Forecasting the time of failure of landslides at slope-scale is a difficult yet important task that can mitigate the effects of slope failures in terms of both human lives and economic losses. Common ...applications include public safety situations, where the risk is represented by dwellings built near active landslides or unstable cut slopes that threaten streets and railways, and open-pit mines, for which accurate warnings are fundamental to safeguard workers and simultaneously avoid unnecessary interruptions of the extraction activities.
The scientific literature is populated by many methods, guidelines and approaches regarding forecasting the time of failure or defining the conditions of imminent collapse. Thus, obtaining a synoptic view of the advantages and limitations of these different methodologies has become difficult. At the same time, innovations in technology have opened new possibilities to the application of such techniques, which are examined here.
This paper discusses and classifies these methods, addressing their respective differences and peculiarities to foster the usage even of less popular methods without overlooking the more scientific aspects and issues of landslide forecasting. Finally, an overview of the future trends and challenges is presented to contribute to the debate around this important topic.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The management of unstable slopes is one of the most critical issues when dealing with safety in open-pit mines. Suitable notice of impending failure events must be provided, and at the same time the ...number of false alarms must be kept to a minimum to avoid financial losses deriving from unnecessary outages of the production works. Comprehensive slope monitoring programs and early warning systems are usually implemented to this aim. However, systematic procedures for their tuning are lacking and several key factors are often overlooked. Therefore the mitigation of slope failure risk is still a topic of great concern, especially in open-pit mines excavated through hard rock masses featuring markedly brittle behavior, which supposedly provide little or no measurable precursors to failure. In this paper, 9 instabilities occurred at an undisclosed open-pit mine, and monitored by ground-based radar devices, were reviewed with the goal of characterizing the typical slope deformation behavior and defining the appropriate strategy for the setup of alarms. The estimated mass of the case studies ranged from 1500t to 750,000t. 5 instabilities culminated to failure, whereas the other 4, although showing considerable amounts and rates of movement, ultimately did not fail. The analysis provided critical insights into the deformation of hard rock masses of high geomechanical quality, and allowed the identification of “signature” parameters of the failure events. General operative recommendations for effective slope monitoring and early warning were consequently derived.
•Radar monitoring data of 9 instabilities at an open-pit mine were reviewed.•The analysis included 5 failures and 4 instabilities that did not reach failure.•Slopes in the pit are prone to very rapid tertiary creep and brittle failure.•“Signature” parameters of the failure events were identified.•Recommendations for effective monitoring and alarm set-up at the pit were derived.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Predicting the time of failure is a topic of major concern in the field of geological risk management. Several approaches, based on the analysis of displacement monitoring data, have been proposed in ...recent years to deal with the issue. Among these, the inverse velocity method surely demonstrated its effectiveness in anticipating the time of collapse of rock slopes displaying accelerating trends of deformation rate. However, inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity plots are processes that may be hampered by the noise present in the measurements; data smoothing is therefore a very important phase of inverse velocity analyses. In this study, different filters are tested on velocity time series from four case studies of geomechanical failure in order to improve, in retrospect, the reliability of failure predictions: Specifically, three major landslides and the collapse of an historical city wall in Italy have been examined. The effects of noise on the interpretation of inverse velocity graphs are also assessed. General guidelines to conveniently perform data smoothing, in relation to the specific characteristics of the acceleration phase, are deduced. Finally, with the aim of improving the practical use of the method and supporting the definition of emergency response plans, some standard procedures to automatically setup failure alarm levels are proposed. The thresholds which separate the alarm levels would be established without needing a long period of neither reference historical data nor calibration on past failure events.
Landslide displacement prediction is an essential component for developing landslide early warning systems. In the Three Gorges Reservoir area (TGRA), landslides experience step-like deformations ...(i.e., periods of stability interrupted by abrupt accelerations) generally from April to September due to the influence of precipitation and reservoir scheduled level variations. With respect to many traditional machine learning techniques, two issues exist relative to displacement prediction, namely the random fluctuation of prediction results and inaccurate prediction when step-like deformations take place. In this study, a novel and original prediction method was proposed by combining the wavelet transform (WT) and particle swarm optimization-kernel extreme learning machine (PSO-KELM) methods, and by considering the landslide causal factors. A typical landslide with a step-like behavior, the Baishuihe landslide in TGRA, was taken as a case study. The cumulated total displacement was decomposed into trend displacement, periodic displacement (controlled by internal geological conditions and external triggering factors respectively), and noise. The displacement items were predicted separately by multi-factor PSO-KELM considering various causal factors, and the total displacement was obtained by summing them up. An accurate prediction was achieved by the proposed method, including the step-like deformation period. The performance of the proposed method was compared with that of the multi-factor extreme learning machine (ELM), support vector regression (SVR), backward propagation neural network (BPNN), and single-factor PSO-KELM. Results show that the PSO-KELM outperforms the other models, and the prediction accuracy can be improved by considering causal factors.
The Three Gorges Hydropower Station is the largest hydropower station worldwide with the impoundment of the 660-km long reservoir. More than 500 landslides have been triggered by the reservoir water ...level fluctuation since the first impoundment in 2003. The classification of the reservoir affected landslide (seepage-driven and buoyancy-driven landslides) is crucial for landslide early warning and risk management. There are still no classification criteria for the reservoir landslide in TGRA. In this study, based on the long term in-situ monitoring, numerical simulation and field investigation methods, two typical reservoir landslide of Tangjiao landslide (seepage-driven) and Tanjiahe landslide (buoyancy-driven) in TGRA were taken as study cases. The comparative analysis of the response relationship between the long term deformation and the influencing factors were carried out. It can be found that the intensive deformation of Tangjiao landslide occurred during the rapid drawdown period of the reservoir water level, while Tanjiahe landslide has been deforming in the whole water year. Moreover, by analyzing the cumulative displacement curve, the permeability of the sliding mass, and the sliding surface of six reservoir landslides in TGRA, the classification criteria for seepage-driven and buoyancy-driven landslides in TGRA were proposed.
•The deformation characteristics of two reservoir landslides are analyzed by long term in-situ monitoring data.•The influencing mechanisms of reservoir level on seepage-driven and buoyancy-driven landslides are comparatively analyzed.•The classification criteria of seepage-driven and buoyancy-driven landslides in TGRA is proposed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Sinkholes represent a natural risk that may hit catastrophically without clearly detectible precursors. However, they are often overlooked by people and administrators. Therefore sinkhole monitoring ...and associated early warnings constitute important research topics but, currently, only a few papers about sinkhole prediction can be found. In this paper an experience of sinkhole monitoring and early warning with GB-InSAR is described. The latter is a highly precise instrument that is able to produce displacement maps with metric spatial resolution. The described activities were carried out on Elba Island (central Italy), where karstified limestone set off the occurrence of nine sinkholes since 2008, all within less than 3000m2, causing major damage to an important road and many indirect losses. In 1year of monitoring two deforming areas were detected, and the point where a sinkhole was about to propagate to the street level was predicted, thus permitting the preventive closure of the road. The deformation area was larger than the hole generated by the sinkhole, thus showing a subsidence that continued for a prolonged time even after the cavity was filled up. The occurrence of a 1.5-m-wide sinkhole, undetected by the GB-InSAR, also showed the lower detection limit of the instrument.
•On Elba Island (Italy) 9 sinkholes occurred since 2008 and represent a risk.•A GB-InSAR has been used for sinkhole monitoring and early warning.•The deformation measured permitted the forecast of the collapse of a sinkhole.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Background
The current availability of advanced remote sensing technologies in the field of landslide analysis allows for rapid and easily updatable data acquisitions, improving the traditional ...capabilities of detection, mapping and monitoring, as well as optimizing fieldwork and investigating hazardous or inaccessible areas, while granting at the same time the safety of the operators. Among Earth Observation (EO) techniques in the last decades optical Very High Resolution (VHR) and Synthetic Aperture Radar (SAR) imagery represent very effective tools for these implementations, since very high spatial resolution can be obtained by means of optical systems, and by the new generations of sensors designed for interferometric applications. Although these spaceborne platforms have revisiting times of few days they still cannot match the spatial detail or time resolution achievable by means of Unmanned Aerial Vehicles (UAV) Digital Photogrammetry (DP), and ground-based devices, such as Ground-Based Interferometric SAR (GB-InSAR), Terrestrial Laser Scanning (TLS) and InfraRed Thermography (IRT), which in the recent years have undergone a significant increase of usage, thanks to their technological development and data quality improvement, fast measurement and processing times, portability and cost-effectiveness. In this paper the potential of the abovementioned techniques and the effectiveness of their synergic use is explored in the field of landslide analysis by analyzing various case studies, characterized by different slope instability processes, spatial scales and risk management phases.
Results
Spaceborne optical Very High Resolution (VHR) and SAR data were applied at a basin scale for analysing shallow rapid-moving and slow-moving landslides in the emergency management and post- disaster phases, demonstrating their effectiveness for post-disaster damage assessment, landslide detection and rapid mapping, the definition of states of activity and updating of landslide inventory maps. The potential of UAV-DP for very high resolution periodical checks of instability phenomena was explored at a slope-scale in a selected test site; two shallow landslides were detected and characterized, in terms of areal extension, volume and temporal evolution. The combined use of GB-InSAR, TLS and IRT ground based methods, was applied for the surveying, monitoring and characterization of rock slides, unstable cliffs and translational slides. These applications were evaluated in the framework of successful rapid risk scenario evaluation, long term monitoring and emergency management activities. All of the results were validated by means of field surveying activities.
Conclusion
The attempt of this work is to give a contribution to the current state of the art of advanced spaceborne and ground based techniques applied to landslide studies, with the aim of improving and extending their investigative capacity in the framework of a growing demand for effective Civil Protection procedures in pre- and post-disaster initiatives. Advantages and limitations of the proposed methods, as well as further fields of applications are evaluated for landslide-prone areas.
Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the ...assessment of landslide magnitude and risk scenarios as well as in refining stability analyses. To assess an LSS is not an easy task and is generally time-consuming and expensive. In this work, a method existing in the literature, based on the inclination of movement vectors along a cross-section to estimate the depth and geometry LSSs, is used for the first time while exploiting satellite interferometric data. Given the advent of satellite interferometric data and the related increasing availability of spatially dense and accurate measurements, we test the effectiveness of this method—here named the vector inclination method (VIM)—to four case landslides located in Italy characterized by different types of movement, kinematics and volume. Geotechnical and geophysical information of the LSS is used to validate the method. Our results show that each of the presented cases provides useful insight into the validity of VIM using satellite interferometric data. The main advantages of VIM applied to satellite interferometry are that it enables estimation of the LSS with a theoretical worldwide coverage, as well as with no need for onsite instrumentation or even direct access; however, a good density of measurement points in both ascending and descending geometry is necessary. The combined use of VIM and traditional investigations can provide a more accurate LSS model.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Impending catastrophic failure of granular earth slopes manifests distinct kinematic patterns in space and time. While risk assessments of slope failure hazards have routinely relied on the ...monitoring of ground motion, such precursory failure patterns remain poorly understood. A key challenge is the multiplicity of spatiotemporal scales and dynamical regimes. In particular, there exist a precursory failure regime where two mesoscale mechanisms coevolve, namely, the preferred transmission paths for force and damage. Despite extensive studies, a formulation which can address their coevolution not just in laboratory tests but also in large, uncontrolled field environments has proved elusive. Here we address this problem by developing a slope stability analytics framework which uses network flow theory and mesoscience to model this coevolution and predict emergent kinematic clusters solely from surface ground motion data. We test this framework on four data sets: one at the laboratory scale using individual grain displacement data; three at the field scale using line-of-sight displacement of a slope surface, from ground-based radar in two mines and from space-borne radar for the 2017 Xinmo landslide. The dynamics of the kinematic clusters deliver an early prediction of the geometry, location and time of failure.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Rockfall risk is usually characterized by a high frequency of occurrence, difficulty in prediction (given high velocity, lack of noticeable forerunners, abrupt collapse, and complex mechanism), and a ...relatively high potential vulnerability, especially against people and communication routes. Considering that larger rockfalls and rockslides are generally anticipated by an increased occurrence of events, in this study, a framework based on microseismic monitoring is introduced for a temporal and spatial rockfall early warning. This approach is realized through the detection, classification, and localization of all the rockfalls recorded during a 6-month-long microseismic monitoring performed in a limestone quarry in central Italy. Then, in order to provide a temporal warning, an observable quantity of accumulated energy, associated to the rockfall rolling and bouncing and function of the number and volume of events in a certain time window, has been defined. This concept is based on the material failure method developed by Fukuzono-Voight. As soon as the first predicted time of failure and relative warning time are declared, all the rockfalls occurred in a previous time window can be located in a topographic map to find the rockfall susceptible area and thus to complement the warning with spatial information. This methodology has been successfully validated in an ex post analysis performed in the aforementioned quarry, where a large rockfall was forecasted with a lead time of 3 min. This framework provides a novel way for rockfall spatiotemporal early warning, and it could be helpful for activating traffic lights and closing mountain roads or other transportation lines using the knowledge of the time and location of a failure. Since this approach is not based on the detection of the triggering events (like for early warnings based on rainfall thresholds), it can be used also for earthquake-induced failures.
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