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

Abstract

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

A multi-scale methodological approach for slow-moving landslide risk mitigation in urban areas, southern Italy

“…The main goal of the proposed multi-scale methodological approach is mitigating the risk to urban areas affected by slow-moving landslides by way of the progressive and integrated planning (at medium scale, 1:25,000), scheduling (at large scale, 1:5000) and design (at detailed scale, > 1:2000) of structural/non structural interventions. Given a scale of analysis, the above approach generally involves (1) characterizing the slow-moving landslides that are representative of a homogeneous geological context (Gullà et al 2017b) and (2) retrieving the relationships between the cause (changes in value of the adopted landslide intensity measure) and the effect (severity of damage suffered by buildings and infrastructure at risk) Nicodemo et al 2017;Palmisano et al 2018;Peduto et al 2017Peduto et al , 2018.…”

“…In this regard, a rich archive of SAR data (i.e., images) covering a long time interval (more than 25 years) is currently available, with a spatial resolution ranging from high (e.g., the first generation radar sensors such as ERS1-2, ENVISAT, RADAR-SAT1, those still in orbit as RADARSAT2 and the Sentinel-1 mission operating at C-band) to very high (e.g., the COSMO-SkyMed and TerraSAR-X missions, both operating at X-band). The images acquired by the two sets of radar sensors and processed by way of differential interferometric (DInSAR) techniques (Bianchini et al 2013;Calò et al 2012Calò et al , 2014Calvello et al 2017;Cascini et al 2013a;Gullà et al 2017b;Herrera et al 2013;Peduto et al 2017;Tofani et al 2014;Wasowski and Bovenga 2014) are, respectively, suitable for analysis at small/medium scale (high resolution) and large/detailed scale (very high resolution) at affordable costs if compared with conventional monitoring systems .…”

“…The activities at large scale are aimed at facilitating local authorities and technical officers in selecting and scheduling the most suitable structural/non-structural interventions (among the categories planned at medium scale). This can be done by analyzing and predicting the damage severity levels-by way of empirical fragility curves-pertaining to homogeneous (in terms of structural and/or foundation typologies) buildings and infrastructure covered by very high-resolution DInSAR data and interacting with slow-moving landslides typified based on their geometric and kinematic characteristics (Cotecchia et al 2016;Ferlisi et al 2018;Gullà et al 2017b;Peduto et al 2017Peduto et al , 2018.…”

“…In particular, based on the frequency of occurrence of each damage severity level referring to different classes of DIn-SAR-derived differential settlements (Fig. 9a), the log-normal distribution function was adopted as probabilistic model Mavrouli et al 2014;Nicodemo et al 2017Nicodemo et al , 2018Peduto et al 2016bPeduto et al , 2017. Accordingly, the fragility curves (Fig.…”

Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves

“…The combination of landslide-induced damage on structure and infrastructure with the ground displacement allows definining the fragility curves of several structures, applying a methodology commonly applied on different engineering fields [50][51][52][53][54][55][56]. These curves represent the probability of exceeding a given damage level in a building or infrastructure as a function of ground displacements.…”