An analysis of failure mechanism constraints on pre-failure rock block deformation using TLS and roto-translation methods

Rowe, Emily; Hutchinson, D. Jean; Kromer, Ryan

doi:10.1007/s10346-017-0886-8

Cited by 18 publications

(9 citation statements)

References 23 publications

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“…As showed in Figure 9 , major rockfalls might be preceded by precursory movements (i.e., pre-failure deformation) highlighting the proneness of certain rock slopes to have deforming areas and associated opening fractures that precede the main failure event [ 111 , 112 ]. Gigapixel techniques can also be combined with LiDAR products for further analysis aimed at three-dimensional characterisation and monitoring of rock slopes [ 113 , 114 , 115 , 116 , 117 ]. In [ 62 , 115 ] the Gigapixel images have been used to identify unweathered rock surfaces in order to map recent rockfall source zones.…”

Section: Discussionmentioning

confidence: 99%

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Romeo

Cosentino

Giani

et al. 2021

Sensors

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Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.

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Section: Discussionmentioning

confidence: 99%

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Romeo

Cosentino

Giani

et al. 2021

Sensors

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“…However, this capability can help to separate events that are deposited into talus channels vs. rocks that are passing primarily over outcrop, to better understand how rockfalls behave on this complex slope. Ongoing work involves the development of a fully 3-D rockfall model using the Unity game engine (Ondercin, 2016) which will consider the influence of these factors (Sala et al, 2017). Additional work utilizing the inventory of rockfall events is focused on developing a logic for identifying rockfall source zones based on multiple factors, considering the slope geometry required to produce rockfalls of varying magnitudes.…”

Section: Discussionmentioning

confidence: 99%

Combining temporal 3-D remote sensing data with spatial rockfall simulations for improved understanding of hazardous slopes within rail corridors

Veen

Hutchinson

Bonneau

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Remote sensing techniques can be used to gain a more detailed understanding of hazardous rock slopes along railway corridors that would otherwise be inaccessible. Multiple datasets can be used to identify changes over time, creating an inventory of events to produce magnitude–frequency relationships for rockfalls sourced on the slope. This study presents a method for using the remotely sensed data to develop inputs to rockfall simulations, including rockfall source locations and slope material parameters, which can be used to determine the likelihood of a rockfall impacting the railway tracks given its source zone location and volume. The results of the simulations can be related to the rockfall inventory to develop modified magnitude–frequency curves presenting a more realistic estimate of the hazard. These methods were developed using the RockyFor3D software and lidar and photogrammetry data collected over several years at White Canyon, British Columbia, Canada, where the Canadian National (CN) Rail main line runs along the base of the slope. Rockfalls sourced closer to the tracks were more likely to be deposited on the track or in the ditch, and of these, rockfalls between 0.1 and 10 m3 were the most likely to be deposited. Smaller blocks did not travel far enough to reach the bottom of the slope and larger blocks were deposited past the tracks. Applying the results of the simulations to a database of over 2000 rockfall events, a modified magnitude–frequency can be created, allowing the frequency of rockfalls deposited on the railway tracks or in ditches to be determined. Suggestions are made for future development of the methods including refinement of input parameters and extension to other modelling packages.

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“…The White Canyon has been extensively monitored by Queen's University as part of the Canadian Railway Ground Hazard Research Program (RGHRP) since April 2012. A number of different landslide mechanisms have been observed including rockfalls (Kromer et al 2015), debris flows , and topples (Rowe et al 2018) at the study site. Moreover, slope failure processes have created a variable landscape that ranges from debris channels to overhanging blocks and spires of varying lithology.…”

Section: Applicationmentioning

confidence: 99%

Supervoxel-Based Multi-Scale Point Cloud Segmentation Using Fnea for Object-Oriented Rock Slope Classification Using TLS

Farmakis

Bonneau

Hutchinson

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Computer vision applications have been increasingly gaining space in the field of remote sensing and geosciences for automated terrain classification and semantic labelling purposes. The continuous and rapid development of monitoring techniques and enhancements in the spatial resolution of sensors have increased the demand for new remote sensing data analysis approaches. For semantic labelling of 2D (or 2.5D) image terrain representations for rock slopes, it has been shown that Object-Based Image Analysis (OBIA) results in high efficiency and accurate identification of landslide hazards. However, the application of such object-based approaches in 3D point cloud analysis is still under development for geospatial data analysis. In the field of engineering geology, which deals with complex rural landscapes, frequently the analysis needs to be conducted based solely on 3D geometrical information accounting for multiple scales simultaneously. In this study, the primary segmentation step of the object-based model is applied to a TLS-derived point cloud collected at a landslide-active rock slope. The 3D point cloud segmentation methodology proposed here builds on the principles of the Fractal Net Evolution Approach (FNEA). The objective is to provide a geometry-based point cloud segmentation framework that preserves the 3D character of the data throughout the process and favours the multi-scale analysis. The segmentation is performed on the basis of supervoxels based on purely geometrical local descriptors derived directly from the TLS point clouds and comprises the basis for the subsequent steps towards the development of an efficient Object-Based Point cloud Analysis (OBPA) framework in rock slope stability assessment by adding semantic meaning to the data through a homogenization process.

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An analysis of failure mechanism constraints on pre-failure rock block deformation using TLS and roto-translation methods

Cited by 18 publications

References 23 publications

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring: The Poggio Baldi Landslide Natural Laboratory

Combining temporal 3-D remote sensing data with spatial rockfall simulations for improved understanding of hazardous slopes within rail corridors

Supervoxel-Based Multi-Scale Point Cloud Segmentation Using Fnea for Object-Oriented Rock Slope Classification Using TLS

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