AMQ SR001 Wolter 139-148.pub A REVIEW OF METHODS USED TO INVESTIGATE STRUCTURAL CONTROL ON SLOPE INSTABILITY. Andrea Wolter Engineering Geology, GNS Science, Avalon, Lower Hutt, New Zealand. Corresponding author: A. Wolter ABSTRACT: Many features and phenomena, such as slope morphology, climate, hydrogeological and hydrological conditions, and material strength, contribute to slope instability. One of the most important preconditioning factors, particularly in rock slopes, is structural control. Structural control includes any tectonic processes or features that may influence landslide initiation, move- ment, or termination, including in situ stress conditions, discontinuities, faults, folds, and foliation. Structures affect not only the failure geometry, such as headscarp shape, but also deposit volume, morphology, block size, damage, and emplacement behav- iour. Structural features and processes thus influence all aspects of landslide behaviour, from the development of unstable condi- tions to deposition. Interestingly, mass movement studies can also highlight structures, and contribute to detailed mapping of pre- viously unrecognised faults, folds, and other features. Methods such as regional lineament mapping, traditional fieldwork, photog- raphy and photogrammetry, LiDAR surveys, InSAR interpretation, and numerical modelling are used to analyse structural features and processes related to slope instability. This short paper presents an overview of these methods and highlight their applications in a case study. Keywords: structural control, slope instability, methods, Vajont Landslide. � Available online http://amq.aiqua.it ISSN (print): 2279-7327, ISSN (online): 2279-7335 Alpine and Mediterranean Quaternary, 33 (2), 2020, 139-147 1. INTRODUCTION Structural geological features and processes, such as discontinuities, folds, faults, foliation, and in situ stresses, are recognised as important controls on slope instability, particularly in rock slopes (Agliardi et al., 2001; Stead and Wolter, 2015) (Fig. 1). In this context, discontinuities include pre-existing lineaments that may influence failure geometry and behaviour, whereas faults are lineaments that typically produce damage or shear zones of weaker material surrounded by more competent material (Loew et al., 2012; Milmo et al., 2014; Bonilla-Sierra et al., 2015). Fold axes are com- monly weak zones, and fold geometry may influence failure geometry (Badger, 2002; Jaboyedoff et al., 2011; Humair et al., 2013). Foliation may affect sliding zone development (Braathen et al., 2004; Adhikary and Dys- kin, 2007; Vick et al., 2020). The tectonic inheritance and in situ stress conditions of a given slope influence slope stability, and rock mass behaviour and damage (Hoek et al., 2009; Ambrosi and Crosta, 2011; Agliardi et al., 2013; Stead and Eberhardt, 2013; Elmo et al., 2018). Inclusion of structural control analysis when study- ing mass movements contributes to improved under- standing of failure preconditioning, initiation and behav- iour at various scales, and ultimately to landslide hazard and risk reduction. For example, Glastonbury and Fell (2000) and Stead et al. (2006) illustrated how structures affect failure mechanisms, from translational to complex multi-mechanism failure. Recent analysis of structural control within the creeping Moosfluh slope adjacent to the Aletsch glacier in Switzerland examines the applica- tion of sophisticated monitoring to determine the role of structural control in developing failure mechanisms and in slope stability evolution (Glueer et al, 2019a, b; Man- coni et al., 2019). In Troms, Norway, Vick et al. (2020) focus on Rock Slope Deformations (RSDs), highlighting the role of foliation, discontinuities, and faults in RSD formation and evolution and presenting a new geotech- nical model for these failures. Aside from recognising the importance of structural control as in the above examples, incorporating tech- niques traditionally used in structural geology into ge- otechnical assessments of slopes facilitates analysis and improves slope characterisation. For example, Ha- vaej and Stead (2016) applied the concept of the strain ellipsoid to brittle fracture and damage in open pit mines and natural slopes, determining an “ellipsoid of dam- age”. The aim of this short paper is to provide a review of methods used to investigate the influence of structural geological features and processes on slope instability. Methods are described in the next section, followed by a case study highlighting the application of several of these methods to the famous Vajont Landslide and a discussion of the methods. 2. METHODS Analysis of slope stability has incorporated numer- ous techniques to improve understanding of failure mechanisms, including site investigations, drilling, groundwater, displacement and climate monitoring, geo- https://doi.org/10.26382/AMQ.2020.05 140 Wolter A. Fig. 1 - Structural features and controls (such as folds, fault zones, rock bridges, crown cracks, transverse cracks and ridges, and radial cracks) on landslides, with methods used to investigate them represented by icons. See Tab. 1 for legend of icons. Tab. 1 - Summary of methods used to investigate structural features and processes affecting slope stability. Note that field methods here include laboratory methods for simplicity. GSI = Geological Strength Index; JRC = Joint Roughness Coefficient; LOS = Line-of-Sight. 141 Methods used for structural control on slope instability physical methods, remote sensing, and physical and numerical simulations. Figure 1 and Tab. 1 summarise the traditional and novel methods applied to the analysis of slope instabilities and their features, focussing on structural control and features, and separated into field methods, remote sensing and visualisation, and simula- tion and modelling. 3. CASE STUDY 3.1. Background The Vajont Landslide is a well-known ~270 million m3 event that failed catastrophically on October 9th, 1963 in the Dolomites of northern Italy, approximately 100 km north of Venice (Fig. 2). The failure initiated on the southern valley wall of the Vajont Valley on the flank of Mt. Toc (peak at 1921 m asl), above the Vajont Dam, which was the highest double-arch dam in the world at the time of the disaster. The sudden failure caused a displacement wave of the Vajont Reservoir that spread upvalley and downvalley, overtopped the dam, and flooded the main Piave Valley below. Resulting in just under 2000 deaths, the Vajont catastrophe is cited as one of the worst engineering and natural disasters in history. The landslide is among the most researched slope failures in the world, with over 200 studies on its geological, hydrogeological, geotechnical, and social aspects (cf. Genevois and Ghirotti, 2005; Superchi et al., 2010; Paronuzzi and Bolla, 2012; Genevois and Tecca, 2013). Several critical aspects, such as the sig- nificance of changing hydrological and hydrogeological conditions and centimetre-scale clay beds, and the ex- istence of a “paleoslide”, have been discussed for dec- ades (cf. Hendron and Patton, 1985; Semenza, 2010). Only recently have geomorphological and structural preconditions and regional evolution been investigated in relation to the Vajont Landslide. Several compressional deformation events shaped the Dolomites before and during the Late Miocene Al- pine orogeny, including the Neoalpine and Dinaric defor- mations. The E-W oriented Vajont Valley follows the Erto Syncline, an E-ESE plunging recumbent fold that formed during the Neoalpine event and was affected by the Belluno Thrust to the South and the Monte Borgá and Spesse thrusts to the North. The Vajont Landslide is located on the southern refolded limb of the Erto Syn- cline (Ravagnan, 2011; Massironi et al., 2013). The recently recognised open Massalezza Syn- cline (Massironi et al., 2013), with a fold hinge oriented N-S and located in the centre of the Vajont Landslide along the Massalezza Stream (Fig. 2), is associated with the Dinaric deformation event and creates the bowl shape observed in the landslide failure scar. Interfer- ence patterns between the Neoalpine and Dinaric fold generations contribute to the complex morphology of the failure scar (Bistacchi et al., 2015). Two faults bound the Vajont Landslide. The Col Tramontin Fault, a sub-vertical splay of the Croda Bian- ca reverse fault, acts as the eastern lateral release. The Col delle Erghene normal fault forms part of the western lateral and rear release of the landslide. Other faults surrounding the landslide include the Col delle Tosatte Fig. 2 - Location and context of the Vajont Landslide. 1 - Vajont Landslide failure scar, 2 - Vajont Landslide deposit, 3 - Vajont Dam, 4 - Pineda Landslide deposit (prehistoric failure), 5 - Col Tramontin Fault, 6 - Croda Bianca Fault, 7 - Col delle Erghene Fault, 8 - Massalezza Stream. (Modified after: Wolter et al., 2014.) reverse fault and the regionally significant Belluno flat- ramp-flat thrust system (Massironi et al., 2013). 3.2. Investigations on Structural Features and Con- trol at Vajont To study the effects of the tectonic setting on fail- ure kinematics and dynamics, engineering geological, structural geological, geophysical and geotechnical methods have been applied to the Vajont Landslide. For example, Bistacchi et al. (2015) constructed a 3D geo- logical model incorporating borehole, morphological, and geological data to characterise damage within the deposit as well as tectonic structures influencing the failure, and Petronio et al. (2016) used P-wave, SH- wave, and surface wave analysis to characterise the rock masses involved in the Vajont Landslide. Paronuzzi and Bolla (2015) investigated the interaction of pre- existing tectonic discontinuities with discontinuities formed due to gravitational stresses within the Vajont Landslide area, based on discontinuity orientation. Wolter et al. (2014, 2015) used terrestrial photogramme- try, engineering geomorphological analysis and map- ping, and engineering geological field investigations to characterise the Vajont Landslide scar and deposit. Through these studies focussed on structural con- trol, several insights have been gained. Detailed mor- phological and structural investigations of the failure scar - influenced mainly by interference patterns be- tween the two fold generations mentioned above - using methods such as photogrammetry, roughness charac- terisation, and block statistics, indicated smooth and rough areas of the scar, with implications for where rock bridges and concentrated damage could have formed within the sliding zone (Fig. 3) (Massironi et al., 2013; Wolter et al., 2014). Lineament mapping shows the influ- ence of tectonic discontinuities on the geometry of the failure. The roughness of the failure scar and the loca- tions of the Col Tramontin Fault and Massalezza Syn- cline also explain the separation of the landslide deposit into several blocks (e.g., Wolter et al., 2015). Continued 142 Fig. 3 - Roughness classes on the Vajont Landslide scar, based on block statistics. Class 1 represents areas that are largely planar and smooth, with roughness increasing to Class 4. Rougher areas are more likely to have caused dilation of the failure mass over asperities and/or rock bridge failure through asperities, with implications for movement behaviour. (Source: Wolter et al., 2014.) Wolter A. evolution of the failure scar post-1963 has focussed in a particularly active area under a fold visible in the failure scar (Wolter et al., 2014). The Vajont Landslide deposits include areas of compression and extension, as indicated by morphologi- cal features such as transverse ridges and internal shear zones on engineering geomorphological maps (Fig. 4) (Wolter et al., 2015). These deformed zones suggest further separation of the landslide mass into individual blocks, and they aid in determining the move- ment behaviour of the catastrophic landslide. Numerical modelling of the Vajont Landslide, in- cluding continuum, discontinuum, and hybrid simula- tions, has suggested that internal damage developed prior to catastrophic failure at critical locations within the rock mass, as also observed in morphological and struc- tural geological field investigations. Modelling suggests that strain concentrated within an ellipsoid of damage, 143 and certain pre-existing discontinuities likely separated the failure mass into several blocks (Wolter et al., 2013; Havaej et al., 2015). 4. DISCUSSION AND CONCLUSION Numerous methods have been developed to ana- lyse structural control on slope failures. The following discusses some of the caveats and limitations of meth- ods mentioned in this paper. Any data collection method must be employed with care and expert judgement, as subsequent analysis is only as good as the dataset input. For example, particu- larly when investigating structural features, structural domains within the study area should be considered. Scale of observation is also an important consideration. Structural geologists commonly examine features either at the regional or microscopic scale to determine tecton- Fig. 4 - Morphostructural features of the Vajont Landslide deposits, showing zones of extension and compression, as well as blocks within the deposit. (Source: Wolter et al., 2015.) Methods used for structural control on slope instability ic history. Although these scales of analysis provide context for slope-scale investigations, meso-scale fea- tures are often more significant to slope stability. In fact, slope investigations can identify previously unknown structures such as local folds and faults, as seen in the Vajont Landslide case above. Field (and laboratory) methods remain essential in assessing rock masses and discontinuities, as well as their role in failure kinematics and dynamics. It is only through these methods that properties such as material strength can be quantified. In recent years, using smartphone applications to measure discontinuity, fold, and foliation orientation, as well as collecting other geo- logical data, has started to displace the use of a com- pass. Apps allow much more efficient collection of data and can reduce time spent in precarious field environ- ments. However, their precision and accuracy have been debated (Vanderlip, 2016; Allmendinger et al., 2017; Lee et al., 2018; Nováková & Pavlis, 2019), and it is still highly recommended to check the accuracy of the app chosen and to calibrate apps using a compass fre- quently (up to one in every 10 app measurements). Understanding the theory behind the data being collect- ed is also important to avoid poor quality or erroneous data. Remote sensing applications allow data to be gath- ered in otherwise inaccessible areas and over large areas and have become widely used to investigate and monitor slopes. These methods require specialised knowledge and awareness of their limitations, some of which are listed in Tab. 1. Scaioni et al. (2014), Fran- cioni et al. (2017) and Stead et al. (2019) provide further review of remote sensing as applied to unstable slopes. Augmented and virtual reality techniques are rela- tively new to structural geology and slope investigations. They have proven to be useful tools in visualising the often multi-layered and complex datasets gathered us- ing other methods. Although not used to map or model phenomena directly yet, there are some promising de- velopments (e.g., Mysiorek et al., 2019). Numerical modelling, powerful when used appro- priately, should be seen as a conceptual tool to aid in understanding physical processes. Like remote sensing methods, numerical methods require highly specialised knowledge. With the increased development of user- friendly interfaces, it is particularly important to have well-defined research goals, and to know the limitations of the approach used as well as the fundamental sci- ence underlying each study. Stead and Wolter (2015) discuss numerical modelling as applied to structural control in slopes in more detail. The incorporation of Artificial Intelligence (AI), par- ticularly machine learning, into slope stability analysis is a relatively new development, and has allowed for more efficient processing of large datasets. Landslide suscep- tibility assessment currently applies AI most frequently. Studies such as Dickson & Perry (2016), who identify controls on coastal cliff stability using machine learning, and �uri� et al. (2019), using machine learning to classi- fy slopes as stable, dormant or active in Belgrade, none- theless show broader applications to slope stability. For a review of machine learning methods applied to struc- tural geology, see Gunderson et al. (2019). This paper has highlighted methods applied to slope investigations, focussed on the characterisation of structural features and processes that may control slope instability. 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