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This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.

This paper describes the results of a field investigation with the objective of evaluating the possibility to produce drone-derived 3D digital point clouds sufficiently dense and accurate to determine discontinuity surface roughness characteristics. A discontinuous rock mass in Italy was chosen as the investigation site and Structure from Motion and Multi-View Stereo techniques adopted for producing three-dimensional point clouds from the two-dimensional image sequences. Since the roughness of discontinuities depends on direction, scale and resolution of the sampling, data were always collected along the maximum slope gradient. The scale effect was evaluated by analysing discontinuity profiles of different lengths (10 cm, 30 cm, 60 cm and 100 cm), with measurements taken from drone flights flown at different distances from the rocky slopes (10 m, 20 m and 30 m). The accuracy of the derived joint roughness coefficients was evaluated by direct comparison with discontinuity profiles measured during fieldwork using conventional techniques and from contemporaneous terrestrial laser scanning. Results from this research show that 3D digital point clouds, derived from the processing of drone-flight images, were successfully used for reliable representation of discontinuity roughness for profiles longer than 60 cm, whereas less reliable results were achieved for shorter profile lengths. This, even if strictly related to this case study since several factors can affect the minimum profile length, represents a significant contribution to improve the knowledge on the use of remotely captured data for characterising the discontinuities in natural or man-made rock outcrops, particularly where access difficulties do not allow conventional engineering-geological surveys to be undertaken.

Block toppling instability can be a common problem in natural rock masses, especially in mining environments where excavation activity may trigger discontinuity-controlled instability by modifying the natural slope geometry. Traditional investigations of block toppling failure consider classic kinematic analyses and simplified two-dimensional limit equilibrium methods. This approach is still the most commonly adopted, but the simple two-dimensional conceptual model may often oversimplify the instability mechanisms, ignoring potential critical factors specifically related to the three-dimensional geometry. This paper uses a three-dimensional distinct element method approach applied to an example case study, identifying the critical parameters that influence direct toppling instability in an open pit environment. Terrestrial laser scanning was used to obtain detailed three-dimensional geometrical information of the slope face geometry for subsequent stability analyses. A series of sensitivity analyses on critical parameters such as friction angle, discontinuity shear and normal stiffness, discontinuity spacing, and orientation was performed, using simple conceptual three-dimensional numerical modeling. Results of the analyses revealed the importance of undertaking three-dimensional analyses for direct toppling investigations that allow identification of critical parameters. A three-dimensional distinct element analysis was then performed using a more realistic complex volumetric mesh model of the case study slope which confirmed the previous modeling results but also identified unstable blocks in high slope angle areas, providing useful information for life of mine design. The paper highlights the importance of slope geometry and fracture network orientation on potential slope instability mechanisms.

In this research, we present a new approach to define the distribution of block volumes during rockfall simulations. Unmanned aerial vehicles (UAVs) are utilized to generate high-accuracy 3D models of the inaccessible SW flank of the Mount Rava (Italy), to provide improved definition of data gathered from conventional geomechanical surveys and to also denote important changes in the fracture intensity. These changes are likely related to the variation of the bedding thickness and to the presence of fracture corridors in fault damage zones in some areas of the slope. The dataset obtained integrating UAV and conventional surveys is then utilized to create and validate two accurate 3D discrete fracture network models, representative of high and low fracture intensity areas, respectively. From these, the ranges of block volumes characterizing the in situ rock mass are extracted, providing important input for rockfall simulations. Initially, rockfall simulations were performed assuming a uniform block volume variation for each release cell. However, subsequent simulations used a more realistic nonuniform distribution of block volumes, based on the relative block volume frequency extracted from discrete fracture network (DFN) models. The results of the simulations were validated against recent rockfall events and show that it is possible to integrate into rockfall simulations a more realistic relative frequency distribution of block volumes using the results of DFN analyses.

This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based kinematic analysis. Results from the kinematic analysis, coupled with several commonly used landslide influencing factors, were adopted as input variables in ML models to predict landslides. In this paper, various ML models, such as random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and deep learning neural network (DLNN) models were evaluated. Results of two validation methods (confusion matrix and ROC curve) show that the involvement of discontinuity-related variables significantly improved the landslide predictive capability of these four models. Their addition demonstrated a minimum of 6% and 4% increase in the overall prediction accuracy and the area under curve (AUC), respectively. In addition, frequency ratio (FR) analysis showed good consistency between landslide probability that was characterized by FR values and discontinuity-related variables, indicating a high correlation. Both results of model validation and FR analysis highlight that inclusion of discontinuities into ML models can improve landslide prediction accuracy.

This paper focuses on the back analysis of an ancient, catastrophic rock avalanche located in the small city of Lettopalena (Chieti, Italy). The integrated use of various investigation methods was employed for landslide analysis, including the use of traditional manual surveys and remote sensing (RS) mapping for the identification of geological structures. The outputs of the manual and RS surveys were then utilised to numerically model the landslide using a 2D distinct element method. A series of numerical simulations were undertaken to perform a sensitivity analysis to investigate the uncertainty of discontinuity properties on the slope stability analysis and provide further insight into the landslide failure mechanism. Both numerical modelling and field investigations indicate that the landslide was controlled by translational sliding along a folded bedding plane, with toe removal because of river erosion. This generated daylighting of the bedding plane, creating kinematic freedom for the landslide. The formation of lateral and rear release surfaces was influenced by the orientation of the discrete fracture network. Due to the presence of an anticline, the landslide region was constrained in the middle-lower section of the slope, where the higher inclination of the bedding plane was detected. The landslide is characterized by a step-path slip surface at the toe of the slope, which was observed both in the modelling and the field. This paper highlights the combined use of a geological model and numerical modelling to provide an improved understanding of the origin and development of rock avalanches under the influence of river erosion, anticline structures, and related faults and fractures.

The use of remote sensing techniques is now common practice in different working environments, including engineering geology. Moreover, in recent years the development of structure from motion (SfM) methods, together with rapid technological improvement, has allowed the widespread use of cost-effective remotely piloted aircraft systems (RPAS) for acquiring detailed and accurate geometrical information even in evolving environments, such as mining contexts. Indeed, the acquisition of remotely sensed data from hazardous areas provides accurate 3-D models and high-resolution orthophotos minimizing the risk for operators. The quality and quantity of the data obtainable from RPAS surveys can then be used for inspection of mining areas, audit of mining design, rock mass characterizations, stability analysis investigations and monitoring activities. Despite the widespread use of RPAS, its potential and limitations still have to be fully understood.In this paper a case study is shown where a RPAS was used for the engineering geological investigation of a closed marble mine area in Italy; direct ground-based techniques could not be applied for safety reasons. In view of the re-activation of mining operations, high-resolution images taken from different positions and heights were acquired and processed using SfM techniques to obtain an accurate and detailed 3-D model of the area. The geometrical and radiometrical information was subsequently used for a deterministic rock mass characterization, which led to the identification of two large marble blocks that pose a potential significant hazard issue for the future workforce. A preliminary stability analysis, with a focus on investigating the contribution of potential rock bridges, was then performed in order to demonstrate the potential use of RPAS information in engineering geological contexts for geohazard identification, awareness and reduction.

The increased accessibility of drone technology and structure from motion 3D scene reconstruction have transformed the approach for mapping inaccessible slopes undergoing active rockfalls and generating virtual outcrop models (VOM). The Poggio Baldi landslide (Central Italy) and its natural laboratory offers the possibility to monitor and characterise the slope to define a workflow for rockfall hazard analysis. In this study, the analysis of multitemporal VOM (2016–2019) informed a rockfall trajectory analysis that was carried out with a physical-characteristic-based GIS model. The rockfall scenarios were reconstructed and then tested based on the remote sensing observations of the rock mass characteristics of both the main scarp and the rockfall fragment inventory deposited on the slope. The highest concentration of trajectory endpoints occurred at the very top of the debris talus, which was constrained by a narrow channel, while longer horizontal travel distances were allowed on the lower portion of the slope. To further improve the understanding of the Poggio Baldi landslide, a time-independent rockfall hazard analysis aiming to define the potential runout associated with several rock block volumetric classes is a critical component to any subsequent risk analysis in similar mountainous settings featuring marly–arenaceous multilayer sedimentary successions and reactivated main landslide scarps.

In recent years data acquisition from remote sensing has become readily available to the quarry sector. This study demonstrates how such data may be used to evaluate and back analyse rockfall potential of a legacy slope in a blocky rock mass. Use of data obtained from several aerial LiDAR (Light Detection and Ranging) and photogrammetric campaigns taken over a number of years (2011 to date) provides evidence for potential rockfall evolution from a slope within an active quarry operation in Cornwall, UK. Further investigation, through analysis of point cloud data obtained from terrestrial laser scanning, was undertaken to characterise the orientation of discontinuities present within the rock slope. Aerial and terrestrial LiDAR data were subsequently used for kinematic analysis, production of surface topography models and rockfall trajectory analyses using both 2D and 3D numerical simulations. The results of an Unmanned Aerial Vehicle (UAV)-based 3D photogrammetric analysis enabled the reconstruction of high resolution topography, allowing one to not only determine geometrical properties of the slope surface and geo-mechanical characterisation but provide data for validation of numerical simulations. The analysis undertaken shows the effectiveness of the existing rockfall barrier, while demonstrating how photogrammetric data can be used to inform back analyses of the underlying failure mechanism and investigate potential runout.

Legacy colliery spoil tip failures pose a significant hazard that can result in harm to persons or damage to property and infrastructure. In this research, the 2020 Wattstown tip landslide caused by heavy rainfall was examined to investigate the likely mechanisms and developmental factors contributing to colliery spoil tip failures in Welsh coalfields. To achieve this, an integrated method was proposed through the combination of remote sensing mapping, stability chart analysis, 2D limit equilibrium (LE) modelling, and 3D finite difference method (FDM) analysis. Various water table geometries were incorporated into these models to ascertain the specific groundwater condition that triggered the occurrence of the 2020 landslide. In addition, sensitivity analyses were carried out to assess the influence of the colliery spoil properties (i.e., cohesion, friction angle, and porosity) on the slope stability analysis. The results indicate that the landslide was characterised by a shallow rotational failure mode and spatially constrained by the critical water table and an underlying geological interface. In addition, the results also imply that the landslide was triggered by the rise of water table associated with heavy rainfall. Through sensitivity analysis, it was found that the properties of the colliery spoil played an important role in confining the extent of the landslide and controlling the process of its development. The findings underscore the detrimental effects of increased pore pressures, induced by heavy rainfall, on the stability of colliery tips, highlighting the urgent needs for local government to enhance water management strategies for this region.