applied sciences

Article Discontinuity Characterization of Rock Masses through Terrestrial Laser Scanner and Unmanned Aerial Vehicle Techniques Aimed at Slope Stability Assessment

Marco Pagano 1, Biagio Palma 1, Anna Ruocco 1 and Mario Parise 2,3,*

1 IdroGeo Srl, 80069 Vico Equense, Italy; [email protected] (M.P.); [email protected] (B.P.); [email protected] (A.R.) 2 Earth and Environmental Sciences Department, University Aldo Moro, 70125 Bari, Italy 3 National Research Council, Institute of Research for Geo-Hydrological Protection, 70126 Bari, Italy * Correspondence: [email protected]



Received: 25 March 2020; Accepted: 22 April 2020; Published: 24 April 2020


Abstract: Stabilization projects of rock masses cannot be performed without a proper geomechanical characterization. The classical approaches, due to logistic issues, typically are not able to cover extensively the areas under study. Geo-structural analysis on point cloud from terrestrial laser scanning and photogrammetry from unmanned aerial vehicles are valid tools for analysis of discontinuity systems. Such methodologies provide reliable data even in complex environmental settings (active cliffs) or at inaccessible sites (excavation fronts in tunnels), offering advantages in terms of both safety of the operators and economic and time issues. We present the implementation of these techniques at a tuff cliff over the Santa Caterina beach (Campania) and at the main entrance of Castellana Caves (Apulia). In the first case study, we also perform an integration of the two techniques. Both sites are of significant tourist and economic value, and present instability conditions common to wide areas of southern Italy: namely, retrogressive evolution of active cliffs along the coast, and instability at the rims of natural and/or artificial sinkholes. The results show the reliability of the data obtained through semi-automatic methods to extract the discontinuity sets from the point clouds, and their agreement with data collected in the field through classical approaches. Advantages and drawbacks of the techniques are illustrated and discussed.

Keywords: laser scanner; geo-structural classification; rock mass; stability

1. Introduction Rock masses are typically anisotropic, due to presence of a variety of discontinuities of both primary (bedding planes) and secondary (joints, faults) origin. They present therefore significant variations in the geotechnical and mechanical properties, also in reference to their time-dependent behavior. At this goal, the design in different fields of engineering projects generally considers the use of geotechnical models to characterize a site [1–13]. In such models, the rock types are subdivided into units that can be considered homogenous based upon their lithology and technical properties. In this way a 2- or 3-D schematic representation is obtained, aimed at providing a basis for forecasting the behavior of the involved materials, but also at understanding the likely interactions between the ground and the engineering works. In the case of carbonate rock masses, the complexity becomes still higher, due to peculiarity of carbonate rocks, and to the effects of karst processes [14–18]. Networks consisting of karst conduits and passages are able to significantly change the behavior of the rock mass; as a matter of fact,

Appl. Sci. 2020, 10, 2960; doi:10.3390/app10082960 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 2960 2 of 25 they are typically the discontinuity sets of greater size and frequency, at the same time showing high pervasiveness throughout the rock mass. This results also in controlling the water flow, which occurs in particular during the most significant rainstorm events. From all these considerations, it appears that karst features strongly complicate any analysis dealing with fractured and karstified carbonate rock masses. Nevertheless, this complication is generally not considered at all in the classical geo-mechanical approaches [19], that are implemented disregarding these important aspects. Karst is known as a particularly difficult to examine environment, so that planning and designing engineering works should be performed with extreme care, including as much as possible the knowledge of the surficial and subterranean karst features, also obtained through direct speleological research and exploration [20–23]. It therefore appears that, whatever the setting and the lithologies involved, the issue of discontinuity analysis within rock masses is crucial for a correct evaluation of the rock stability, as well as for choosing the most suitable stabilization measures. Standard traditional methods for the geo-mechanical characterization of rock masses are typically time-consuming, with the measurements being confined to some sectors in the rock face, due to difficulties in accessibility [24–27]. Such limitations result in providing only punctual information about the geo-mechanical setting of the rock mass. The main relevant problem for analysis of rock slopes is therefore logistics, and the possibility to directly approach the rock face: since instability processes mostly affect near vertical to vertical slopes, the only opportunity for operators is making a scanline at the base of the cliff, thus strongly limiting the assessment of the overall rock face. Changes in the properties along the rock face, at different heights, are typically estimated through rough, qualitative approaches. As an alternative, availability by rock-climber geologists allows to fill some gaps, representing however a very difficult, expensive, and time-consuming phase of the survey, in any case able to cover only a few vertical lines. Stereo-photogrammetric techniques are nowadays frequently being used to measure the orientations of discontinuities [28–30]. Adoption of the basic principles of photogrammetry can in fact result extremely useful in rock mechanics and in the evaluation of rock stability as well. The first attempts in this direction, however, were quite frustrating due to the long time required to precisely outline the discontinuities, and the slowness in their elaboration [31–36]. Only during the recent decades, thanks to availability of powerful computers, and advancement in the technologies, these techniques have become widespread, and several approaches have been proposed to obtain 3D models of rock faces by remote sensing data. Among these, terrestrial laser scanners (TLS) and unmanned aerial vehicles (UAV) have undoubtedly proved to be among the most efficient and reliable tools, and are being today used in many stability analyses worldwide [37–50]. In this paper, we present some considerations about the use of surveys from these techniques, through production of dense point clouds and the related semi-automatic acquisition of discontinuity data for stability analysis, based upon some experiences carried out in the last years. Advantages and drawbacks of the single techniques, and of their integration as well, will be illustrated through two significant case studies in southern Italy, concerning a cliff in pyroclastic rocks in Campania and a natural karst cave in Apulia. Choice of the sites was dictated by two common situations in southern Italy, that is instability problems along high sea cliffs and the possibility of failures from natural or artificial sinkholes. This latter case, in detail, was selected to highlight the importance of high-resolution analysis and studies in carbonate rock masses, and the need to include in the analysis the role played by karst processes, which very often is underestimated or neglected. After introductory sections describing the techniques and the instruments used, the two case studies are presented, before reaching the conclusions where our considerations about pros and cons of the techniques are illustrated. Appl. Sci. 2020, 10, 2960 3 of 25

2. Materials and Methods

2.1. Survey through Laser Scanner 3D 3D terrestrial laser scanner (TLS) is an innovative methodology to model and monitor rock cliffs and underground environments. The method allows to survey and reproduce the measured surfaces with an acquisition mesh at sub-centimeter resolution. The outcome is a snapshot of the observed scene at the date of acquisition, expressed by millions of georeferenced points; it is easily navigable and shareable through cloud-computing. The first phase of the survey consists of a careful and detailed analysis of the area, and in the selection of the scan positions, that is the sites where the instrument has to be located; further, based upon the distance between the laser and the object, the resolution of the scan is defined, taking into account the detail required in the output (in turn, depending upon the aim of the work). This activity is mandatory to the survey design, aimed at locating the control points (targets). To reach the best results in terms of final outcome, it is necessary that the scan positions are such that the laser beam is always perpendicular to the main discontinuity sets. In this way, it is possible to have a complete and homogeneous point cloud, without void spaces, and where all the planes of the scanned object could be perfectly recognized when varying the orientation of the model in space. The survey, carried out in the case studies of this work with a Laser Scanner RIEGL VZ400 (Figure1), allows acquisition of a georeferenced point cloud of millions of points, each one of which is described by the following information: geographical position in space (X, Y, Z), chromatic information (RGB), and reflectance (i). Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 25

 Class 1 Laser  Range greater than 500 mt  High definition camera (>6 Mpixel)  First and last impulse  Ability to reduce the shadow areas due to vegetation  Integrated inclinometer sensor and lead laser  Integrated GPS antenna  High velocity of acquisition: min. 122,000 pti/sec  Scan angles: 360° horizontal – 360° vertical  Precision: 3 mm  Integrated compass

FigureFigure 1.1. Main characteristics of the Laser Scanner RieglRiegl VZ400.VZ400.

DuringOnce the the scans survey, have multiple been merged, scans are and taken a unique at each scan3D georeferenced position, with variableand colored resolution: model initially,created an[52, overall53], optimization scan with wide of mesh the is point obtained clouds to get proceeds a file that by is easy eliminating to manage, the and untrue to have elements, an immediate the controloverlapping of the and wide redundant surveyed points, area; then,and the several not relevant other scans features are produced(electric cables, withprogressively etc.), and to reduce more the noise [54–56]. The outcome, at this point of the elaboration, is a 3D model consisting of a highly detailed point cloud, able to represent with high accuracy the object geometry. To provide easier management of the successive phases of implementation, a spatial decimation around the points is carried out, to lighten the data and make them exportable to other software and in other formats. The final phase of processing the point clouds consists in the realization of the solid surface called mesh, through the software MeshLab, that represents, in an exhaustive manner, the poligonalization of the point cloud to create filled surfaces and volumes of easier visual and computational interpretation. All the survey products eventually end up in 2D and 3D graphic elaborates such as maps, drawings, profiles, mesh, DTM (digital terrain model), DSM (digital surface model), etc.

2.2. UAV Photogrammetric Survey The technique of photogrammetric survey of rock faces by means of unmanned aerial vehicles (UAV) is used to: - solve the problems related to lack of stable points where to establish the laser scanner (for instance, at the base of a rock cliff); - integrate the point cloud from laser scanner in those sectors characterized by no data, due to the relative position between laser and rock cliff; - survey areas not accessible by land (sea cliffs with no or limited beach, islands, etc.). The photogrammetric survey was carried out for the case studies here presented by using the UAV ITALDRON 4HSEPRO (Figure 2). Appl. Sci. 2020, 10, 2960 4 of 25 accurate meshes. During the phase of setting the detailed scans, the data regarding photographs are set too, that include the exposition time and the diaphragm aperture, so that the digital images to be linked to the point cloud are properly calibrated, based upon the variable light conditions and the closeness to the survey object. At the end of each scan, before changing the scan position, a detailed scan of the targets is carried out to allow the scanner to localize and record the installed reflectors, which will be used as tie points and ground control points in merging and georeferencing the point cloud. The data acquired with the laser scanner are elaborated with dedicated post-processing software, to merge the scans at the different scan positions, and to clean the model from untrue elements to eventually move toward the 2D and 3D elaborations [51]. The elaboration phase further includes assignment of colors to the point cloud, linking to each X, Y, Z point also the coordinates R, G, B, derived from the images acquired during the acquisition phase. This procedure occurs through alignment techniques, both manual and automatic, between the photos and the scans. At this point, it is possible to move to merging and roto-translation of the several acquired point clouds. This operation is of crucial importance; aimed at obtaining minor errors, it is necessary to merge the cloud points through high-reflectance targets, georeferenced by means of GPS and high-precision total station. At the end of these operations, the error deriving from merging the scans is evaluated. In case the error results to be unacceptable, “forced” alignment techniques, defined multi-station-adjustment (MSA), are applied. During the adjustment phase, it is possible to bypass the coordinates of targets, and to use the coordinates of the filtrated point clouds (polydata), properly created for each scan. The alignment technique MSA is aimed at reducing the error when “common” points belonging to the different polydata are joined. Once the scans have been merged, and a unique 3D georeferenced and colored model created [52,53], optimization of the point clouds proceeds by eliminating the untrue elements, the overlapping and redundant points, and the not relevant features (electric cables, etc.), and to reduce the noise [54–56]. The outcome, at this point of the elaboration, is a 3D model consisting of a highly detailed point cloud, able to represent with high accuracy the object geometry. To provide easier management of the successive phases of implementation, a spatial decimation around the points is carried out, to lighten the data and make them exportable to other software and in other formats. The final phase of processing the point clouds consists in the realization of the solid surface called mesh, through the software MeshLab, that represents, in an exhaustive manner, the poligonalization of the point cloud to create filled surfaces and volumes of easier visual and computational interpretation. All the survey products eventually end up in 2D and 3D graphic elaborates such as maps, drawings, profiles, mesh, DTM (digital terrain model), DSM (digital surface model), etc.

2.2. UAV Photogrammetric Survey The technique of photogrammetric survey of rock faces by means of unmanned aerial vehicles (UAV) is used to:

- solve the problems related to lack of stable points where to establish the laser scanner (for instance, at the base of a rock cliff); - integrate the point cloud from laser scanner in those sectors characterized by no data, due to the relative position between laser and rock cliff; - survey areas not accessible by land (sea cliffs with no or limited beach, islands, etc.).

The photogrammetric survey was carried out for the case studies here presented by using the UAV ITALDRON 4HSEPRO (Figure2). Appl. Sci. 2020, 10, 2960 5 of 25

Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 25

 Assembled for critical scenarios  Max height: 150 m  Maximum range: 1.5 km  Camera: Sony alpha FullFrame (42 Mpixel and video 4 k)  Sensor size: 35.8 × 23.9 mm  Parachute  3 axes gimbal  Autonomy: 30 min  Wind resistance: up to 50km/h

FigureFigure 2. Main 2. Main features features of the of Italdron the Italdron 4HSEPRO. 4HSEPRO.

As a a mandatory mandatory condition condition to properly to properly use UAV, use UAV,there is there the need is the to geo need-reference to geo-reference a number of a number ofpoints points (ground (ground control control points points—GCPs)—GCPs) to treat toin a treat metric in system a metric the systemimages, theand images,to correctly and roto to- correctly roto-translatetranslate the poi thent point cloud. cloud. At this At goal, this we goal, used we a used GPS (GPS a GPS LEICA (GPS GS14) LEICA and GS14) a Total and Station a Total Station (TRIMBLE S6). (TRIMBLE S6). Choice of the GCPs must be carefully planned: preferentially, all selected points should be Choice of the GCPs must be carefully planned: preferentially, all selected points should be materialized with well-visible targets located in proximity of areas with a chromatic variation, at materializeddifferent elevations, with well-visible in sectors well targets distributed located over in the proximity whole area of to areas be surveyed, with a farchromatic from the variation, atshadow different areas elevations, by trees and in other sectors objects. well distributed over the whole area to be surveyed, far from the shadowTo increase areas by the trees quality and of other the survey, objects. it is preferable to perform a single flight, or, at least, the flightTo must increase be carried the quality out in ofthe the same survey, part of it isthe preferable day; further, to performthe overlap a single among flight, adjacent or, at images least, the flight mustmust benever carried be below out in80 the%–85%. same part of the day; further, the overlap among adjacent images must never be belowFollowing 80–85%. the flight, the delicate stage of image elaboration starts, by checking a variety of characteristics,Following name thely: flight, the delicate stage of image elaboration starts, by checking a variety of (1) correct frame of each single snapshot (the optical axis must be perpendicular to the rock face characteristics, namely: as much as possible); (1)(2) quality correct of frame the image of each (out single of focus snapshot photograph (thes opticalmust be axisdeleted); must be perpendicular to the rock face as much(3) exposure: as possible); over- or under-exposed photographs must be deleted, or treated with dedicated software.(2) quality At this ofaim, the it imageis better (out to sho ofo focust in raw photographs modality in mustorder to be be deleted); able to balance the exposure during(3) the exposure: post-processing over- orphase; under-exposed photographs must be deleted, or treated with dedicated software.(4) sequential At this aim,order it of is the better photo tograph shoot strips. in raw modality in order to be able to balance the exposure duringClear the pictures post-processing with high phase;depth of field are able to provide a good point cloud, whereas a good selection(4) sequential of GCPs allows order to ofreduce the photograph significantly the strips. metric errors in the survey. After having properly treatedClear the pictures, pictures the with following high depth steps offollow field (as are already able todescribed provide in a the good previous point section cloud, fo whereasr laser a good scanner surveys, to which readers are referred for details): photographs alignment, building of the selection of GCPs allows to reduce significantly the metric errors in the survey. After having properly dense point cloud, realization of the mesh and of the texturized mesh. These elaborations require the treateduse of commercial the pictures, software the following such as Agisoft, steps followPix4D, (asContextCapture, already described etc. in the previous section for laser scanner surveys, to which readers are referred for details): photographs alignment, building of the dense3. Geo point-Structural cloud, Analysis realization on Point of the Clouds mesh and of the texturized mesh. These elaborations require the use ofThe commercial geo-structural software analysis such is carried as Agisoft, out through Pix4D, innovative ContextCapture, methods etc.that allow measurement of the attitude directly on the point clouds. Specific software perform analyses of the point clouds, 3. Geo-Structural Analysis on Point Clouds measuring the values of the normals associated to the identified planes, and how these adapt to aggregatesThe geo-structural of sets of adjacent analysis points is [57, carried58]. These out throughnormals define innovative the orientation methods of that the geometrical allow measurement ofbodies the attitudeto which directlythey belong on thein space, point and clouds. are in Specificturn expressed software by three perform spatial analyses coordinates. of the Thus, point clouds, measuringstarting from the thevalues point cloud of the and normals its DSM associated(in the case of to rock the masses identified coinciding planes, with and the how DTM) these, it is adapt to aggregates of sets of adjacent points [57,58]. These normals define the orientation of the geometrical bodies to which they belong in space, and are in turn expressed by three spatial coordinates. Thus, Appl. Sci. 2020, 10, 2960 6 of 25

Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 25 starting from the point cloud and its DSM (in the case of rock masses coinciding with the DTM), it necessaryis necessary to toestimate estimate the the anal analyticytic equations, equations, in in the the form form AX AX ++ BYBY ++ CZCZ ++ DD == 00 (plane (plane equation), equation), determdeterminingining orientation orientation and and position position in in the the space space of of the planes that better approximate locally the point cloud. The most most used used methods methods to defineto define and and measure measure these these planes planes are the are least the squares least and squares the geometric and the geometricsegmentation segmentation of DSM [59 of]. DSM The least[59]. squaresThe least method squares takes method into takes account into the account whole the set whole of available set of availabledata. However, data. However, it is possible it is that possible some of that the some identified of the planes identified might planes aggregate might points aggregate belonging points to belongingdifferent planes, to different so that theplanes, results so can that be the conditioned results can by grossbe conditioned errors, commonly by gross present errors, when commonly dealing presentwith experimental when dealing data. with This experimental problem might data. result This problem in invalidating might theresult overall in invalidating validity of the the overall model; validityin other words,of the model; there is in the other risk towords, have, there within is thethe set risk to to interpolate, have, within points the belonging set to interpolate, to another poi planents belonging(Figure3). to another plane (Figure 3).

Figure 3. Failure in the reduction to least squares for the estimation of data affected by gross error Figure(after [ 603.]). Failure in the reduction to least squares for the estimation of data affected by gross error (after [60]). The algorithm RANSAC [60,61], on the other hand, allows to identify the different planes and to evaluateThe algorithm the related RANSAC characteristic [60,61], on equations, the other also hand, in theallows presence to identify of asignificant the different percentage planes and of togross evaluate errors. the The related procedure characteristic followed equations, by the algorithm also in isthe completely presence diofff aerent signific byant those percentage in the usual of grossestimation errors. methods: The procedure instead followed of using initiallyby the algorithm the highest is possiblecompletely number different of data by tothose obtain in the a starting usual estimationsolution, from methods: which instead to distinguish of using the initially invalid the points, highest RANSAC possible usesnumber the of minor data possibleto obtain number a starting of solution,initial data from to generatewhich to thedistinguis model,h thenthe invalid trying topoints, enlarge RANSAC such set uses with the data minor coherent possible to number the model, of initialif possible. data to generate the model, then trying to enlarge such set with data coherent to the model, if possible.To clarify the procedure, we recall here the bidimensional case of the line to find within a set of experimentalTo clarify data the procedure, containing awe percentage, recall here also the significant,bidimensional of gross case of errors the line [62]: to find within a set of experimentalAn adequate data numbercontaining of testsa percentage, is carried also out through:significant, of gross errors [62]: An adequate number of tests is carried out through: - casual- extractioncasual extraction of n = 2 of data n = (minimum2 data (minimum necessary necessary to define to thedefine model); the model); - evaluation- evaluation of the deviations,of the deviati withons, respectwith respect to the to model: the model: the the points points that that are are located located within within a predefineda predefined threshold t (greenhreshold points (green in Figure points4 )in are Figure selected, 4) are and selected, the others and (red the pointsothers in(red Figure points4) are discarded.in Figure 4) are discarded. - The- setThe containing set containing the highest the highest number number of data representsof data represents the model: the model: - the- selected the selected model is model re-estimated is re-estimated at least squares at least bysquares using by all theusing points all the classified points asclassified inliers. as inliers. Repeating the procedure for i times we will obtain i set of points, each one generated by a different choice of the two initial points. Given a certain value of i, it can be stated that with a determined probability p at least one of the couples of initial points does not include an outlier. In the case more subsets of coherent data are present, the algorithm eliminates all those points belonging to the line that has the highest consensus (in red) and repeats the procedure of researching of the line which best approximates the remaining points (Figure 4; [62]). Appl. Sci. 2020, 10, 2960 7 of 25 Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 25

Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 25

FigureFigure 4. 4.Individuation Individuation ofof thethe interpolatinginterpolating line in in the the case case of of a a single single dataset dataset (a (),a ),and and of ofmul multipletiple datasetsdatasets (b ().b).

RepeatingAt this aim, the for procedure each model, for i timestests on we different will obtain portionsi set ofof points,DTM have each been one carried generated out byto look a diff forerent choicea threshold of the twovalue initial that is points. efficient, Given independently a certain valuefrom the of i ,average it can besize stated of portions, that with roughness a determined and probabilityundulationp atof leastthe wall. one of the couples of initial points does not include an outlier. InIn the detail, case morethe present subsets study of coherent was realized data areby using present, the the algorithm algorithm Shape eliminates Detection all of thoseRANSAC points Figurebelonging (RansacSD)4. Individuation to the in the line of formthe that interpolating proposed has the highest by line the inUniversity consensus the case of of (in aBonn red)single [61] and dataset, repeatsthat allows(a), theand to procedureof isolate multiple forms,of researching or a set datasetsofof the (b forms,). line which within best the approximates DSM (procedure the remaining carried out points with the(Figure open4;[ source62]). software Cloud Compare; CloudCompareAt this aim, forVersion each model,2.9.1 User tests Manual, on diff 2017)erent [57] portions. of DTM have been carried out to look for At this aim, for each model, tests on different portions of DTM have been carried out to look for a thresholdThus, valueto extrapolate that is e thefficient, planes independently interpolating aggregates from the averageof points sizewith ofequal portions, attitude, roughness an analysis and a threshold value that is efficient, independently from the average size of portions, roughness and undulationof the perpendicular of the wall. associated to the individual planes so determined is performed on the filtrated undulation of the wall. pointIn detail,cloud; this the allows present to studyindividuate was realized portions byof the using space the with algorithm similar orientation.Shape Detection A newof attrib RANSACute In detail, the present study was realized by using the algorithm Shape Detection of RANSAC (RansacSD)is then assigned in the to form the point proposed cloud byby associating the University the attitude of Bonn (d [ip/61d],ip that direction) allows to to the isolate perpendicular forms, or a (RansacSD) in the form proposed by the University of Bonn [61], that allows to isolate forms, or a set setof of each forms, individual within plane. the DSM (procedure carried out with the open source software Cloud Compare; of forms, withinAll the the DSM points (procedure having similar carried attitude out withwithin the a definedopen source variability software range Cloud are selected Compare; along the CloudCompare Version 2.9.1 User Manual, 2017) [57]. CloudComparegeological Version alignments 2.9.1 User of Manual,interest, aimed2017) [57].at interpolating the planes that better follow the distribution Thus, to extrapolate the planes interpolating aggregates of points with equal attitude, an analysis Thus, toof extrapolate the points [41,63, the planes64]. interpolating aggregates of points with equal attitude, an analysis of the perpendicular associated to the individual planes so determined is performed on the filtrated of the perpendicularThe parameters associated to to be the taken individual into account planes are so [65] determined: is performed on the filtrated point cloud; this allows to individuate portions of the space with similar orientation. A new attribute point cloud; this allows maximumto individuate number portions of points of the belonging space with to the similar same planeorientation. (function A newof the attribute point density); is then assigned to the point cloud by associating the attitude (dip/dip direction) to the perpendicular is then assigned to the pointtolerance cloud threshold by associating of the the distance attitude between (dip/dip the direction) selected to plane the perpendicular and the other points of each individual plane. of each individual plane.(function of the point cloud accuracy); All the points having similar attitude within a defined variability range are selected along the All the points having maximum similar deviationattitude withinof the vecto a definedr normal variability to the selected range plane;are selected along the geological alignments of interest, aimed at interpolating the planes that better follow the distribution geological alignments ofimposition interest, aimed of threshold at interpolating values, aimed the planes at finalizing that better the iterativefollow the procedure distribution to look for of the points [41the,63 most,64]. suitable planes, without considering those with greater error. This method is of the points [41,63,64]. The parameterssemi-automatic, to be taken with into manual account control are [65 and]: validation, since a process of manual selection The parameters to be taken into account are [65]: of the entities to model is at the origin of a mainly computational phase of recognition,  maximummaximum number number of of points points belonging belonging to to the the same same plane plane (function (function of of the the point point density); density); computation and conversion of the normals in geo-structural data, dictated by the  tolerance threshold of the distance between the selected plane and the other points toleranceexperience threshold ofof thethe distanceoperator who between keeps the a direct selected control plane on and the thedataset other of pointsoutcomes. (function of (functiontheEventually, point of cloud the the point accuracy); automatic cloud creationaccuracy); of the discontinuity plane interpolating the selected and near  points,maximummaximum and the deviation deviation extraction of of theof the the vector vector attitude normal normal for eachto to the the of selected selectedthem (computation plane; plane; of dip direction – dip) are  obtained.impositionimposition The of ofplanes threshold threshold thus values, individuated values, aimed aimed by at at thefinalizing finalizing average the the parameters iterative iterative procedure of dip and to dip looklook direction forfor the are most representedthesuitable most suitable planes, by means withoutplanes, of stereographicwithout considering considering those projections. with those greater The with discretization error.greater This error. method in This discontinuity method is semi-automatic, is sets is operatedsemi-automatic,with manual by means control with of a selectionmanual and validation, control of the poles and since validation,of a planes process through since of manual a windowsprocess selection of within manual of thewhich selection entities the value to model of dip/dipofis the at thedirectionentities origin to is ofmodel averaged. a mainly is at computationalthe origin of a phasemainly of computational recognition, computation phase of recognition, and conversion of computationthe normals inand geo-structural conversion data,of the dictated normals by in the geo-structural experience of thedata, operator dictated who by keeps the a direct 4.experience Casecontrol Studies on of the the dataset operator of outcomes. who keeps a direct control on the dataset of outcomes. Eventually, the automatic creation of the discontinuity plane interpolating the selected and near points, and 4.1.theEventually, S extractionanta Caterina the of automaticthe attitude creation for each of of the them discontinuity (computation plane of interpolating dip direction the – selecteddip) are and near obtained.points, The planesThe and rock the thus extractioncliff individuated over the of thebeach by attitude ofthe Santa average for Caterina each parameters of is them located (computation of in dip the andmunicipality dip of dip direction direction of Sant’Agnello are – dip) are representedobtained.(Naples by means province, The of planes stereographic northern thus individuatedcoast projectionof the Sorrento bys. theThe Peni average discretizationnsula), parameters in Campania in discontinuity of region dip and (Figure dip sets 5). direction is are operated representedby means of by a selection means of of stereographic the poles of projections.planes through The discretizationwindows within in discontinuity which the value sets of is operated dip/dip direction is averaged.

4. Case studies

4.1. Santa Caterina The rock cliff over the beach of Santa Caterina is located in the municipality of Sant’Agnello (Naples province, northern coast of the Sorrento Peninsula), in Campania region (Figure 5). Appl. Sci. 2020, 10, 2960 8 of 25 by means of a selection of the poles of planes through windows within which the value of dip/dip direction is averaged.

4. Case Studies

4.1. Santa Caterina The rock cliff over the beach of Santa Caterina is located in the municipality of Sant’Agnello (NaplesAppl. Sci. 20 province,20, 10, x FOR northern PEER REVIEW coast of the Sorrento Peninsula), in Campania region (Figure5). 8 of 25

FigureFigure 5. 5. LocationLocation of of the the Santa Santa Caterina Caterina cliff, cliff, in Campania, southern Italy.

Due to presence of a small marina, and to beauty of the area, which attracts a wide number of Due to presence of a small marina, and to beauty of the area, which attracts a wide number of tourists during the summer season, the beach is highly frequented. tourists during the summer season, the beach is highly frequented. From a geological-structural standpoint the Sorrento Plain represents a tectonic graben, between From a geological-structural standpoint the Sorrento Plain represents a tectonic graben, between two limestone horsts of Cretaceous age. The graben was filled during Wurm with materials from an two limestone horsts of Cretaceous age. The graben was filled during Wurm with materials from an explosive volcanic eruption, dated about 39,000 years ago [66], within the Phlegrean volcanic district. explosive volcanic eruption, dated about 39,000 years ago [66], within the Phlegrean volcanic district. The volcanic products were emplaced through huge, high temperature, pyroclastic surges and flows, The volcanic products were emplaced through huge, high temperature, pyroclastic surges and flows, that entirely filled the pre-existing morphologies. that entirely filled the pre-existing morphologies. These pyroclastic products belong to the formation of the Campanian Grey Tuff (or Campanian These pyroclastic products belong to the formation of the Campanian Grey Tuff (or Campanian Ignimbrite), consisting of yellow-greyish ashes, associated to black scoriae and lava fragments. After the Ignimbrite), consisting of yellow-greyish ashes, associated to black scoriae and lava fragments. After emplacement, the formation of sanidine and zeolite crystals (zeolitization processes) caused lithification the emplacement, the formation of sanidine and zeolite crystals (zeolitization processes) caused of the pyroclastic material. Cooling processes in the pyroclastic mass generated the development of lithification of the pyroclastic material. Cooling processes in the pyroclastic mass generated the sub-vertical and strongly inclined joints that, combined with the bedding, create the main discontinuity development of sub-vertical and strongly inclined joints that, combined with the bedding, create the planes, fragmenting the tuff rock mass. Concerning morphology, the slope is an approximately 50-m main discontinuity planes, fragmenting the tuff rock mass. Concerning morphology, the slope is an high rock cliff, the terrace of pyroclastic aggradation of the Sorrento Plain bounding toward the sea. approximately 50-m high rock cliff, the terrace of pyroclastic aggradation of the Sorrento Plain The rock cliff shows a retrogressive evolution through rapid slope movements with different bounding toward the sea. failure mechanisms (falls, toppling, slides, wedges). The cliff is directly hit by sea waves, and this action The rock cliff shows a retrogressive evolution through rapid slope movements with different produces a further erosional action, involving both mechanical action and weathering processes [67,68], failure mechanisms (falls, toppling, slides, wedges). The cliff is directly hit by sea waves, and this thus contributing to degrade the rock mass. Eventually, other negative factors working toward action produces a further erosional action, involving both mechanical action and weathering processes [67,68], thus contributing to degrade the rock mass. Eventually, other negative factors working toward instability of the cliff are represented by natural factors such as earthquakes, wind, rainfall, and the alteration in the first meters of slope exerted by the tree roots. Regarding the factors of anthropogenic origin, in the past the rock cliff was interested by quarrying activity to extract the tuff. These activities are testified by presence of artificial cavities and passages, where it is possible to observe a net decrease in the mechanical properties of the rock mass [69–73], with development of release tension cracks and widening in the aperture of the fissures. In general, the area is characterized by sub-vertical scarps in tuff deposits crossed by multiple discontinuity sets, which intersections determine the formation of isolated, potentially unstable, Appl. Sci. 2020, 10, 2960 9 of 25 instability of the cliff are represented by natural factors such as earthquakes, wind, rainfall, and the alteration in the first meters of slope exerted by the tree roots. Regarding the factors of anthropogenic origin, in the past the rock cliff was interested by quarrying activity to extract the tuff. These activities are testified by presence of artificial cavities and passages, where it is possible to observe a net decrease in the mechanical properties of the rock mass [69–73], with development of release tension cracks and widening in the aperture of the fissures. In general, the area is characterized by sub-vertical scarps in tuff deposits crossed by multiple discontinuity sets, which intersections determine the formation of isolated, potentially unstable, slabs, wedges, and blocks. Dimensions of the rock blocks are widely variable, from cubic decimeters to cubic meters, in function of the spacing of the main discontinuity sets within the rock mass. Due to the relevant socio-economical interest of the area and to site logistics, it was decided to proceed with an evaluation of the stability conditions through a geo-structural analysis on point cloud acquired with a combined approach through the use of both laser scanner and drone. Such integrated approach was necessary in order to overcome the logistic and morphological problems present at the site, since the tuff cliff is definitely active, and, as such, not accessible from below for long sectors. First, a laser survey by means of RIEGL VZ400 was carried out, through acquisition of three scan positions for a total number of 12 scans. Following the preliminary visits to the study area, useful to check the site logistics and its features, including the presence of people and its frequency during the different hours of the day, the survey was planned in order to schedule the best temporal windows, with the aim at avoiding any possible interference. Further, the planning phase included the choice of the precise locations for the control targets. In this specific case, three georeferenced flat targets (4 cm diameter plates) and four cylinder targets (10 cm diameter) were used. The laser survey consisted of two phases. The first was carried out during the early morning at the pier located east of the cliff, by performing two scans at 80 m distance from the cliff, for a temporal duration of 1 h and 45 min. This procedure allowed to acquire a 2000 pt/m2 point cloud. Then, a third scan was acquired from the beach, 50 m far from the cliff. This second phase lasted 50 min, and resulted in the acquisition of a point cloud with density of 2000 pt/m2. The two phases of survey allowed to characterize the eastern side only of the cliff. Then, to get a point cloud adequate to be worked on with geo-structural aims, it should had been necessary to locate the scanner in frontal position with respect to the main discontinuity systems of the rock mass, aimed at avoiding the presence of “dark points” within the point cloud. In the specific case, however, it was not possible to have the scanner in the western sector of the cliff, due to logistic conditions. For this reason, the laser scanner survey was then integrated with a photogrammetric survey by SAPR, carried out by means of the UAV Italdron 4HSE PRO. For this survey, too, the acquisition phase was preceded by inspection visits to investigate the best conditions of light exposition of the cliff, and to establish the strategies to avoid interference with seagulls. The survey was carried out at noon, since this time during the day was the best to guarantee direct light on the cliff, and absence of shadow areas. It was performed in two flights, each one lasting 20 min, to take both frontal and zenithal views of the cliff. The UAV survey, beside covering the western side of the study area (not acquired by the LST survey), overlapped also part of the LST point cloud, eventually resulting in a detailed cloud with density of about 3200 pt/m2. Regarding this latter survey, to obtain a high-detailed pixel resolution at the ground, needed to perform geo-structural analysis on point clouds, the flight was carried out at maximum distance of 40 m from the cliff. This allowed to obtain a ground resolution of 5.6 mm per pixel. During the survey, both zenithal and oblique images were produced, with overlapping percentage always greater than 80–85%. Flight characteristics are as follows:

Sensor size 35.8 23.9 mm • × Average distance from the cliff: 40 m • Ground resolution: 5.6 mm/px • Overlap H %: 80% • Appl. Sci. 2020, 10, 2960 10 of 25

Overlap L %: 80% • Photo height (m): 27.31 m • Photo width (m): 40.91 m • Photo spacing (m): 5 m • Interval (m): 16 m • In order to obtain a high-definition, properly geo-referenced, point cloud (indispensable condition toAppl. perform Sci. 2020 geo-structural, 10, x FOR PEER REVIEW analysis on discontinuity of the rock mass), the manual modality of10 flight of 25 was chosen. This was because experience has demonstrated the higher reliability of this modality againstof flight the was automatic chosen. one.This Itwas was because also decided experience to acquire has demonstrated photos with high the higherdepth of reliability field; therefore, of this themodality shoot ofagainst the camera the automatic was set withone. ISOIt was priority, also decided keeping to blocked acquire both photos aperture with andhigh time depth during of field; the flight.therefore, In addition, the shoot the of flightthe camera direction was wasset with chosen ISO according priority, tokeeping attitude blocked of the both main aperture discontinuities and time of theduring rock the cliff flight.. In addition, the flight direction was chosen according to attitude of the main discontinuitiesGeoreference of the of realrock andcliff. fake waypoints was carried out. Fake waypoints are represented by a multitudeGeoreference of points of real within and the fake georeferenced waypoints was laser-acquired carried out. cloud,Fake waypoints well identifiable are represented on the screen. by a Thismultitude allowed of points to operate within sophisticated the georeferenced roto-translational laser-acquired alignment cloud, well procedures identifiable with on the the software screen. 3DReshaper,This allowed to to obtain operate a cloud sophisticated at very high roto resolution,-translational well alignment positioned procedures in space. with the software 3DReshaIn thisper, case to obtain study, a integrating cloud at very the high point resolution, cloud obtained well positioned by the laser in scannerspace. with that acquired by droneIn this was case possible study, thanksintegrating to the the very point scarce cloud presence obtained of by vegetation the laser onscanner the cli withff. Actually, that acquired when vegetationby drone was is present, possible the thanks photogrammetry to the very scarce by drone presence is not of able vegetation to identify on the underlyingcliff. Actually, surfaces, when divegetationfferently thanis present, the laser the scanner photogrammetry equipped withby drone last and is not first able impulse to identify technology, the underlying that are capable surfaces, to partlydifferently filtrate than the the vegetation. laser scanner equipped with last and first impulse technology, that are capable to partlyThe 3Dfiltrate geometrical the vegetation. model obtained through the combined laser scanner-drone survey produced a high-definitionThe 3D geometrical point cloud model consisting obtained through of 55.6 million the combined points (correspondinglaser scanner-drone to a survey point densityproduced of 6950a high pt-definition/m2), 15.5 point million cloud (2000 consisting pt/m2) out of of55. which6 million were points from (corresponding the laser scanner, to a andpoint 40.1 density million of (31606950 pt/m pt/m22)), 15 from.5 million the drone (2000 (Figure pt/m62)). out This of cloud,which properlywere from georeferenced the laser scanner, with GPSand and40.1 Totalmillion Station, (3160 allowedpt/m2) from to measure the drone the bedding(Figure 6). and This spacing cloud, of properly any plane georeferenced in the rock mass, with which GPS isand a crucial Total elementStation, forallowed the following to measure phase the ofbedding designing and thespacing stabilization of any plane works. in the rock mass, which is a crucial element for the following phase of designing the stabilization works.

Figure 6.6. TexturizedTexturized point point cloud cloud at atthe the Santa Santa Caterina Caterina cliff . cliff The. yellowThe yellow vertical vertical line provides line provides the height the ofheight the cli offf the. cliff.

The acquired point cloud underwent first a manual procedure of decimation and filtering to eliminate the spurious elements (vegetation, electric cables, trellis, etc.). The cleaned point cloud was then object of a second step of decimation, according to a grid defined in a way to maintain the sections representative of the rock mass. Then, the geo-structural analysis started on the point cloud by individuating portions of space with similar orientation, to extrapolate the value of attitude in terms of dip/dip direction by applying the algorithm RANSAC (Figure 7). The so identified planes were portrayed on stereonets, and at least five discontinuity sets were detected, summarized in Table 1 and shown in Figure 7. Appl.Appl. Sci. Sci. 20202020, 10, x10 FOR, 2960 PEER REVIEW 11 of 25 11 of 25

Table 1. Average attitude of the discontinuity sets at Santa Caterina rock cliff, identified through geo- structuralThe acquired analysis on point point cloud cloud.underwent first a manual procedure of decimation and filtering to eliminate the spurious elements (vegetation, electric cables, trellis, etc.). The cleaned point cloud Set Dip Direction Dip was then object of a second step of decimation, according to a grid defined in a way to maintain the K1 15 77 sections representative of theK2 rock mass. Then,308 the geo-structural76 analysis started on the point cloud by individuating portions of spaceK3 with similar orientation,72 to extrapolate77 the value of attitude in terms of dip/dip direction by applyingK4 the algorithm165 RANSAC (Figure775).

FigureFigure 7. RGB 7. RGB point point cloud cloud at Santa at Santa Caterina Caterina rock rock cliff. cli Above:ff. Above: mapping mapping the dip the direction dip direction value value attributedattributed to the to point the point cloud cloud over the over stud they area study. Below: area. representation Below: representation of the dip directions of the dip for directions each for individualeach individual discontinuity discontinuity set, distinguished set, distinguished by different by di colorsfferent; in colors; the inset, in the the inset, steroplot the steroplot shows shows distributiondistribution of the of thesets. sets.

ToThe verify so identifiedthe reliability planes of the were attitudes portrayed acquired on stereonets, semi-automatically and at least on the five point discontinuity cloud, some sets were discontinuitdetected,ies summarized in the tuff rock in Table mass1 andwere shown checked in on Figure site. 7In. detail, the main discontinuity sets were Appl. Sci. 2020, 10, 2960 12 of 25

Table 1. Average attitude of the discontinuity sets at Santa Caterina rock cliff, identified through geo-structural analysis on point cloud.

Set Dip Direction Dip K1 15 77 Appl. Sci. 2020, 10, x FOR PEER REVIEWK2 308 76 12 of 25 K3 72 77 identified, and traditional measureK4 stations were 165 casually performed 75 by means of the Clar compass. Average attitudes of the mainK5 measured discontinuity 110 systems thus 75obtained are reported in Table 2.

ToTable verify 2. Average the reliability attitudesof of thethe discontinuity attitudes acquired systems semi-automatically identified through geological on the pointmeasurements cloud, some discontinuitieson the rock inmass. the tuff rock mass were checked on site. In detail, the main discontinuity sets were identified, and traditional measureSet stationsDip were Direction casually performedDip by means of the Clar compass. Average attitudes of the main measuredK1 discontinuity17 systems thus89 obtained are reported in Table2. K2 128 88 Table 2. Average attitudes of theK3 discontinuity systems72 identified through88 geological measurements on the rock mass. K4 345 86 The above described procedure allowed to compare the structural data obtained with the two Set Dip Direction Dip different survey techniques [8], which is the traditional, manual, geomechanical survey, and the geo- structural survey from pointK1 cloud acquired w 17ith TLS and UAV techniques 89 (Figure 8). Comparing K2 128 88 the datasets, it appears that theK3 results obtained 72 from geo-structural 88survey from point cloud fit well those acquired directly on theK4 rock face. 345 86 Since the joints show mostly high dipping, variations in the discontinuity polarity are related to the often undulated pattern, which results in opposite sectors within the stereoplot (sets K2 and K4 The above described procedure allowed to compare the structural data obtained with the two in Figure 8). different survey techniques [8], which is the traditional, manual, geomechanical survey, and the As concerns the K5 set, this was only identified through geo-structural analysis on point cloud. geo-structural survey from point cloud acquired with TLS and UAV techniques (Figure8). Comparing This is because such system is rarely shown in the rock mass, practically consisting of very few joints the datasets, it appears that the results obtained from geo-structural survey from point cloud fit well with high spacing; therefore, the direct acquisitions in correspondence of a limited sector of the rock those acquired directly on the rock face. face, accessible on the ground, did not allow to point out its presence.

Figure 8. Stereoplot comparing the data from the two different techniques. Figure 8. Stereoplot comparing the data from the two different techniques.

4.2. Castellana Caves The Castellana Caves are located in the SE Murge of Apulia (southern Italy; Figure 9), and are one of the most famous Italian karst areas [74–76], with more than 300,000 tourists yearly, as the most visited show cave in Italy. Apulia backbone consists of Jurassic-Cretaceous limestones and dolostones, unconformably overlain Tertiary and Quaternary clastic carbonates, with stratigraphic successions varying in different parts of the region, depending upon paleo-geography of the individual sectors. Since the Lower Pleistocene, Apulia was subjected to a general uplifting, until Appl. Sci. 2020, 10, 2960 13 of 25

Since the joints show mostly high dipping, variations in the discontinuity polarity are related to the often undulated pattern, which results in opposite sectors within the stereoplot (sets K2 and K4 in Figure8). As concerns the K5 set, this was only identified through geo-structural analysis on point cloud. This is because such system is rarely shown in the rock mass, practically consisting of very few joints with high spacing; therefore, the direct acquisitions in correspondence of a limited sector of the rock face, accessible on the ground, did not allow to point out its presence.

4.2. Castellana Caves The Castellana Caves are located in the SE Murge of Apulia (southern Italy; Figure9), and are one of the most famous Italian karst areas [74–76], with more than 300,000 tourists yearly, as the most visited show cave in Italy. Apulia backbone consists of Jurassic-Cretaceous limestones and dolostones, unconformably overlain Tertiary and Quaternary clastic carbonates, with stratigraphic successions Appl. Sci. 2020, 10, x FOR PEER REVIEW 13 of 25 varying in different parts of the region, depending upon paleo-geography of the individual sectors. Since the Lower Pleistocene, Apulia was subjected to a general uplifting, until reaching its present reaching its present configuration, that is a blocky structure fragmented by high dip faults [77–79]. configuration, that is a blocky structure fragmented by high dip faults [77–79]. An extensive network An extensive network of underground caves and karst conduits, and widespread karst landforms at of underground caves and karst conduits, and widespread karst landforms at the surface as well, the surface as well, characterize the three main karst sub-regions (from north to south, Gargano, characterize the three main karst sub-regions (from north to south, Gargano, Murge, and Salento). Murge, and Salento).

Figure 9. LocationLocation of of the the Castellana Castellana Caves, in Apulia, southern Italy.

As in other other sectors sectors of of the the Apulian Apulian karst, karst, and and in inmany many other other karst karst areas areas worldwide worldwide [14,80 [14–,8880]–, 88the], thedevelopment, development, elongation elongation,, and and spatial spatial distribution distribution of ofkarst karst landforms landforms in in the the Castellana Castellana area, area, both at the surface and underground, isis stronglystrongly controlledcontrolled byby thethe mainmain tectonictectonic lineationslineations [[7755,,8989].]. First explored by Professor Franco Anelli in January 1938, the Castellana Caves were exploited as show cave soon after [[9090–92],], due to beauty beauty of the the underground underground passages and richness in the speleothems decorating the karst systems, whilst in the decades later the explorations added further passages to to the the overall overall development, development, that that therefore therefore reached reached a length a greaterlength than greater 3300 than m, with 3300 maximum m, with depthmaximum of 122depth m [93of ].−122 The m karst [93] system. The karst has asystem prevailingly has a sub-horizontalprevailingly sub pattern,-horizontal with widepattern, caverns with − wide caverns ranging in height from a few to some tens of meters, and intervening, structurally controlled corridors. The karst system at Castellana opens in the Upper Cretaceous Altamura Limestone formation, showing moderately spaced bedding planes [94]. The carbonate rock mass is fractured, with local arching and deformations in the strata, and thick weathered zones at the contact with clastic sediments or where the carbonates are wetted by trickling or condense water, and weathered material is protected against mechanical erosion [95]. The contact with fine-grained sediments is particularly important, since it contributes to provide the moisture required for dissolution. Corrosive moisture has been in fact invoked as the main reason for limestone weathering in several cases, including the drenching of clay pebble surfaces [96–98]. Besides bedding of strata, showing a sub-horizontal attitude, four main discontinuity systems can be identified at Castellana, the prevailing system always being in the range N 130–150 [99]. The most spectacular view of the system is the 55 m deep Grave, a natural opening created by a collapse sinkhole [18,100–103], through progressive falls from the vault. The name derives from the pre-Latin term grava, meaning pit or hole, and is locally used to indicate deep entrance to cave systems [104]. This type of feature, generally produced through collapse or cover-collapse sinkhole processes, is very common in the Apulian karst [74,75,105–107]. At Castellana, given the relevance of the Grave (the most iconic symbol of the Castellana Caves, with the typical picture showing the solar Appl. Sci. 2020, 10, 2960 14 of 25 ranging in height from a few to some tens of meters, and intervening, structurally controlled corridors. The karst system at Castellana opens in the Upper Cretaceous Altamura Limestone formation, showing moderately spaced bedding planes [94]. The carbonate rock mass is fractured, with local arching and deformations in the strata, and thick weathered zones at the contact with clastic sediments or where Appl.the carbonates Sci. 2020, 10, arex FOR wetted PEER REVIEW by trickling or condense water, and weathered material is protected against14 of 25 mechanical erosion [95]. The contact with fine-grained sediments is particularly important, since it raycontributes entering tothe provide sinkhole the at moisture middle morning), required for its dissolution.location above Corrosive parts of moisturethe tourist has path, been and in factthe needinvoked to evaluate as the main the stability reason for conditions, limestone th weatheringis site was insurveyed several cases,by means including of the thelaser drenching scanner RIEGL of clay VZ400pebble (Figure surfaces 1). [96 –98]. DuringBesides bedding the survey, of strata, a high showing-density a sub-horizontal geo-referenced attitude, point cloud four main (430, discontinuity000 million points systems, with can 21be,500 identified pt/m2 atdensity Castellana,) was the produced prevailing (Figure system 10). always The survey being in consisted the range of N 42 130–150 scans [ from99]. 20 scan positionsThe most(eight spectacular located at the view surface, of the 12 system within is the the cavern). 55 m deep SeveralGrave different, a natural resolution opening scans created were by produceda collapse at sinkhole each survey [18,100 station,–103], throughand the overall progressive amount falls of from data thewas vault. processed The namethrough derives dedicated from software.the pre-Latin In order term tograva obtain, meaning frontal pitscans or hole,of the and discontinuity is locally used sets, to seven indicate out of deep the entrance12 scans towithin cave thesystems Grave [ 104were]. Thistaken type with of the feature, laser inclined generally 90° produced with respect through to the collapse vertical oraxis cover-collapse, by means of sinkholethe “Tilt Mounting”processes, is tool very. common in the Apulian karst [74,75,105–107]. At Castellana, given the relevance of the GraveThe resulting(the most iconicpoint symbolcloud was of the of Castellana high defini Caves,tion, with also the thanks typical to picturepresence showing in the thecave solar of boardwalksray entering and the sinkholeramps for at the middle tourist morning), visits, that its made location it possible above parts to locate of the the tourist scanner path, at many and the points. need Into evaluatethis way, the all stabilitythe scans conditions, were produced this site at was distance surveyed not bygreater means than of the 30– laser40 m scanner from the RIEGL rock VZ400walls. Further(Figure1, ).the opening of the Grave allowed natural lighting inside the cave, thus obtaining good chromaticDuring pictures the survey,. At this a regard, high-density the survey geo-referenced was carried point out during cloud (430,000 early morning million and points, late afternoon,with 21,500 to pt avoid/m2 density) the direct was arrival produced of sun (Figure rays during 10). The the survey acquisition consisted phase of of 42 the scans photographs from 20 scan. positionsOverall, (eight the located survey at required the surface, two 12working within days the cavern).: on the Severalfirst day, di fftheerent 10 resolutioncylinder targets scans were surveyedproduced by at eachGPS and survey total station, station, and and the the overall surface amount scans outside of data the was cave processed were performed through dedicated (16 scans oversoftware. eight Inscan order-position to obtains), for frontal a total scans time of 6 the h; discontinuityon the second sets,day, seven26 scans out within of the the 12 scanscavern within were carriedthe Grave outwere in 8 takenh, also with surveying the laser by inclinedtotal station 90◦ thewith 15 respect flat target to thes. The vertical elaboration axis, by process, means ofconsisting the “Tilt ofMounting” linking and tool. roto-translating the different point clouds, and in optimization by cleaning the dataset, produced the high-detail solid surface (mesh) to represent the geometry of the Grave (Figure 11).

Figure 10. RealReal numerical numerical model model of of the the point point cloud. cloud. The The blue blue vertical vertical line line provides provides the theheight height of the of cavern.the cavern.

The resulting point cloud was of high definition, also thanks to presence in the cave of boardwalks and ramps for the tourist visits, that made it possible to locate the scanner at many points. In this way, all the scans were produced at distance not greater than 30–40 m from the rock walls. Further, the opening of the Grave allowed natural lighting inside the cave, thus obtaining good chromatic Appl. Sci. 2020, 10, 2960 15 of 25 pictures. At this regard, the survey was carried out during early morning and late afternoon, to avoid the direct arrival of sun rays during the acquisition phase of the photographs. Overall, the survey required two working days: on the first day, the 10 cylinder targets were surveyed by GPS and total station, and the surface scans outside the cave were performed (16 scans over eight scan-positions), for a total time of 6 h; on the second day, 26 scans within the cavern were carried out in 8 h, also surveying by total station the 15 flat targets. The elaboration process, consisting of linking and roto-translating the different point clouds, and in optimization by cleaning the dataset, produced the high-detail solid surface (mesh) to represent the geometry of the Grave (Figure 11). Appl. Sci. 2020, 10, x FOR PEER REVIEW 15 of 25

Figure 11.11. RGB point cloud of the Grave. To the right,right, heightheight ofof thethe bustbust dedicateddedicated toto Prof.Prof. Franco Anelli (discoverer(discoverer ofof thethe cavecave systemsystem inin 1938)1938) providesprovides anan indicationindication ofof scale.scale.

The geological-structuralgeological-structural analysis of the Grave was performed by direct measurement of the discontinuity sets in the point cloud throughthrough specificspecific software (RiscanPro, Cyclone, Cyclone 3DR, Cloud Compare) able to analyze its attributes,attributes, with particular regard to the values (described by three spatial coordinates) of thethe normalsnormals associatedassociated toto eacheach pointpoint withinwithin thethe cloud.cloud. TheThe semi-automaticsemi-automatic procedure started from identificationidentification of sectorssectors showing similar orientation, through analysis of thethe normals. The The same same procedure procedure described described in in the the Santa Santa Caterina Caterina cliff cliff case study was then followed, assigning a new attribute to the point cloud, computed by associating the attitude attitude ( (dipdip//ddipip d direction)irection) to the value of thethe normalnormal at eacheach point. In this semi semi-automatic-automatic method, the phases of identification, identification, computation,computation, and conversion of the normals in geological datum is preceded by a process of manual selectionselection (control and validation) of the elementselements to bebe modelled.modelled. This represents a very importantimportant point, allowingallowing thethe operatoroperator throughthrough hishis/her/her expertiseexpertise toto fullyfully controlcontrol thethe output.output. The planes identified identified we werere portrayed portrayed in in stereographic stereographic projections, projections, by by ranking ranking the the data data in insets sets of ofdiscontinuities. discontinuities. The The great great majority majority of ofthe the attitudes attitudes are are high high dipping; dipping; for for this this reason, variations inin discontinuity polarity, duedue toto thethe possibilitypossibility toto encounterencounter undulatedundulated planes,planes, werewere considered.considered. Three main sets sets of discontinuities,of discontinuities, plus the plu bedding,s the bedding, were identified were identified from the 113 from discontinuity the 113 measurements discontinuity (Tablemeasurements3). The graphic (Table representation 3). The graphic of representation the main discontinuity of the main planes discontinuity identified directly planes identified from the pointdirectly cloud from is the shown point in cloud Figure is 12 shown. The mostin Fig frequenture 12. The and most developed frequent sets and of tectonicdeveloped origin sets (namely, of tectonic K1 andorigin K2) (namely, are well K1 identified and K2) are by thewell statistical identified analysis by the statistical carried out analysis on the carried point cloud, out on as the shown point bycloud, the histogramas shown by presented the histogram in Figure presented 13. in Figure 13.

Table 3. Average attitude of the discontinuity sets at Castellana Caves, identified through geo- structural analysis on point cloud.

Set Dip Direction Dip K1 52 87 K2 151 85 K3 185 85 S’ 212 4 S’’ 35 3 Appl. Sci. 2020, 10, 2960 16 of 25

Table 3. Average attitude of the discontinuity sets at Castellana Caves, identified through geo-structural analysis on point cloud.

Set Dip Direction Dip K1 52 87 K2 151 85 K3 185 85 S’ 212 4 Appl. Sci. 2020, 10, x FOR PEER REVIEWS” 35 3 16 of 25 Appl. Sci. 2020, 10, x FOR PEER REVIEW 16 of 25

FigureFigure 12. 12. Representation Representation of of the the main main discontinuity discontinuity planes identified identified directlydirectly from from from the the point point cloud: cloud: (a) (alla all) allsets; sets; sets; ( (bb )() bbedding; bedding;) bedding; ( (cc ())c K1;) K1; K1; ( ( dd(d)) )K2. K2. K2.

FigureFigure 13. 13.Mapping Mapping the the dip dip direction directionvalue value attributedattributed to the point point cloud cloud of of the the GraveGrave atat Castellana Castellana Caves.FigureCaves. In13. the InMapping inset,the inset, the the histogram the dip histogram direction shows showsvalue distribution attributed distribution of the to discontinuity ofthe the point discontinuity cloud sets, of with the setsthe Grave, most with at represented theCastellana most clearlyCaves.represented corresponding In the clearly inset, corresponding the to peaks. histogram to shows peaks. distribution of the discontinuity sets, with the most represented clearly corresponding to peaks. DataData obtained obtained thro throughugh the the above above described described semi-automatic semi-automatic procedure procedure were checked were through checked throughtraditional traditional survey, by survey, operating by operatingtwo scanlines two along scanlines the vertical along walls the of vertical the Grave walls, with of geologist the Grave- , Data obtained through the above described semi-automatic procedure were checked through climbers (Figure 14) to properly measure the discontinuity parameters. Adopting the traditional survey, by operating two scanlines along the vertical walls of the Grave, with geologist- recommendations by the International Society for Rock Mechanics [108], all the relevant parameters climbers (Figure 14) to properly measure the discontinuity parameters. Adopting the to describe each discontinuity were measured along the scanlines, in order to fully characterize the recommendations by the International Society for Rock Mechanics [108], all the relevant parameters sets of discontinuity within the rock mass. The data so acquired (about 50) were portrayed in polar to describeequiareal eachprojection, discontinuity and analyzed were throughmeasured a cluster along analysis. the scanlines, The field in dataorder allowed to fully to characterize identify three the setsmain of discontinuity sets beside the within bedding, the plusrock a mass. few random The data discontinuities so acquired (Table (about 4). 50) were portrayed in polar equiarealThe projection, data from and the traditionalanalyzed through geo-mechanical a cluster survey analysis. were The compared field data with allowed those toobtained identify from three mainthe sets digital beside survey the frombedding, cloud plus point a fewacquired random by TLS.discontinuities The comparison (Table resulted 4). in a good agreement betweenThe data the from different the traditionaltechniques geo(Fig-uremechanical 15), with surveyslight differences were compared probably with related those to obtained the logistic from the digital survey from cloud point acquired by TLS. The comparison resulted in a good agreement between the different techniques (Figure 15), with slight differences probably related to the logistic Appl. Sci. 2020, 10, 2960 17 of 25

Appl. Sci. 2020, 10, x FOR PEER REVIEW 17 of 25 with geologist-climbers (Figure 14) to properly measure the discontinuity parameters. Adopting the recommendations by the International Society for Rock Mechanics [108], all the relevant parameters to difficulties in performing the survey and taking geological measurements along the overhanging describe each discontinuity were measured along the scanlines, in order to fully characterize the sets of sectors ofdiscontinuity the rock walls. within the rock mass. The data so acquired (about 50) were portrayed in polar equiareal As regardsprojection, the and K4 analyzed set, this through is mostly a cluster highlighted analysis. The by field lineations data allowed at the to identify roof, threewith mainscarce sets presence along thebeside rock the mass bedding, (walls), plus aand few th randomerefore discontinuities it did not (Tablecome4 ).out from the point cloud geo-structural analysis.

Figure 14.Figure Acquisition 14. Acquisition phase phase of geo of geo-mechanical-mechanical data with with standardized standardized techniques techniques (scanline (scanline by by geologistgeologist rock climbers). rock climbers). Table 4. Average attitude of the discontinuity sets at the Grave entrance, identified by means of the Table 4. classicalAverage geomechanical attitude of surveythe discontinuity in rock walls. sets at the Grave entrance, identified by means of the classical geomechanical survey in rock walls. Set Dip Direction Dip SetK1 Dip D 231irection 86Dip K1K2 231 309 8586 K3 24 87 K2K4 309 51 4685 K3S’ 20824 887 K4S” 33351 646 S’ 208 8 The data from the traditionalS’’ geo-mechanical333 survey were compared6 with those obtained from the digital survey from cloud point acquired by TLS. The comparison resulted in a good agreement between the different techniques (Figure 15), with slight differences probably related to the logistic difficulties in performing the survey and taking geological measurements along the overhanging sectors of the rock walls.

Figure 15. Stereoplot showing the comparison among the data from geo-structural analysis from point cloud and direct acquisition from standard geomechanical survey.

5. Discussion Appl. Sci. 2020, 10, x FOR PEER REVIEW 17 of 25 difficulties in performing the survey and taking geological measurements along the overhanging sectors of the rock walls. As regards the K4 set, this is mostly highlighted by lineations at the roof, with scarce presence along the rock mass (walls), and therefore it did not come out from the point cloud geo-structural analysis.

Figure 14. Acquisition phase of geo-mechanical data with standardized techniques (scanline by geologist rock climbers).

Table 4. Average attitude of the discontinuity sets at the Grave entrance, identified by means of the classical geomechanical survey in rock walls.

Set Dip Direction Dip K1 231 86 K2 309 85 K3 24 87 K4 51 46 Appl. Sci. 2020, 10, 2960 S’ 208 8 18 of 25 S’’ 333 6

Figure 15. Stereoplot showing the comparison among the data from geo-structural analysis from point Figure 15. Stereoplot showing the comparison among the data from geo-structural analysis from point cloud and direct acquisition from standard geomechanical survey. cloud and direct acquisition from standard geomechanical survey. As regards the K4 set, this is mostly highlighted by lineations at the roof, with scarce presence along 5. Discussion the rock mass (walls), and therefore it did not come out from the point cloud geo-structural analysis.

5. Discussion The two case studies presented in this paper regard a very common situation in long stretches of the Italian coastlines (high cliffs interested by rockfalls and slope failures, threatening tourist sites below) and in wide areas of the country where underground voids are present, of both natural (karst caves) or artificial (man-made cavities) origin. Rock failures and sinkholes are definitely among the most significant geological hazards in a fragile territory as Italy. Very often, studying sites affected by these processes has several difficulties related to logistics (overhanging walls, danger from detachment of rocks, etc.) that make it more complicated to carry out a thorough analysis, aimed at defining the kinematics of possible slope movements, at performing the most proper stability analysis, and at designing the related stabilization works. The traditional approaches to assess the quality of rock masses, as the main available geomechanical classification, proposed since many decades in the international literature [109–117], still appear to be important but present many problems when applied in logistically difficult situations. The fieldwork campaign, necessary for implementation of these classifications, is expensive and time-consuming, and typically does not allow to entirely cover the area under study. Further, it requires somewhat repetitive actions, often resulting in the possibility of having incorrect data. To effectively reach the goal to evaluate the stability conditions in these situations through detailed analyses, based upon a sound amount of reliable data, the use of remote techniques may play a crucial role in the identification of the discontinuity sets characterizing the rock mass, but need to be carefully planned and always integrated by the necessary geo-structural and geomechanical checks on site [12,118]. We described implementation and integration of techniques used for the above aims, and applied them to study two sites where terrestrial laser scanner and unmanned aerial vehicles were used individually or in combination to build a cloud point from which to develop a semi-automatic extraction of discontinuity data to use for stability analyses. At Santa Caterina cliff, a combination of the two techniques was necessary to ensure a complete coverage of the area under study, at the same time obtaining high quality photographs of the cliff. In the case of Castellana, a variety of scan Appl. Sci. 2020, 10, 2960 19 of 25

positions to occupy (thanks to the tourist pathways), and good light coming from the outside through the natural opening, permitted to use TLS only. Through implementation of the two techniques, further integrated by other experience from the literature, and from many years of direct expertise from the authors, we were able to identify the main advantages of these approaches, but also some drawbacks that are worthy of further investigation in the future. As regards the LST technique, a very positive feature is represented by the high accuracy of the survey (in our case <5 mm, by using Riegl VZ400), together with the relative velocity in elaboration of the point cloud. On the other hand, drawbacks are the chromatic outcome of photographs, due to the technique used (spherical panchromatic pictures), and the common difficulty in moving around the Appl. Sci. 2020object, 10, x to FOR be PEER surveyed, REVIEW due to site logistics. This latter problem might potentially result7 of in 25 “loosing” some discontinuity sets, depending upon the relative geometries among their attitudes and location of the scan positions. The two drawbacks above can be solved by using UAV techniques: orthophotos are in this case of good chromatic quality, and there is no problem in moving around the object, which allows to survey the discontinuity plans always with a frontal view. Other problems, however, do still exist, the main one being the presence of “holes” in shadow areas; further, UAV do not typically go through the vegetation, so that in forested areas the percentage of uncovered zones might become significant (this latter drawback being in part common to laser scanner surveys as well). The first problem is not easily solved [119]: even paying attention to the best time during the day for flying, depending upon the light and cliff exposure conditions, there might be sectors of the areas always in the shade (overhanging walls, etc.). A possibility, which however requires longer times, in both acquisition and elaboration phases, is using data from two or more flights, taken at different hours and in different Figurelight 4. Individuation conditions. of the interpolating line in the case of a single dataset (a), and of multiple datasets (bVegetated). areas, on the other hand, can be a real obstacle when it is too dense to obtain an accurate view of the ground. Using Lidar sensors is probably the best way to overcome such drawback. At this aim, for each model, tests on different portions of DTM have been carried out to look for Other negative aspects about UAV are the impossibility to use it in no-fly zones (densely urbanized a threshold value that is efficient, independently from the average size of portions, roughness and areas, landing corridors near airports, military areas, etc.); lower accuracy (1–2 to some cm, depending undulation of the wall. upon technical features of the camera and distance from the object), also in function of the impossibility In detail, the present study was realized by using the algorithm Shape Detection of RANSAC to keep a constant distance from the cliff; survey heavily conditioned by the meteoric and climatic (RansacSD) in the form proposed by the University of Bonn [61], that allows to isolate forms, or a set conditions (wind, humidity, sun light, etc.); long elaboration times for post-processing, requiring robust of forms, within the DSM (procedure carried out with the open source software Cloud Compare; and highly performing hardware (most recent generation of i7 processor, 64 Gb RAM, 6 Gb dedicated CloudCompare Version 2.9.1 User Manual, 2017) [57]. video card). Thus, to extrapolate the planes interpolating aggregates of points with equal attitude, an analysis Given all the above considerations about individual LTS and UAV surveys, we are convinced that of the perpendicular associated to the individual planes so determined is performed on the filtrated integrating the point clouds acquired by UAV to that produced from TLS allows to get a complete 3D point cloud; this allows to individuate portions of the space with similar orientation. A new attribute cloud: this may eventually include also data difficult to be acquired from fixed measurement positions is then assigned to the point cloud by associating the attitude (dip/dip direction) to the perpendicular (a feature typical of TLS acquisitions), such as horizontal terraces, flat roofs of buildings, and areas of each individual plane. hidden by obstacles. The integration is also extremely useful in those cases where it is not possible to All the points having similar attitude within a defined variability range are selected along the perform surveys from the land, or to reach the upper sectors of rock faces, and the top of mountains, geological alignments of interest, aimed at interpolating the planes that better follow the distribution ridges, and hills, due to complex morphology and topography. of the points [41,63,64]. Based upon our experiences, we conclude that: The parameters to be taken into account are [65]:  maximumDiscontinuity number analysis of points by means belonging of remote to the sensing same plane techniques (function results of the in goodpoint outcomesdensity); through  tolerancethe semi-automatic threshold of extraction the distance of data,between thus the allowing selected to plane solve and significant the other logistic points problems (functionencountered of the by point the classical cloud accuracy); survey methods;  maximumIt is possible deviation to perform of the avector geo-structural normal to characterizationthe selected plane; of the whole area to study, rather  impositionthan proceeding of threshold site per values, site. Thisaimed results at finalizing in a relevant the iterative increase procedure in number to of look data, for useful for theprobabilistic most suitable and planes, statistical without elaborations; considering those with greater error. This method is semi-automatic,The combined procedure with manual allows control to provide and validation, a large amount since a of process high-resolution of manual data selection about a variety ofof the parameters entities to that model are is fundamental at the origin for of thea mainly proper computational design of stabilization phase of works.recognition, Among these, computation and conversion of the normals in geo-structural data, dictated by the experience of the operator who keeps a direct control on the dataset of outcomes. Eventually, the automatic creation of the discontinuity plane interpolating the selected and near points, and the extraction of the attitude for each of them (computation of dip direction – dip) are obtained. The planes thus individuated by the average parameters of dip and dip direction are represented by means of stereographic projections. The discretization in discontinuity sets is operated by means of a selection of the poles of planes through windows within which the value of dip/dip direction is averaged.

4. Case studies

4.1. Santa Caterina The rock cliff over the beach of Santa Caterina is located in the municipality of Sant’Agnello (Naples province, northern coast of the Sorrento Peninsula), in Campania region (Figure 5). Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 25

Figure 4. Individuation of the interpolating line in the case of a single dataset (a), and of multiple datasets (b).

At this aim, for each model, tests on different portions of DTM have been carried out to look for a threshold value that is efficient, independently from the average size of portions, roughness and undulation of the wall. In detail, the present study was realized by using the algorithm Shape Detection of RANSAC (RansacSD) in the form proposed by the University of Bonn [61], that allows to isolate forms, or a set of forms, within the DSM (procedure carried out with the open source software Cloud Compare; CloudCompare Version 2.9.1 User Manual, 2017) [57]. Thus, to extrapolate the planes interpolating aggregates of points with equal attitude, an analysis of the perpendicular associated to the individual planes so determined is performed on the filtrated point cloud; this allows to individuate portions of the space with similar orientation. A new attribute is then assigned to the point cloud by associating the attitude (dip/dip direction) to the perpendicular of each individual plane. Appl. Sci. 2020, 10, 2960 20 of 25 All the points having similar attitude within a defined variability range are selected along the geological alignments of interest, aimed at interpolating the planes that better follow the distribution of the points [41,63,64].in particular, spacing and persistence of the discontinuities are two main key features in the The parametersestimation to be of taken the block into account volume; are [65]:  maximumPossibility number to highlight of points the mostbelonging significant to the sectorssame plane of the (function investigated of the areas, point where density); to carry out  tolerancegeomechanical threshold scanlines of the by distance geologists between/rock climbers, the selected in order plane to directly and the acquire other parameterspoints such (functionas Joint Compressiveof the point cloud Strength, accuracy); filling materials, etc.;  maximumThe combined deviation use of the UAV vector and normal TLS is recommendedto the selected plane; wherever the logistics do not allow to  impositionestablish TLS of threshold scan positions values, at diaimedfferent at locations,finalizing and, the iterative even more, procedure for frontal to viewslook for toward the thediscontinuity most suitable planes; planes, without considering those with greater error. This method is semi-automatic,Increase in the safetywith manual of the operators control and working validation, in the since field; a process of manual selection ofLess the timeentities required to model for datais at acquisitionthe origin of (this a mainly is a very computational significant, if phase not strategic, of recognition, point in settings computationas tunnels and and quarries); conversion of the normals in geo-structural data, dictated by the experienceSignificant of reduction the operator in the who costs. keeps a direct control on the dataset of outcomes. Eventually, the automatic creation of the discontinuity plane interpolating the selected and near points, and theTo extraction effectively of reach the attitude all the abovefor each goals of them it is crucial(computation to develop of dip a dedicateddirection – design dip) are of the scan obtained.positions, The planes aimed thus at individuated avoiding as muchby the asaverage possible parameters that some of of dip the discontinuityand dip direction sets mightare result representedhidden by frommeans the of observation stereographic points, projection and to haves. The the possibilitydiscretization to validate in discontinuity the data by sets means is of direct operated surveyby means in a of limited a selection number of the of accessiblepoles of planes points. through windows within which the value of dip/dip directionOverall, is averaged. it is particularly worth pointing out the significance of obtaining reliable geo-structural parameters from remote techniques: in detail, the most robust parameters are represented by attitude, 4. Case studiespersistence, spacing, and aperture (this latter depending upon the laser scanner used); roughness, on the other hand, definitely represents a still uncertain feature. Further, advantages in reaching higher 4.1. Santalevels Caterina of safety for operators, saving time and costs of work, and allowing coverage of the total area to study represent definitely important goals, which encourage toward integrating different techniques The rock cliff over the beach of Santa Caterina is located in the municipality of Sant’Agnello whenever this is possible. (Naples province, northern coast of the Sorrento Peninsula), in Campania region (Figure 5). Combination with other techniques, too, might be an option, especially in the case of low budget research projects. For instance, the use of digital cameras at high resolution, or in specific cases of thermal cameras, could add further precious information, especially to cover the aforementioned “holes” from UAV surveys, and to get valuable data about thermal changes due to flowing of water and/or dilation of the rock mass.

Author Contributions: Conceptualization, B.P. and M.P. (Mario Parise); methodology, M.P. (Marco Pagano), B.P., A.R. and M.P. (Mario Parise); software, M.P. (Marco Pagano); validation, M.P. (Marco Pagano) and A.R.; data curation, M.P. (Marco Pagano) and A.R.; writing—original draft preparation, M.P. (Marco Pagano) and A.R.; writing—review and editing, M.P. (Mario Parise); supervision, B.P. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Fookes, P.G. Geology for engineers: The geological model, prediction and performance. Q. J. Eng. Geol. 1997, 30, 293–424. [CrossRef] 2. Miroshnikova, L.S. Engineering-Geologic models as an effective method of schematization of rock masses for purposes of hydrotechnical construction. Hydrotechn. Constr. 1999, 33, 603–612. [CrossRef] 3. Barla, G.; Barla, M. Continuum and discontinuum modelling in tunnel engineering. Gallerie Grandi Opere Sotter. 2000, 61, 15–35. 4. Jing, L.; Hudson, J.A. Numerical methods in rock mechanics. Int. J. Rock Min. Sci. 2002, 39, 409–427. [CrossRef] 5. Jing, L. A review of techniques, advances and outstanding issues in numerical modelling for rock mechanics and rock engineering. Int. J. Rock Min. Sci. 2003, 40, 283–353. [CrossRef] 6. Garcıa-Jerez, A.; Navarro, M.; Alcali, F.J.; Luzon, F.; Perez-Ruiz, J.A.; Enomoto, T.; Vidal, F.; Ocana, E. Shallow velocity structure using joint inversion of array and h/v spectral ratio of ambient noise: The case of Mula town (SE of Spain). Soil Dyn. Earthq. Eng. 2007, 27, 907–919. [CrossRef] Appl. Sci. 2020, 10, 2960 21 of 25

7. Stavropoulou, M.; Exadaktylos, G.; Saratsis, G. A combined threedimensional geological—geostatistical—numerical model of underground excavations in rock. Rock Mech. Rock Eng. 2007, 40, 213–243. [CrossRef] 8. Palma, B.; Ruocco, A.; Lollino, P.; Parise, M. Analysis of the behaviour of a carbonate rock mass due to tunneling in a karst setting. In The Present and Future of Rock Engineering, Proceedings of the 7th Asian Rock Mechanics Symposium, Seoul, Korea, 15–19 October 2012; Han, K.C., Park, C., Kim, J.D., Jeon, S., Song, J.J., Eds.; Korean Society for Rock Mechanics: Seoul, Korea, 2012; pp. 772–781. 9. Palma, B.; Parise, M.; Reichenbach, P.; Guzzetti, F. Rock-fall hazard assessment along a road in the Sorrento Peninsula, Campania, southern Italy. Nat. Hazards 2012, 61, 187–201. [CrossRef] 10. Lisjak, A.; Grasselli, G. A review of discrete modeling techniques for fracturing processes in discontinuous rock masses. J. Rock Mech. Geotech. Eng. 2014, 6, 301–314. [CrossRef] 11. Andriani, G.F.; Parise, M.; Diprizio, G. Uncertainties in the application of rock mass classification and geomechanical models for engineering design in carbonate rocks. In Engineering Geology for Society and Territory, 5. Urban Geology, Sustainable Planning and Landscape Exploitation; Lollino, G., Manconi, A., Guzzetti, F., Culshaw, M., Bobrowsky, P., Luino, F., Eds.; Springer: Berlin, Germany, 2015; Volume 5, pp. 545–548. 12. Andriani, G.F.; Parise, M. On the applicability of geomechanical models for carbonate rock masses interested by karst processes. Environ. Earth Sci. 2015, 74, 7813–7821. [CrossRef] 13. Parise, M.; Ravbar, N.; Živanovic, V.; Mikszewski, A.; Kresic, N.; Mádl-Szonyi, J.; Kukuric, N. Hazards in Karst and Managing Water Resources Quality. In Karst Aquifers—Characterization and Engineering; Stevanovic, Z., Ed.; Professional Practice in Earth Sciences; Springer: Berlin, Germany, 2015; pp. 601–687. 14. Palmer, A.N. Origin and morphology of limestone caves. Geol. Soc. Am. Bull. 1991, 103, 1–21. [CrossRef] 15. Palmer, A.N. Cave Geology; Cave Books: Dayton, OH, USA, 2007. 16. Waltham, A.C. The engineering classification of karst with respect to the role and influence of caves. Int. J. Speleol. 2002, 31, 19–35. [CrossRef] 17. Ford, D.C.; Williams, P. Karst Hydrogeology and Geomorphology; John Wiley and Sons: Chichester, UK, 2007. 18. Parise, M. Rock failures in karst. In Landslides and Engineered Slopes, Proceedings of the 10th International Symposium on Landslides, Xi’an, China, June 30–4 July 2008; Cheng, Z., Zhang, J., Li, Z., Wu, F., Ho, K., Eds.; Taylor and Francis Group: London, UK, 2008; pp. 275–280. 19. Andriani, G.F.; Parise, M. Applying rock mass classifications to carbonate rocks for engineering purposes with a new approach using the rock engineering system. J. Rock Mech. Geotechn. Eng. 2017, 9, 364–369. [CrossRef] 20. Milanovic, P.T. Geological Engineering in Karst; Zebra: Belgrade, Yugoslavia, 2000; p. 347. 21. Milanovic, P. The environmental impacts of human activities and engineering constructions in karst regions. Episodes 2002, 25, 13–21. [CrossRef][PubMed] 22. Stevanovic, Z.; Jemcov,I.; Milanovic, S. Management of karst aquifers in Serbia for water supply. Environ. Geol. 2007, 51, 743–748. [CrossRef] 23. Parise, M.; Closson, D.; Gutierrez, F.; Stevanovic, Z. Anticipating and managing engineering problems in the complex karst environment. Environ. Earth Sci. 2015, 74, 7823–7835. [CrossRef] 24. Rosser, N.J.; Petley, D.N.; Lim, M.; Dunning, S.; Allison, R.J. Terrestrial laser scanning for monitoring the process of hard rock coastal cliff erosion. Q. J. Eng. Geol. Hydrogeol. 2005, 38, 363–375. [CrossRef] 25. Oppikofer, T.; Jaboyedoff, M.; Blikra, L.; Derron, M.H.; Metzger, R. Characterization and monitoring of the Åknes rockslide using terrestrial laser scanning. Nat. Hazards Earth Syst. Sci. 2009, 9, 1003–1019. [CrossRef] 26. Viero, A.; Teza, G.; Massironi, M.; Jaboyedoff, M.; Galgaro, A. Laser scanning based recognition of rotational movements on a deep-seated gravitational instability: The Cinque Torri case (North-Eastern Italian Alps). Geomorphology 2010, 122, 191–204. [CrossRef] 27. Riquelme, A.; Tomás, R.; Abellán, A. Characterization of rock slopes through slope mass rating using 3D point clouds. Int. J. Rock Mech. Min. Sci. 2016, 84, 165–176. [CrossRef] 28. Chen, J.; Zhu, H.; Li, X. Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud. Comput. Geosci. 2016, 95, 18–31. [CrossRef] 29. Gomes, R.K.; de Oliveira, L.P.L.; Gonzaga, L.; Tognoli, F.M.W.; Veronez, M.R.; de Souza, M.K. An algorithm for automatic detection and orientation estimation of planar structures in LiDAR-scanned outcrops. Comput. Geosci. 2016, 90, 170–178. [CrossRef] Appl. Sci. 2020, 10, 2960 22 of 25

30. Riquelme, A.; Cano, M.; Tomás, R.; Abellán, A. Identification of Rock Slope Discontinuity Sets from Laser Scanner and Photogrammetric Point Clouds: A Comparative Analysis. Procedia Eng. 2017, 191, 838–845. [CrossRef] 31. Rengers, N. Terrestrial Photogrammetry: A valuable tool for engineering geological purposes. Rock Mech. Eng. Geol. 1967, 5, 150–154. 32. Feng, Q.; Sjögren, P.; Stephansson, O.; Jing, L. Measuring fracture orientation at exposed rock faces by using a non-reflector total station. Eng. Geol. 2001, 59, 133–146. [CrossRef] 33. Slob, S.; Hack, R. 3D terrestrial laser scanning as a new field measurement and monitoring technique. Lect. Notes Earth Sci. 2004, 104, 179–189. 34. Slob, S.; van Knapen, B.; Hack, R.; Turner, K.; Kemeny, J. Method for automated discontinuity analysis of rock slopes with three-dimensional laser scanning. J. Transp. Res. Board 2005, 1913, 187–194. [CrossRef] 35. Slob, S.; Hack, H.; Feng, Q.; Röshoff, K.; Turner, A. Fracture Mapping using 3D Laser Scanning Techniques. In Proceedings of the 11th Congress of the International Society for Rock Mechanics, Lisbon, Portugal, 9–13 July 2007; pp. 299–302. 36. Abellán, A.; Vilaplana, J.M.; Martínez, J. Application of a long-range terrestrial laser scanner to a detailed rockfall study at Vall de Núria (Eastern Pyrenees, Spain). Eng. Geol. 2006, 88, 136–148. [CrossRef] 37. Sturzenegger, M.; Stead, D. Close-range terrestrial digital photogrammetry and terrestrial laser scanning for discontinuity characterization on rock cuts. Eng. Geol. 2009, 106, 163–182. [CrossRef] 38. Sturzenegger, M.; Stead, D. Quantifying discontinuity orientation and persistence on high mountain rock slopes and large landslides using terrestrial remote sensing techniques. Nat. Hazards Earth Syst. Sci. 2009, 9, 267–287. [CrossRef] 39. García-Sellés, D.; Falivene, O.; Arbués, P.; Gratacos, O.; Tavani, S.; Muñoz, J.A. Supervised identification and reconstruction of near-planar geological surfaces from terrestrial laser scanning. Comput. Geosci. 2011, 37, 1584–1594. [CrossRef] 40. Gigli, G.; Casagli, N. Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds. Int. J. Rock Mech. Min. Sci. 2011, 48, 187–198. [CrossRef] 41. Sturzenegger, M.; Stead, D.; Elmo, D. Terrestrial remote sensing-based estimation of mean trace length, trace intensity and block size/shape. Eng. Geol. 2011, 119, 96–111. [CrossRef] 42. Jaboyedoff, M.; Oppikofer, T.; Abellán, A.; Derron, M.H.; Loye, A.; Metzger, R.; Pedrazzini, A. Use of LIDAR in landslide investigations: A review. Nat. Hazards 2012, 61, 5–28. [CrossRef] 43. Brodu, N.; Lague, D. 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology. ISPRS J. Photogramm. Remote Sens. 2012, 68, 121–134. [CrossRef] 44. James, M.R.; Robson, S. Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application. J. Geophys. Res. Earth Surf. 2012, 117, F03017. [CrossRef] 45. James, M.R.; Robson, S. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf. Proc. Landf. 2014, 39, 1413–1420. [CrossRef] 46. Lato, M.J.; Vöge, M. Automated mapping of rock discontinuities in 3D lidar and photogrammetry models. Int. J. Rock Mech. Min. Sci. 2012, 54, 150–158. [CrossRef] 47. Abellán, A.; Oppikofer, T.; Jaboyedoff, M.; Rosser, N.J.; Lim, M.; Lato, M.J. Terrestrial laser scanning of rock slope instabilities. Earth Surf. Proc. Landf. 2014, 39, 80–97. [CrossRef] 48. Abellán, A.; Derron, M.H.; Jaboyedoff, M. ‘Use of 3D Point Clouds in Geohazards’ Special Issue: Current Challenges and Future Trends. Remote Sens. 2016, 8, 130. 49. Riquelme, A.; Abellán, A.; Tomás, R.; Jaboyedoff, M. A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Comput. Geosci. 2014, 68, 38–52. [CrossRef] 50. Riquelme, A.; Abellán, A.; Tomás, R. Discontinuity spacing analysis in rock masses using 3D point clouds. Eng. Geol. 2015, 195, 185–195. [CrossRef] 51. Lague, D.; Brodu, N.; Leroux, J.J. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (NZ). ISPRS J. Photogramm. Rem. Sens. 2013, 82, 10–26. [CrossRef] 52. Fernández, O. Obtaining a best fitting plane through 3D georeferenced data. J. Struct. Geol. 2005, 27, 855–858. [CrossRef] Appl. Sci. 2020, 10, 2960 23 of 25

53. Jaboyedoff, M.; Metzger, R.; Oppikofer, T.; Couture, R.; Derron, M.; Locat, J.; Turmel, D. New insight techniques to analyze rock-slope relief using DEM and 3D-imaging cloud points: COLTOP-3D software. In Proceedings of the 1st Canada–US Rock Mechanics Symposium, Vancouver, BC, Canada, 27–31 May 2007. 54. Ester, M.; Kriegel, H.P.; Sander, J.; Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, 2–4 August 1996; pp. 226–231. 55. Lato, M.; Diederichs, M.S.; Hutchinson, D.J. Bias correction for view-limited Lidar scanning of rock outcrops for structural characterization. Rock Mech. Rock Eng. 2010, 43, 615–628. [CrossRef] 56. Khoshelham, K.; Altundag, D.; Ngan-Tillard, D.; Menenti, M. Influence of range measurement noise on roughness characterization of rock surfaces using terrestrial laser scanning. Int. J. Rock Mech. Min. Sci. 2011, 48, 1215–1223. [CrossRef] 57. Girardeau-Montaut, D. CloudCompare (version 2.8) [GPL Software], OpenSource Project. 2016. Available online: http://www.danielgm.net/cc/ (accessed on 14 November 2019). 58. Dewez, T.J.B.; Girardeau-Montaut, D.; Allanic, C.; Rohmer, J.F. A CloudCompare plugin to extract geological planes from unstructured 3D point clouds. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, XLI-B5, 799–804. [CrossRef] 59. Roncella, R.; Forlani, G.; Remondino, F. Photogrammetry for geological applications: Automatic retrieval of discontinuity orientation in rock slopes. In Proceedings of the International Society for Optics and Photonics, San Diego, CA, USA, 12 May 2004. 60. Fischler, M.A.; Bolles, R.C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 1981, 24, 381–395. [CrossRef] 61. Schnabel, R.; Wahl, R.; Klein, R. Efficient RANSAC for Point-Cloud Shape Detection. Comput. Graph. Forum Citeseer 2007, 26, 214–226. [CrossRef] 62. Project INTERREG III ALCOTRA; Rockslidetec—Développement d’outils Méthodologiques Pour la Détection et la Propagation des Éboulements de Masse. Final Report. 2002–2006. Available online: http://www.risknat.org/ projets/rockslidetec/ (accessed on 14 November 2019). 63. Olariu, M.I.; Ferguson, J.F.; Aiken, C.L.; Xu, X. Outcrop fracture characterization using terrestrial laser scanners: Deep-water Jackfork sandstone at Big Rock Quarry, Arkansas. Geosphere 2008, 4, 247–259. [CrossRef] 64. Vöge, M.; Lato, M.J.; Diederichs, M.S. Automated rockmass discontinuity mapping from 3-dimensional surface data. Eng. Geol. 2013, 164, 155–162. [CrossRef] 65. Tarsha-Kurdi, F.; Landes, T.; Grussenmeyer, P. Extended RANSAC algorithm for automatic detection of building roof planes from Lidar data. Photogramm. J. Finland 2008, 21, 97–109. 66. De Vivo, B.; Rolandi, G.; Gans, P.B.; Calvert, A.; Bohrson, W.A.; Spera, F.J.; Belkin, A.E. New constraints on the pyroclastic eruption history of the Campanian volcanic plain (Italy). Mineral. Petrol. 2001, 73, 47–65. [CrossRef] 67. Pellegrino, A.; Prestininzi, A.; Scarascia Mugnozza, G. Construction of engineering-geology model of crystalline-metamorphic rock masses experiencing deep weathering processes: Example of application to the Allaro and Amusa river basin (Serre Massif, Calabria, Italy). Ital. J. Eng. Geol. Environ. 2008, 1, 33–60. 68. Calcaterra, D.; Parise, M. (Eds.) Weathering as a Predisposing Factor to Slope Movements. Geol. Soc. Lond. Eng. Geol. Spec. Publ. 2010, 23, 1–4. 69. Culshaw, M.G.; Waltham, A.C. Natural and artificial cavities as ground engineering hazards. Q. J. Eng. Geol. 1987, 20, 139–150. [CrossRef] 70. Parise, M.; Lollino, P. A preliminary analysis of failure mechanisms in karst and man-made underground caves in Southern Italy. Geomorphology 2011, 134, 132–143. [CrossRef] 71. Del Prete, S.; Parise, M. An overview of the geological and morphological constraints in the excavation of artificial cavities. In Proceedings of the 16th International Congress Speleology, Brno, Czech Republic, 21–28 July 2013; Filippi, M., Bosak, P., Eds.; Czech Speleological Society: Praha, Czech Republic, 2013; Volume 2, pp. 236–241. 72. Lollino, P.; Martimucci, V.; Parise, M. Geological survey and numerical modeling of the potential failure mechanisms of underground caves. Geosys. Eng. 2013, 16, 100–112. [CrossRef] 73. Fazio, N.L.; Perrotti, M.; Lollino, P.; Parise, M.; Vattano, M.; Madonia, G.; Di Maggio, C. A three-dimensional back analysis of the collapse of an underground cavity in soft rocks. Eng. Geol. 2017, 238, 301–311. [CrossRef] Appl. Sci. 2020, 10, 2960 24 of 25

74. Sauro, U. A polygonal karst in Alte Murge (Puglia, Southern Italy). Z. Geomorph. 1991, 35, 207–223. 75. Parise, M. Surface and subsurface karst geomorphology in the Murge (Apulia, southern Italy). Acta Carsologica 2011, 40, 79–93. [CrossRef] 76. Gueguen, E.; Formicola, W.; Martimucci, M.; Parise, M.; Ragone, G. Geological controls in the development of palaeo-karst systems of High Murge (Apulia). Rend. Online Soc. Geol. Ital. 2012, 21, 617–619. 77. Ricchetti, G.; Ciaranfi, N.; Luperto Sinni, E.; Mongelli, F.; Pieri, P. Geodinamica ed evoluzione sedimentaria e tettonica dell’Avampaese Apulo. Mem. Soc. Geol. Ital. 1988, 41, 57–82. 78. Bosellini, A.; Parente, M. The Apulia Platform margin in the Salento peninsula (southern Italy). Giorn. Geol. 1994, 56, 167–177. 79. Doglioni, C.; Mongelli, F.; Pieri, P. The Puglia uplift (SE Italy): An anomaly in the foreland of the Apenninic subduction due to buckling of a thick continental lithosphere. Tectonics 1994, 13, 1309–1321. [CrossRef] 80. Ford, D.C.; Ewers, R.O. The development of limestone cave systems in the dimensions of length and depth. Can. J. Earth Sci. 1978, 15, 1783–1798. [CrossRef] 81. Palmer, A.N. Patterns of dissolution porosity in carbonate rocks. In Karst Modeling; Palmer, A.N., Palmer, M.V., Sasowsky, I.D., Eds.; Karst Water Institute: Lewisburg, PA, USA, 1999; pp. 71–78. 82. Sebela, S.; Orndorff, R.C.; Weary, D.J. Geological controls in the development of caves in the south-central Ozarks of Missouri, USA. Acta Carsologica 1999, 28, 273–291. 83. Harrison, R.W.; Newell, W.L.; Necdet, M. Karstification along an Active Fault Zone in Cyprus; Kuniansky, E.L., Ed.; Water Res Invest Rep, 02-4174; USGS Karst Interest Group: Atlanta, GA, USA, 2002; pp. 45–48. 84. Klimchouk, A.; Andrejchuk, V. Karst breakdown mechanisms from observations in the gypsum caves of the Western Ukraine: Implications for subsidence hazard assessment. Int. J. Speleol. 2002, 31, 55–88. [CrossRef] 85. Bruno, E.; Calcaterra, D.; Parise, M. Development and morphometry of sinkholes in coastal plains of Apulia, southern Italy. Preliminary sinkhole susceptibility assessment. Eng. Geol. 2008, 99, 198–209. [CrossRef] 86. Iovine, G.; Parise, M.; Trocino, A. Breakdown mechanisms in gypsum caves of southern Italy, and the related effects at the surface. Zeit. Geomorph. 2010, 54 (Suppl. 2), 153–178. [CrossRef] 87. Basso, A.; Bruno, E.; Parise, M.; Pepe, M. Morphometric analysis of sinkholes in a karst coastal area of southern Apulia (Italy). Environ. Earth Sci. 2013, 70, 2545–2559. [CrossRef] 88. Pepe, M.; Parise, M. Structural control on development of karst landscape in the Salento Peninsula (Apulia, SE Italy). Acta Carsologica 2014, 43, 101–114. [CrossRef] 89. De Waele, J.; Parise, M. Discussion on the article “Coastal and inland karst morphologies driven by sea level stands: A GIS based method for their evaluation” by Canora F, Fidelibus D, Spilotro, G. Earth Surf. Proc. Landf. 2013, 38, 902–907. [CrossRef] 90. Anelli, F. First researches of the Italian Institute of Speleology in the Murge of Bari. Le Grotte d’Italia 1938, 2, 11–34. (In Italian) 91. Anelli, F. Guide to the excursion II. Bari-Alberobello-Selva di Fasano-Castellana Grotte-Bari. In Proceedings of the XVII Italian Congress Geography, Bari-Salerno, Italy, 23–29 April 1957; pp. 69–120. (In Italian). 92. Parise, M. Some considerations on show cave management issues in Southern Italy. In Karst Management; Van Beynen, P.E., Ed.; Springer: Berlin, Germany, 2011; pp. 159–167. 93. Parise, M.; Proietto, G.; Savino, G.; Tartarelli, M. Ripresa delle attività esplorative alle Grotte di Castellana: Primi risultati e prospettive future. Grotte e Dintorni 2002, 4, 179–186. 94. Lollino, P.; Parise, M.; Reina, A. Numerical analysis of the behavior of a karst cave at Castellana-Grotte, Italy. In Proceedings of the 1st International UDEC Symposium “Numerical Modeling of Discrete Materials”, Bochum, Germany, 29 September–1 October 2004; Konietzky, H., Ed.; Taylor and Francis Group: London, UK, 2004; pp. 49–55. 95. Zupan Hajna, N. Incomplete Solution: Weathering of Cave Walls and the Production, Transport and Deposition of Carbonate Fines. Carsologica, ZRC, Postojna-Ljubljana; Založba ZRC: Ljubljana, Slovenija, 2003. 96. Davis, G.D.; Mosch, C. Pebble indentations: A new speleogen from a Colorado Cave. Bull. Natl. Speleol. Soc. 1988, 50, 17–20. 97. Fookes, P.G.; Hawkins, A.B. Limestone weathering: Its engineering significance and a proposed classification scheme. Quart. J. Eng. Geol. 1988, 21, 7–31. [CrossRef] 98. Waltham, A.C.; Fookes, P.G. Engineering classification of karst ground conditions. Q. J. Eng. Geol. Hydrogeol. 2003, 3, 101–118. [CrossRef] Appl. Sci. 2020, 10, 2960 25 of 25

99. Parise, M.; Trisciuzzi, M.A. Geomechanical characterization of carbonate rock masses in underground karst systems: A case study from Castellana-Grotte (Italy). In Karst and Cryokarst; Tyc, A., Stefaniak, K., Eds.; Studies of the Faculty of EarthSciences, University of Silesia: Sosnowiec-Wroclaw, Poland, 2007; Volume 45, pp. 227–236. 100. Waltham, T.; Bell, F.; Culshaw, M. Sinkholes and Subsidence; Springer: Chichester, UK, 2005. 101. Parise, M.; Gunn, J. (Eds.) Natural and Anthropogenic Hazards in Karst Areas: Recognition, Analysis and Mitigation; special publication 279; Geological Society: London, UK, 2007. 102. Parise, M. Sinkholes. In Encyclopedia of Caves, 3rd ed.; White, W.B., Culver, D.C., Pipan, T., Eds.; Academic Press, Elsevier: London, UK, 2019; pp. 934–942. 103. Gutierrez, F.; Parise, M.; De Waele, J.; Jourde, H. A review on natural and human-induced geohazards and impacts in karst. Earth-Sci. Rev. 2014, 138, 61–88. [CrossRef] 104. Parise, M.; Federico, A.; Delle Rose, M.; Sammarco, M. Karst terminology in Apulia (southern Italy). Acta Carsologica 2003, 32, 65–82. [CrossRef] 105. Castiglioni, B.; Sauro, U. Large collapse dolines in Puglia (southern Italy): The cases of “Dolina Pozzatina” in the Gargano Plateau and of “Puli” in the Murge. Acta Carsologica 2000, 29, 83–93. [CrossRef] 106. Delle Rose, M.; Federico, A.; Parise, M. Sinkhole genesis and evolution in Apulia, and their interrelations with the anthropogenic environment. Nat. Hazards Earth Syst. Sci. 2004, 4, 747–755. [CrossRef] 107. Simone, O.; Fiore, A. Five large collapse dolines in Apulia (Southern Italy)—The Dolina Pozzatina and the Murgian Puli. Geoheritage 2014, 6, 291–303. [CrossRef] 108. ISRM. Suggested methods for the quantitative description of discontinuities in rock masses. Int. J. Rock Mech. Min. Sci. Geomech. Abs. 1978, 15, 319–368. 109. Barton, N.; Lien, R.; Lunde, J. Engineering classification of rock masses for design of tunnel support. Rock Mech. 1974, 6, 189–236. [CrossRef] 110. Barton, N.; Lien, R.; Lunde, J. Estimation of support requirement for underground excavation. In Proceedings of the 16th US Symposium Rock Mechanics, Minneapolis, MN, USA, 22–24 September 1975; University of Minnesota: Minneapolis, MN, USA, 1975; pp. 163–178. 111. Bieniawski, Z.T. Geomechanics classification of rock masses and its application in tunnelling. In Proceedings of the 3rd International Congress Rock Mechanics, Denver, CO, USA, 1–7 September 1974; Volume 2A, pp. 27–32. 112. Bieniawski, Z.T. Engineering Rock Mass Classifications; John Wiley and Sons: Chichester, UK, 1989. 113. Romana, M. New adjustment ratings for application of Bieniawski classification to slopes. In Proceedings of the International Symposium Role Rock Mechanics, Zacatecas, Mexico, 2–4 September 1985; pp. 49–53. 114. Hoek, E.; Brown, E.T. Practical estimates of rock mass strength. Int. J. Rock Mech. Mining Sci. Geomech. Abs. 1997, 34, 1165–1186. [CrossRef] 115. Hoek, E.; Marinos, P.; Benissi, M. Applicability of the geological strength index (GSI) classification for very weak and sheared rock masses. The case of the Athens Schist Formation. Bull. Eng. Geol. Environ. 1998, 57, 151–160. [CrossRef] 116. Marinos, P.; Hoek, E. Estimating the geotechnical properties of heterogeneous rock masses such as flysch. Bull. Eng. Geol. Environ. 2001, 60, 85–92. [CrossRef] 117. Tomás, R.; Delgado, J.; Serón Ganez, J.B. Modification of slope mass rating (SMR) by continuous functions. Int. J. Rock Mech. Min. Sci. 2007, 44, 1062–1069. [CrossRef] 118. Ferrero, A.M.; Forlani, G.; Roncella, R.; Voyat, H.I. Advanced geostructural survey methods applied to rock mass characterization. Rock Mech. Rock Eng. 2009, 42, 631–665. [CrossRef] 119. Lato, M.; Kemeny, J.; Harrap, R.M.; Bevan, G. Rock bench: Establishing a common repository and standards for assessing rockmass characteristics using LiDAR and photogrammetry. Comput. Geosci. 2013, 50, 106–114. [CrossRef]

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