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Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales
Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales
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Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales
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Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales
Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales

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Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales
Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales
Journal Article

Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales

2017
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Overview
Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies.