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• W2510381844 abstract "ABSTRACT A slowly moving loess landslide along the River Danube in South Hungary was studied using electrical resistivity tomography. Our aim was to determine the fracture system of the study site. Due to the homogeneous composition of the loess, it seems to be the only possibility to get information about the landslide and its further evolution. The applicability of the electrical resistivity tomography technique for such a supposedly dense fracture system was studied by numerical modelling, and the results have been verified in the field. The dip of the fractures could not always been observed, and they could not be explored deeply. However, it was possible to map their surface projection to get the desired information about the structure of the landslide. Fracture zones could have been especially well localized, enabling the prediction of the positions of future rupture surfaces and thus the delineation of the endangered zone. Although the area outside of the already subsided one is not endangered yet, the area which has already started to move is going to break into two. Parts of the about 5 m wide blocks at the front of the landslide may fall or slide down anytime. A large area was assumed to move as one unit. Most of our predictions have been verified by the mass movements that occurred about one and half years after the measurements. The electrical resistivity tomography method proved to be a good tool to characterize the fracture system of such a landslide area, enabling the prediction of future rupture surfaces and also delineation of the endangered area. Its use is therefore highly recommended to monitor landslides to provide early risk warnings to avoid damage to constructions or endangering human life." @default.
• W2510381844 created "2016-09-16" @default.
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• W2510381844 date "2016-08-25" @default.
• W2510381844 modified "2023-10-18" @default.
• W2510381844 title "Prediction of the location of future rupture surfaces of a slowly moving loess landslide by electrical resistivity tomography" @default.
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• W2510381844 doi "https://doi.org/10.1111/1365-2478.12421" @default.
• W2510381844 hasPublicationYear "2016" @default.
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