broadening the motorway jeopardizes the lives of vehicle passengers and the lives of future residents should the development materialize. Full article
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<p>Location map (the source of the two upper maps is Open Topo Map). The aerial photo (bottom) was taken by a drone, and the map was compiled with QGIS software 3.22).</p>
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<p>The rockfall of 24 January 1996, shortly after Motorway 385 was completed. (<b>A</b>) From a news release on the daily Yedi’ot A’hronot; (<b>B</b>) slightly later, after the 3 m-wide boulder in the foreground (rolled through the protective concrete wall) was shattered for removal (photo by Yossi Leshem). The newspaper (from the day following the fall) noted that a real disaster was prevented when a four-metric-ton rock rolled onto the road, as traffic had been stopped earlier when smaller rocks fell; residents and Society for Nature Protection people had warned that, due to the haste to evacuate Bethlehem, the road was poorly constructed and was dangerous for traffic.</p>
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<p>1:50,000 Geological map [<a href="#B4-geotechnics-04-00041" class="html-bibr">4</a>]. Stratigraphic and lithological symbols are given in <a href="#geotechnics-04-00041-f004" class="html-fig">Figure 4</a>. The blue quadrangle demarcates the slide site. Motorway 385 is red. White circles mark rock fall blocks mapped by Katz et al., 2011 [<a href="#B1-geotechnics-04-00041" class="html-bibr">1</a>]. the inscriptions in Hebrew symbolize ancient sites, springs and streams.</p>
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<p>Stratigraphy [<a href="#B4-geotechnics-04-00041" class="html-bibr">4</a>].</p>
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<p>(<b>A</b>). A LiDAR point cloud of an open crevasse with depth reaching up to ~6.5 m. (<b>B</b>,<b>C</b>) An open crack in a dirt road, cut for installing a barrier around 2012; (<b>B</b>) A person halfway within the crevasse. (<b>C</b>) Zoom-in with a 0.4 m geological pick for scale. Notice the paleo-boulder within the breccia rock mass, split open by the new crevasse (photos taken 15 December 2020).</p>
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<p>LiDAR sensors: (<b>Left</b>), GeoSlam Zeb Horizon LiDAR system mounted on a Matrice 600 Pro UAV. (<b>Right</b>), DJI L1 LiDAR system mounted on a Matrice 300 RTK UAV.</p>
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<p>Orthophotos since 1970. The 1970 photo shows mild terracing, a traditional method practiced by farmers for soil accumulation and preservation (e.g., [<a href="#B6-geotechnics-04-00041" class="html-bibr">6</a>]).</p>
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<p>Contour maps: The yellow ellipse marks the area where the slide has modified the shapes of the contour map. (<b>A</b>) Contours traced from British Mandate (pre-1948) maps by Hall (2008) (25 m DTM) [<a href="#B5-geotechnics-04-00041" class="html-bibr">5</a>], superimposed on an orthophoto acquired by the drone. (<b>B</b>,<b>C</b>) are from <a href="https://amudanan.co.il/" target="_blank">https://amudanan.co.il/</a> (1:50,000). Contours in the more recent map (<b>C</b>) show inverted concavity where the slide intercepted the surface. This concavity controlled by the landslide head scarp is more pronounced on the 5 m contour map of the Israel Mapping Center.</p>
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<p>Orthophoto with the five main areas where movements have been recognized.</p>
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<p>LiDAR 2021/2022 comparison of areas 1 and 2 indicate the difference in topography (location in <a href="#geotechnics-04-00041-f009" class="html-fig">Figure 9</a>).</p>
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<p>Photogrammetry 2021/2022 comparison of main differences (location in <a href="#geotechnics-04-00041-f009" class="html-fig">Figure 9</a>).</p>
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<p>LiDAR 2021/2022 comparison of area 3 indicates the difference in topography (location in <a href="#geotechnics-04-00041-f009" class="html-fig">Figure 9</a>).</p>
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<p>LiDAR 2021/2022 comparison of area 5 indicates the difference in topography (location in <a href="#geotechnics-04-00041-f009" class="html-fig">Figure 9</a>).</p>
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<p>The amount of monthly rain (only for rainy months) measured in Jerusalem between 1967 and 2023. Median, average, and average + standard deviation are marked green, orange, and violet, respectively. The storm preceding the rockfall is outlined (Jan 1996, 157.5 mm). While this storm was significant, it was far from unusual (as compared to the Pinatubo winter marked—1991/2). We suggest that the combination of the storm together with the landscaping and road cutting for Motorway 385, with possible contribution of a regional earthquake, triggered the rock fall. Data from Israel Meteorological Service (IMS).</p>
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<p>Annual average of rain measured in Jerusalem. The winter of 1995/6 was slightly below average. Also shown (top) are the years for which we present air photos (<a href="#geotechnics-04-00041-f007" class="html-fig">Figure 7</a>). Data from Israel Meteorological Service (IMS).</p>
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<p>Epicenters of earthquakes with a magnitude larger than 5.0 around the study site (yellow star) between 1900 and 2023. The largest earthquake (M7.2) hit the Gulf of Aqaba on 22 November 1995. Data from the Geological Survey of Israel <a href="https://eq.gsi.gov.il/earthquake" target="_blank">https://eq.gsi.gov.il/earthquake</a> (accessed on 2 March 2024).</p>
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<p>(<b>Left</b>): A portion of a map (1:50,000) marking intermediate potential for ground amplification during earthquakes [<a href="#B10-geotechnics-04-00041" class="html-bibr">10</a>], with the landslide site marked by a blue star. The potential was calculated based on slope and lithology. The semi-circular band about the site is formed by the marly-clayey Motza Formation (<a href="#geotechnics-04-00041-f004" class="html-fig">Figure 4</a>). The blue square and star, respectively, mark the sloping hill and the slide. (<b>Right</b>): A portion of the geological map (<a href="#geotechnics-04-00041-f003" class="html-fig">Figure 3</a>) where the blue square is marked for ease of orientation between panels. The inscriptions in Hebrew symbolize ancient sites, springs and streams.</p>
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<p>A traditional agricultural installation carved in the carbonate cemented breccia (likely an olive or grape press), Coord. 31.731274/35.148377. A crack is cutting through the stone-carved installation (yellow ellipse). We suggest that this is a part of the ongoing cracking triggered by the landscaping related to Motorway 385 and the pedestrian barrier.</p>
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<p>Field photos.</p>
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