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Frequent rockfall events pose a major threat to the safe operation of the Taihang Grand Canyon Scenic Area (GCSA) in China. The traditional techniques for identifying potential rockfall sources and hazard assessment methods are often challenged in the alpine canyon landform. This study aims to establish an early identification framework for regional potential rockfall sources applicable to the canyon region and to assess rockfall hazards in potentially hazardous areas using unmanned aerial vehicle (UAV) photogrammetry. Specifically, by incorporating high-precision topographic information and geotechnical properties, the slope angle distribution method was used for static identification of potential rockfall sources. Moreover, SBAS-InSAR technology was used to describe the activity of potential rockfall sources. Finally, taking the key potentially hazardous area of the Sky City scenic spot as an example, the Rockfall Analyst tool was used to analyze the rockfall frequency, bounce height and energy characteristics based on the high-precision UAV 3D real scene model, and the analytic hierarchy process was introduced to achieve quantitative rockfall hazard assessment. The results show that the potential rockfall source areas in the Taihang GCSA is 33.47 km2 (21.47%), mainly distributed in strips on the cliffs on both sides of the canyon, of which the active rockfall source area is 2.96 km2 (8.84%). Taking the scenic spot of Sky City as example, the proposed UAV-based real scene modeling technology was proven to be able to quickly and accurately construct a 3D high-precision model of the canyon area. Moreover, the 3D rockfall simulation showed that the high-energy rockfall area was mainly distributed at the foot of the steep cliff, which mainly threatens the tourist distribution center below. The early identification and quantitative evaluation scheme of rockfall events proposed in this study can provide technical reference for the prevention and control of rockfall hazards in similar alpine valley areas.

The impact-induced rock mass fragmentation in a rockfall is analyzed by comparing the in situ block size distribution (IBSD) of the rock mass detached from the cliff face and the resultant rockfall block size distribution (RBSD) of the rockfall fragments on the slope. The analysis of several inventoried rockfall events suggests that the volumes of the rockfall fragments can be characterized by a power law distribution. We propose the application of a three-parameter rockfall fractal fragmentation model (RFFM) for the transformation of the IBSD into the RBSD. A discrete fracture network model is used to simulate the discontinuity pattern of the detached rock mass and to generate the IBSD. Each block of the IBSD of the detached rock mass is an initiator. A survival rate is included to express the proportion of the unbroken blocks after the impact on the ground surface. The model was calibrated using the volume distribution of a rockfall event in Vilanova de Banat in the Cadí Sierra, Eastern Pyrenees, Spain. The RBSD was obtained directly in the field, by measuring the rock block fragments deposited on the slope. The IBSD and the RBSD were fitted by exponential and power law functions, respectively. The results show that the proposed fractal model can successfully generate the RBSD from the IBSD and indicate the model parameter values for the case study.

Among the types of landslides that occur in Alpine areas, rockfalls are one of the most frequent and dangerous. Rockfalls affect every type of rock mass with greater or lesser incidence based on the intrinsic characteristics of rocks (structural setting, lithotype, geotechnical quality, weathering conditions, etc.) and climate features of geographic areas considered. In order to assess the hazard and subsequently the risk associated with these phenomena, it is first necessary to identify areas most likely to be affected. This paper presents a methodology applicable on a regional scale to identify the most susceptible Alpine areas to rockfalls. The methodology is based on statistical cross-analyses of environmental factors to identify the Alpine areas most susceptible to rockfalls. The predisposing factors considered to generate a rockfall susceptibility map are both qualitative (geological variables) and quantitative (morphometric, altimetric and climatic variables) and have been compared with a selection of historical data on rockfalls occurrence and distribution (consisting in 2800 events). As result, a classification of the Piemonte’s Alpine territory identifying 5 classes characterized by a greater or lesser propensity to generate rockfalls has been obtained. The regional susceptibility map has been subsequently used as basis to identify road networks more exposed to rockfall hazard to obtain a preliminary risk analysis at regional scale.

The presented article attempted to analysis rockfalls susceptibility mapping which is considered as one the most important type of the land-slides with high frequent occurrence. The machine learning based multiple-layer perceptron (MLP) model was used to provide the pridicive model and hazard risk maps for studied area. This artificial neural network (ANN) was received significant success in susceptibility assessment for landslides and rockfall. The predictive model was conducted on a dataset containing 15 main triggering factors which concluded 10 regular (e.g., precipitation, slope aspect, slope angle, weathering, lithology, distance to river, main faults’ unsafe radius, landslide-prone areas, distance to roads, and distance to the cities), 5 specific (e.g., RQD, Qslope, GSI, SMR, and RHRS), and 57 historical landslides which represent different aspects of the rockfall failures at Alborz province in Iran. To susceptibility assessment, the dataset that used in ANN-based analysis was randomly divided into training (70%) and testing (30%) sets and utilized to predict the risk-able area.. The predictive model was comparatively justified by using benchmark learning classifiers concluded support-vector machine (SVM), decision tree (DT), and random forest(RF) algorithms. The confusion matrix and receiver operating characteristic curves (ROC) were used for performance analysis and obtaining models’ accuracy. According to the susceptibility results, the north part of the studied region has high risks for rockfall failures. As machine learning-based performance analysis, the MLP-based model by 0.82 accuracy/0.85 precision reached the highest rank more than other classifiers such asSVM (accuracy = 0.78/precision = 0.78), DT (accuracy = 0.70/precision = 0.73), and RF (accuracy = 0.68/precision = 0.70). Also, overall accuracy obtained from ROC, the main model MLP (AUC = 0.811) is higher than SVM (AUC = 0.780), DT (AUC = 0.740), and RF (AUC = 0.500). MLP model provide more conservative results than other classifier and RF is estimated the risk rate less than others.

Hazard and risk assessment procedures of different types of rockfall were analyzed to compare their outcomes when they are applied to the same case study. Although numerous methodologies are available in literature, rockfall hazard and risk analyses are often limited to standard estimations, affected by a margin of uncertainty, especially when relevant engineering projects are about to be realized. Based on the design purpose, different types of approaches can be chosen among the qualitative and quantitative ones available in literature, which allow different levels of analysis. One of the main criticisms related to rockfall events is the risk affecting linear structures, such as road or railways, due both to their strategic relevance for trade and communications and to the great entity of the exposed value (traffic units) traveling along them. In this perspective, a comparison between the qualitative method known as Evolving Rockfall Hazard Assessment (EHRA), the semi-quantitative modified Rockfall Hazard Rating System (RHRS) and the quantitative Rockfall Risk Management (RoMa) approach is herein commented according to a practical application to a case study. It is the case of the rockfall threat along slopes crossed by a strategic road connecting two of the most known spots of eastern Sicily (Italy), at the Taormina tourist complex. Data were retrieved from both recent literature and technical surveys on field. Achieved results highlight how the approaches are affected by a different level of detail and uncertainty, arising also by some necessary assumption that must be taken into account, especially when mitigation measures or territory planning have to be designed. Achieved results can be also taken into account for similar studies worldwide, in order to choose the most suitable procedure based on the design purpose. This is indeed crucial in the perspective of the optimization of time and economic resources in the territorial planning practice.

Using change detection and semi-automated identification methods, it is possible to extract detailed rockfall information from terrestrial laser scanning data to build a database of events, which can be used in the development of the frequency-magnitude relationship for a slope. In this study, we have applied these methods to the White Canyon, a hazardous slope that presents rockfall hazards to the CN Rail line in British Columbia, to build a database of rockfalls including their locations, volumes, and block shapes. We identified over 1900 rockfall events during a 15-month period, ranging in volume from 0.01 to 45 m 3 . The frequency of these events changed throughout the year, with the highest periods of activity occurring over the winter months. We investigated how the sampling interval, or duration between scans, can affect how the rockfalls are identified, and therefore the frequency-magnitude relationship for the slope using datasets with fewer scans. We show that as the duration between scans becomes larger, fewer rockfalls are detected, as multiple events that have occurred in the same location cluster together into a single event. The results of this study can be used to assist the railways in planning the appropriate number and duration between future scans, in order to capture frequency-magnitude data for the slope with a desired level of detail.

The ancient Kilistra settlement is a natural, historical and cultural heritage site in Central Anatolia (Turkey), which makes it an attractive destination for tourists. However, the settlement located on a hill with steep hillsides has suffered from rockfall events, causing the destruction of some historical buildings. The rockfall risk in the region continues to create a serious danger today for land users and visitors during uncontrolled tourist visits. This paper offers an assessment of rockfall hazard for the ancient Kilistra settlement based on experimental investigation and numerical analyses. For the study, comprehensive field studies were carried out, including the identification of slope profiles, scanline surveys on discontinuities and stability analysis of the slopes. The location and size of the fallen, detached and hanging blocks were also identified. Geomechanical properties of the geological units were determined, and also the rockfall risk rating method was applied for the evaluation of the rockfall hazard risk. Runout distance, bounce height, kinetic energy as well as the velocity of the detached and hanging blocks were determined by using two-dimensional rockfall analyses. Based on the results from the rockfall analyses, possible rockfall-based danger zones have been defined for the ancient Kilistra settlement and its close vicinity. The results of this study point at an immediate necessity for the installation of support systems. Findings of the study also offer preliminary data for the description of risk administration strategies and also provide scientific contribution to the study of the hazard and risk resulting from rockfall phenomena.

Rockfall poses a formidable threat to the ongoing fast-paced construction of large-scale projects in uninhabited areas in high mountain valleys. In this study, an optimization method for arranging passive nets on high and steep slopes was presented to mitigate the threat from rockfalls. This method diverges from the conventional method of subjectively arranging passive nets along the perimeter of protected regions (due to its emphasis on cost considerations), in which the quantitative appraisal of rockfall movement characteristics and interception rates is frequently omitted, consequently failing to comprehensively ensure transportation routes and temporary construction sites. The methodology encompasses the acquisition of terrain data by unmanned aerial vehicles (UAVs), identification of rockfall sources based on UAV point clouds, quantitative assessment of rockfall hazards using a 3D probabilistic model, and optimization of the layout of passive nets based on the assessment results. The aim of the optimization of passive nets is to quantitatively assess the cost–effect relationship of passive nets, accounting for construction feasibility, interception potential, and likelihood of successful rockfall interception. We applied this method to the Feishuiyan slope in southwest China as an example, and the results demonstrated an enhanced interception rate of 99% and cost reduction by a factor of three relative to the original scheme. This innovative approach could enhance rockfall mitigation in high and steep areas, providing a viable strategy for future prevention efforts in these areas.

Rockfalls are one of the most dangerous natural events in hilly terrains, and they substantially threaten residential areas and transport corridors in these environments. This study is aimed to analyze the risk of rockfall from a slope to nearby houses in a historical settlement with past rockfall histories. It contains numerous applications to study rockfall danger from different points of view (e.g., kinematics, numerical stability analysis, risk assessment, 2D trajectory). The rockfall kinematics revealed the statistics for different structurally controlled failure modes among the surveyed slope discontinuities, especially wedge type and block toppling were the most significant ones. Finite element analysis showed that the slope was stable under the natural condition with a safety factor of 2.19. The rockfall risk rating system calculated a medium risk for the houses downstream. Based on the field measurements, a possible rockfall profile was determined and located as an input in the 2D rockfall trajectory program. The rigid-body impact model runs utilized various shapes and sizes of blocks to simulate the rockfall events realistically. According to the 2D trajectory model results, there was no rockfall danger for the investigated downslope houses. The study showed the importance of using different analysis techniques to solve rockfall risk in protected areas based on scientific and rational approaches.

Proximity remote sensing techniques, both land- and drone-based, allow for a significant improvement of the quality and quantity of raw data employed in the analysis of rockfall phenomena. In particular, the large amount of data these techniques can provide allows for the use of probabilistic approaches to rock mass characterization, with particular reference to block volume and shape definition. These, in return, are key parameters required for a proper rockfall hazard assessment and the optimization of countermeasures design. This study aims at providing a sort of guide, starting from the data gathering phase to the processing, up to the implementation of the outputs in a probabilistic-based scenario, which is able to associate a probability of not being exceeded with total kinetic energy values. By doing so, we were able to introduce a new approach for the choice of design parameters and the evaluation of the effectiveness of mitigation techniques. For this purpose, a suitable case study located in Varaita Valley (Cuneo, Italy) has been selected. The area has been surveyed, and a model of the slope and a digital model of the rock faces have been defined. The results show that a 6.5 m3 block has a probability of not being exceeded of 75%; subsequent simulations show that the level of kinetic energy involved in such a rockfall is extremely high. Some mitigation techniques are discussed.