Tunnelling-induced landslides: The Val di Sambro tunnel case study

Bandini, Annalisa; Berry, Paolo; Boldini, Daniela

doi:10.1016/j.enggeo.2015.07.001

Cited by 59 publications

(17 citation statements)

References 21 publications

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“…In the formation of landslide, there are many factors play key role, some are natural factors, and some are human factors. Accroding to Bandini et al (2015), the human factors are very important and cannot be ignored (Bandini et al, 2015). It is also observed that climate change and landslide activities have some significant relationship (Sewell et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

et al. 2017

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This study proposed a hybrid modelling approach using two methods: support vector machines and random subspace to create a novel model namely Random SubSpace based Support Vector Machines (RSSVM) for landslide susceptibility assessment. The newly developed model was then tested at Wuning area, China to produce landslide susceptibility map. To achieve the objective of the study, first a spatial dataset was constructed that includes a landslide inventory map consisting of 445 landslide locations; and various landslide conditioning factors, such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), soil, lithology, normalized difference vegetation index (NDVI), land use, rainfall, distance to roads, distance to rivers, distance to faults. Next, the result of RSSVM model was validated using statistical index-based evaluations and receiver operating characteristic curve method. Next, to assess the performance of the proposed RSSVM model, a comparison analysis was performed with other existing methods such as ANN (Artificial Neural Network), NB (Naïve Bayes) and SVM (Support Vector Machine). In general, the performance of the RSSVM model showed higher than the rest of the models for spatial prediction of landslides. The AUC results of the applied models are: RSSVM (AUC=0.857), followed by MLP (AUC=0.823), SVM model (AUC=0.814) and NB model (AUC=0.783). The present study indicates that RSSVM can be used for spatial prediction of landslides and the result is very useful for the lcoal government and people lived in the Wuning area.

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Section: Introductionmentioning

confidence: 99%

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

et al. 2017

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“…Ground failures and ground instability hazards are globally widespread phenomena caused by natural geological and climatic processes such as landslides and slope movements (Di Martire et al, 2016a), soil volumetric changes in relation to dry and wet periods, soil/rock dissolution, oscillations of groundwater levels (Chaussard et al, 2014), seismic and volcanic activity (Lagios et al, 2013), neo-tectonic uplift or subsidence (Di Martire et al, 2016b) or induced by anthropogenic sources such as ground water pumping, (Modoni et al, 2013), inappropriate water management (Valipour et al, 2015 and references therein), gas and oil withdrawal, mining activity (Ferretti et al, 2011b), subsurface and surface engineering works (Bandini et al, 2015). They determine a significant number of human losses and injury as well as extensive economic damage to private and public properties.…”

Section: Introductionmentioning

confidence: 99%

Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis

Confuorto

Martire

Centolanza

et al. 2017

Remote Sensing of Environment

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Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40 mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.Peer ReviewedPostprint (author's final draft

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“…These can be found in other works that are specifically focused on these topics 11,13,14,24 . SqueeSAR has been used to investigate the spatial and temporal distribution of ground deformation in a wide range of fields related to geotechnical engineering, such as landslides 30,31 , slope stability in open-pit mines 32 , subsidence induced by groundwater overexploitation 33 or mining 34 , assessment of damage from tunneling or other excavation activities 35 , and stability of buildings and infrastructures 36 .…”

Section: Methodsmentioning

confidence: 99%

Perspectives on the prediction of catastrophic slope failures from satellite InSAR

Raspini¹,

Carlà²,

Intrieri³

et al. 2020

Preprint

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In many landslide studies, the possibility to predict future behaviour is still a major concern. To date, early-warning systems have mostly relied on the availability of detailed, high-frequency data from sensors installed in situ. Methods deducing reliable failure predictions have been largely applied at local scale, where in situ monitoring systems can be installed.The same purpose could not be chased through spaceborne monitoring applications, as these could not yield information acquired in sufficiently systematic fashion: the low data sampling frequency of most of the satellite systems hampered the possibility to retrieve the necessary details of tertiary creep characterized by accelerating deformation. So far, the lack of systematic information on ground displacement acquired at regional scale was another serious limit hampering the application of failure prediction methods at wide scale. Such limitations can be partially solved through the exploitation of new generation spaceborne platforms.The launch of Sentinel-1 mission opened a new opportunity for InSAR monitoring applications thanks to the increased acquisition frequency, the regularity of acquisitions and the policy on data access. We demonstrate the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) to identify precursors to catastrophic slope failures.Here we present three sets of Sentinel-1 constellation images processed by means of multi-interferometric analysis. We detect clear trends of accelerating displacement prior to the catastrophic failure of three large slopes of very different nature: an open-pit mine slope, a natural rock slope in alpine terrain, and a tailings dam embankment. We determine that these events could have been located several days or weeks in advance. The results highlight that satellite InSAR may now be used to support decision making and enhance predictive ability for this type of hazard.

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Tunnelling-induced landslides: The Val di Sambro tunnel case study

Cited by 59 publications

References 21 publications

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis

Perspectives on the prediction of catastrophic slope failures from satellite InSAR

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