Search Results (56)

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

Photogrammetry can be a valuable tool for understanding landscape evolution and natural hazards such as landslides. However, factors such as vegetation cover, shadows, and unstable ground can limit its effectiveness. Using photos across time to monitor an area with unstable or changing ground conditions results in fewer tie points between images across time, and often leads to low comparative accuracy if single-epoch (i.e., classical) photogrammetric processing approaches are used. This paper presents a study evaluating the co-alignment approach applied to fixed terrestrial timelapse photos at an active landslide site. The study explores the comparative accuracy of reconstructed surface models and the location and behavior of tie points over time in relation to increasing levels of global change due to landslide activity and rockfall. Building upon previous work, this study demonstrates that high comparative accuracy can be achieved with a relatively low number of inter-epoch tie points, highlighting the importance of their distribution across stable ground, rather than the total quantity. High comparative accuracy was achieved with as few as 0.03 percent of the overall co-alignment tie points being inter-epoch tie points. These results show that co-alignment is an effective approach for conducting change detection, even with large degrees of global changes between surveys. This study is specific to the context of geoscience applications like landslide monitoring, but its findings should be relevant for any application where significant changes occur between surveys. Full article

This study explores the growing application of 3D remote sensing in geohazard studies, particularly for rock slope monitoring. It highlights the use of cost-effective Street View Imagery (SVI) and Unmanned Aerial Vehicles (UAV) through Structure-from-Motion (SfM) photogrammetry as tools for 3D rockfall monitoring. Using multi-temporal SVI and UAV Imagery from the Koto Panjang cliff in Indonesia, we quantify rockfall volume changes over seven years and assess associated geohazards. The results reveal a total rockfall retreat of 5270 m³, with an average annual rate of 7.53 m³/year. Structural analysis identified six major discontinuity sets and confirmed inherent instability within the rock mass. Kinematic simulations using SVI and UAV-derived data further assessed rockfall trajectories and potential impact zones. Results indicate that 40% of simulated rockfall deposits accumulated near existing roads, with significant differences in distribution based on scree slope angles. This emphasizes the role of scree slope in influencing rockfall propagation. In conclusion, SVI and UAV imagery presents a valuable tool for 3D point cloud reconstruction and rockfall hazard assessment, particularly in areas lacking historical data. The study showcases the effectiveness of using SVI and UAV imagery in quantifying historical past rockfall volume and identifies critical areas for mitigation strategies, highlighting the importance of scree slope angle in managing rockfall hazard. Full article

Slope-related geological disasters occur frequently in various countries, posing significant threats to surrounding infrastructure, ecosystems, and human lives and property. Traditional manual monitoring methods for slope hazards are inefficient and have limited coverage. To enhance the monitoring and analysis of geological hazards, a study was conducted on the legacy slopes of an abandoned quarry in Jinan, Shandong Province, China. High-resolution images of the slopes were captured using unmanned aerial vehicle (UAV) phase tilt photogrammetry, and three-dimensional models were subsequently constructed. Software tools, including LiDAR360 5.2 and ArcMap 10.8, were employed to extract slope geological information, identify disaster-prone areas, and conduct stability analyses. The Analytic Hierarchy Process (AHP) was employed to further evaluate the stability of hazardous slopes. The results reveal the presence of two geohazard-prone areas in the study area. Geological analysis shows that both areas exhibit instability, with a high susceptibility to small-scale rockfalls and landslides. The integration of UAV remote sensing technology with AHP represents a novel approach, and the combination of multiple analytical methods enhances the accuracy of slope stability assessments. Full article

The stability analysis of rock slopes traditionally involves the evaluation of limit state conditions to determine the potential for rockslides and rockfalls. However, empirical evidence supported by experimental studies has highlighted the complex response of rock interfaces under differential loading. It is characterized by distinct pre-peak and post-peak stress–deformation relationships, which represent the deformation profile of loaded rock interfaces and, thus, capture dynamic and evolving events. The present research introduces an interpretation framework to reconcile these contradicting paradigms by interpreting empirically and explicitly the full stress–displacement relationship along active shear surfaces of rockslide events. The Complete Stress–Displacement Surface (CSDS) model was incorporated into conventional analytical solutions for a rock slope planar failure to describe the evolving stress conditions during an active rockslide event. The Ruinon rockslides (Italy), monitored and studied extensively at the turn of the century, are revisited using the adapted CSDS model to describe the evolving stress–deformation conditions. Empirical and experimental calibrations of the model are implemented and compared using the CSDS model for the description of evolving shear stresses in large rockslide events based on topographical monitoring. This paper contributes a detailed framework for correlating in situ topographical monitoring with relevant geomechanical information to develop a representative model for the evolving stress conditions during a rockslide event. Full article

This study aims to address the challenges associated with rockfall assessment and monitoring, focusing on the coastal cliffs of “Navagio Shipwreck Beach” in Zakynthos. A complete time-series analysis was conducted using state-of-the-art methodologies including a 2020 survey using unmanned aerial systems (UASs) and two subsequent surveys, incorporating terrestrial laser scanning (TLS) and UAS survey techniques in 2023. Achieving high precision and accuracy in georeferencing involving direct georeferencing, the utilization of pseudo ground control points (pGCPs), and integrating post-processing kinematics (PPK) with global navigation satellite system (GNSS) permanent stations’ RINEX data is necessary for co-registering the multitemporal models effectively. For the change detection analysis, UAS surveys were utilized, employing the multiscale model-to-model cloud comparison (M3C2) algorithm, while TLS data were used in a validation methodology due to their very high-resolution model. The synergy of these advanced technologies and methodologies offers a comprehensive understanding of rockfall dynamics, aiding in effective assessment and monitoring strategies for coastal cliffs prone to rockfall risk. Full article

This manuscript presents a multidisciplinary study that proposes a methodology for delineating and categorizing vulnerability at rockfall risk areas to avoid human injuries and infrastructure damage caused by rockfalls. The presented workflow includes (i) classical geological mapping, (ii) the interpretation of high-resolution satellite data for observing the spatial distribution of fallen boulders, (iii) analytical hierarchy processing of spatial information within a Geographical Information System (GIS) platform, (iv) close-range remote sensing campaigns with Unmanned Aerial Systems (UASs), and (v) integrated simulation of rockfall events. This methodology was applied to Kalymnos Island, which belongs to the Dodecanese Islands complex of the southeastern Aegean Sea in Greece. It is characterized by unique geomorphological features, including extensive vertical limestone cliffs that span the island. These cliffs make it one of the world’s most densely concentrated areas for sport climbing. The results highlighted the areas that the local authorities need to focus on and suggested measures for increasing the safety of climbers and infrastructure. Full article

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

Drainage tunnel stability is crucial for engineering project safety (e.g., mine engineering and dams), and rockfall events and water release are key indicators of drainage tunnel stability. To address this, we developed a monitoring system to simulate drainage tunnel intrusions based on distributed acoustic sensing (DAS), and we obtained typical characteristics of events like rockfall events and water release. Given the multitude of DAS signal feature parameters and challenges, such as high-dimensional features impacting the classification accuracy of machine learning, we proposed an identification method for drainage tunnel intrusion events using principal component analysis (PCA) and neural networks. PCA reveals that amplitude-related parameters—amplitude, mean amplitude, and energy—significantly contribute to DAS signal classification, reducing the feature parameter dimensions by 54.8%. The accuracy of intrusion event classification improves with PCA-processed data compared to unprocessed data, with overall accuracy rates of 79.1% for rockfall events and 72.7% for water release events. Additionally, the artificial neural network model outperforms the Bayesian and logistic regression models, demonstrating that ANN has advantages in handling complex models for intrusion event classification. Full article

When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of resources such as steel or concrete. However, these solutions are expensive, considering their construction and maintenance, and it is very difficult to protect long linear stakes. A more sustainable and effective risk management strategy could be to account for changes on rockfall activity related to weather conditions. By integrating sustainability principles, we can implement mitigation measures that are less resource-intensive and more adaptable to environmental changes. For instance, instead of solely relying on physical barriers, solutions could include measures such as restriction of access, monitoring and mobilization of emergency kits containing eco-friendly materials. A critical step in developing such a strategy is accurately predicting periods of increased rockfall activity according to meteorological triggers. In this paper, we test four machine learning models to predict rockfalls on the National Road 1 at La Réunion, a key road for the socio-economic life of the island. Rainfall and rockfall data are used as inputs of the predictive models. We show that a set of features derived from the rainfall and rockfall data can predict rockfall with performances very close and almost slightly better than the standard expert model used for operational management. Metrics describing the performance of these models are translated in operational terms, such as road safety or the duration of road closings and openings, providing actionable insights for sustainable risk management practices. Full article

Hazards like landslides have significant economic and societal repercussions; hence, the issue of remote structure health monitoring has grown in significance for geologic applications. Wireless sensor networks (WSNs) stand out among the new sensing architectures as a particularly well-suited solution, thanks to the versatility they offer. This research, necessary for safety reasons, predictive maintenance and emergency evacuation, presents a WSN-based landslide monitoring system with multi-technology sensor implementation. Its goal is to track the land movements on a hillside. The network is composed of long range (LoRa) sensor nodes connected using a LoRaWAN media access control (MAC) layer. The nodes are several and of different natures and help monitor land movements, hydric parameters and rockfall events, and they also offer a camera view of the landslide in case of an emergency. The system is built on an Internet of Things (IoT) framework, enabling online access to data and reports. The final work will include a system description of the hardware and functionality of all the devices, a description of the web section for remote monitoring, a power analysis and statistics from actual scenarios. Full article

The instability of slopes and ground subsidence caused by the conversion from open-pit to underground mining are important aspects of mining disaster research. This study focuses on the instability of slopes and ground subsidence during the conversion from open-pit to underground mining in the Beiyi mining area of the Shilu iron ore mine. Using numerical simulation and analysis, this study establishes a mechanical analysis model to assess the rock stability and movement of rockfall. The research findings indicate that there are significant stress concentration phenomena in the surrounding and floor areas of the goaf during the mining process. The collapse zone mainly develops in the western area before and after a certain level of mining and then shifts to the eastern part of Beiyi area. Surface subsidence expands after mining at a certain level, resulting in a large-scale disturbance area. Furthermore, the eastern slope experiences extensive landslides. This study suggests the continued monitoring of landslides and slope stability in specific areas of the mine. The research results can help us to understand the stability of the open-pit to underground rock mass in Hainan, judge the development trend of the surface subsidence range, and provide a reference for the stability evaluation of the rock mass mined by the open-pit-to-underground caving method. Full article

The alpine environment with a high degree of nature protection is characterized by complete non-intervention. The processes and phenomena occurring in it are exclusively of a natural origin. Related geohazards are threatening the safety of people’s movement. They arise as a result of a combination of meteorological, hydrological, and geological–morphological factors permanently operating in the country. Therefore, the prevention of fatal events is limited to monitoring and predicting changes in selected objects where we expect change. Changes in the shape and dimension, or the object’s deformation, can be documented using geodetic and photogrammetric measurements. Our research focuses on monitoring a rock talus cone in High Tatras, Slovakia, at an altitude of 1700 m above sea level (ASL), created mainly due to erosion and seasonal torrential rains. To monitor changes in selected objects, we used mass non-contact methods of terrestrial laser scanning (TLS), UAS photogrammetry based on the principle of structure-from-motion–multi-view stereo (SfM–MVS), and airborne laser scanning (ALS). From the selective measurement methods, spatial measurement by a total station (TS) and height measurement based on the principle of precise leveling were used in the monitoring deformation network on a stand-alone boulder. The research results so far analyze and evaluate the possibilities, limits, effectiveness, and accuracy of the measurement and data processing methods used. As a result, we propose a complex methodology for monitoring similar phenomena in alpine environments. Full article

Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, which is the focus of this study. This study uses automatically captured photos taken at a daily frequency by five fixed-base cameras in conjunction with multi-epoch Structure-from-Motion (SfM) photogrammetric processing techniques to evaluate changes in a rock slope in Majes, Arequipa, Peru. The results of the monitoring campaign demonstrate that there are potential issues with the common notion that higher frequency change detection is always superior. For lower spatial resolutions or when only large changes are of concern, using a high-frequency monitoring method may cause small volume changes that eventually aggrade into larger areas of change to be missed, whereas most of the total volume change would be captured with lower-frequency monitoring intervals. In this study, daily change detection and volume calculation resulted in a cumulative rockfall volume of 4300 m³ over about 14 months, while change detection and volume calculation between dates at the start and end of the 14-month period resulted in a total rockfall volume of 12,300 m³. High-frequency monitoring is still the most accurate approach for evaluating slope evolution from a rockfall frequency and size distribution perspective, and it allows for the detection of short accelerations and pre-failure deformations, but longer-term comparison intervals may be required in cases where spatial resolution is low relative to temporal resolution to more accurately reflect the total volume change of a given rock slope over a long period of time. Full article

In this research article, we propose a practical methodology for evaluating the affecting potential of detachment areas in rockfalls. Our innovative approach combines an assessment of the visibility of rockfall source areas, with reference to specific rockfall scenarios and elements at risk, considering the rockfall Susceptibility Index to Failure (SIF) of these areas. The result is the characterization of source areas through a rockfall Source Affecting Index (SAI), which considers both the morphology of the slope and the geostructural conditions of the rock walls. This information can be very useful since it aids in optimizing more in-depth analyses, as well as the placement of monitoring instruments or stabilization systems. The proposed methodology has been implemented in the open-source software QGIS through the development of an easy-to-use plugin named Ranking of the Affecting Potential of Detachment Areas in Rockfalls, or “RADAR”. RADAR is designed to be used in conjunction with QPROTO, a well-known QGIS plugin for preliminary rockfall susceptibility/hazard analyses based on a visibility analysis and a simplified mechanical method. To demonstrate the effectiveness of the proposed approach, an application to a case study located in the Western Alps (Bardonecchia, Italy) is presented and discussed in the paper. Full article