Quantitative analysis of consequences to masonry buildings interacting with slow-moving landslide mechanisms: a case study

Quantitative analysis of consequences (in terms of expected monetary losses) induced by slow-moving landslide mechanisms to buildings or infrastructure networks is a key step in the landslide risk management framework. It can influence risk mitigation policies as well as help authorities in charge of land management in addressing/prioritizing interventions or restoration works. This kind of analysis generally requires multidisciplinary approaches, which cannot disregard a thorough knowledge of landslide mechanisms, and rich datasets that are seldom available as testified by the limited number of examples in the scientific literature. With reference to the well-documented case study of Lungro town (Calabria region, southern Italy)—severely affected by slow-moving landslides of different types—the present paper proposes and implements a multi-step procedure for monetary loss forecasting associated with different landslide kinematic/damage scenarios. Procedures to typify landslide mechanisms and physical vulnerability analysis, previously tested in the same area, are here appropriately merged to derive both kinematic and damage scenarios to the exposed buildings. Then, the outcomes are combined with economic data in order to forecast monetary loss at municipal scale. The proposed method and the obtained results, once further validated, could stand as reference case for other urban areas in similar geo-environmental contexts in order to derive useful information on expected direct consequences unless slow-moving landslide risk mitigation measures are taken.

[1]  Gianfranco Fornaro,et al.  The Use of DInSAR Data for the Analysis of Building Damage Induced by Slow-Moving Landslides , 2015 .

[2]  S. Ferlisi,et al.  Geology, slow-moving landslides, and damages to buildings in the Verbicaro area (north-western Calabria region, southern Italy) , 2018 .

[3]  Olivier Deck,et al.  Development of building vulnerability functions in subsidence regions from analytical methods , 2012 .

[4]  S. Lagomarsino,et al.  Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings , 2006 .

[5]  Jordi Corominas,et al.  Vulnerability assessment for reinforced concrete buildings exposed to landslides , 2014, Bulletin of Engineering Geology and the Environment.

[6]  Gianfranco Fornaro,et al.  Tomographic Processing of Interferometric SAR Data: Developments, applications, and future research perspectives , 2014, IEEE Signal Processing Magazine.

[7]  Nicola Casagli,et al.  Building Deformation Assessment by Means of Persistent Scatterer Interferometry Analysis on a Landslide-Affected Area: The Volterra (Italy) Case Study , 2015, Remote. Sens..

[8]  Kyriazis Pitilakis,et al.  Vulnerability assessment of buildings exposed to coseismic permanent slope displacements , 2015 .

[9]  G. Fornaro,et al.  Geometric and kinematic characterization of landslides affecting urban areas: the Lungro case study (Calabria, Southern Italy) , 2017, Landslides.

[10]  J. Maccabiani,et al.  Multi-scale analysis of settlement-induced building damage using damage surveys and DInSAR data: A case study in The Netherlands , 2017 .

[11]  G. Fornaro,et al.  Conventional and Innovative Techniques for the Monitoring of Displacements in Landslide Affected Area , 2013 .

[12]  Susana da Silva Pereira,et al.  Assessment of physical vulnerability and potential losses of buildings due to shallow slides , 2014, Natural Hazards.

[13]  Robin Fell,et al.  A framework for landslide risk assessment and management , 2005 .

[14]  Liesbet Vranken,et al.  Economic valuation of landslide damage in hilly regions: a case study from Flanders, Belgium. , 2013, The Science of the total environment.

[15]  Veronica Tofani,et al.  Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry , 2014, Landslides.

[16]  R B Peck,et al.  Soil mechanics in engineering practice; second edition , 1967 .

[17]  L. Cascini,et al.  A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas , 2015 .

[18]  An integrated approach for landslide characterization in a historic centre , 2018 .

[19]  Annegret H. Thieken,et al.  Review article: assessing the costs of natural hazards - state of the art and knowledge gaps , 2013 .

[20]  W. Z. Savage,et al.  Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning , 2008 .

[21]  Luigi Borrelli,et al.  Time evolution of landslide damages to buildings: the case study of Lungro (Calabria, southern Italy) , 2015, Bulletin of Engineering Geology and the Environment.

[22]  Juan Remondo,et al.  Quantitative landslide risk assessment and mapping on the basis of recent occurrences , 2008 .

[23]  C. V. van Westen,et al.  Assessing landslide exposure in areas with limited landslide information , 2014, Landslides.

[24]  Richard J. Jardine,et al.  Ground performance and building response due to tunnelling , 2004 .

[25]  Diego Reale,et al.  Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales , 2017, Landslides.

[26]  Gianfranco Fornaro,et al.  Four-Dimensional SAR Imaging for Height Estimation and Monitoring of Single and Double Scatterers , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Paolo Frattini,et al.  Local scale multiple quantitative risk assessment and uncertainty evaluation in a densely urbanised , 2012 .

[28]  J. Malet,et al.  Recommendations for the quantitative analysis of landslide risk , 2013, Bulletin of Engineering Geology and the Environment.

[29]  J. Hübl,et al.  Towards an empirical vulnerability function for use in debris flow risk assessment , 2007 .

[30]  J. Zêzere,et al.  Assessment of physical vulnerability of buildings and analysis of landslide risk at the municipal scale: application to the Loures municipality, Portugal , 2015 .

[31]  S. Ferlisi,et al.  Analysis of Building Vulnerability to Slow-Moving Landslides via A-DInSAR and Damage Survey Data , 2017 .

[32]  L. Cascini,et al.  The combination of DInSAR and facility damage data for the updating of slow-moving landslide inventory maps at medium scale , 2013 .

[33]  K. Terzaghi,et al.  Soil mechanics in engineering practice , 1948 .

[34]  D. Alexander.  Vulnerability to landslides. , 2012 .

[35]  Landslide damage assessment at the intermediate to small scale , 2016 .

[36]  Olivier Deck,et al.  Development of building vulnerability functions in subsidence regions from empirical methods , 2009 .

[37]  Thomas Ulrich,et al.  Fragility curves for masonry structures submitted to permanent ground displacements and earthquakes , 2014, Natural Hazards.

[38]  Rosario Montuori,et al.  Probabilistic analysis of settlement-induced damage to bridges in the city of Amsterdam (The Netherlands) , 2018 .

[39]  José Luís Zêzere,et al.  Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon (Portugal) , 2008 .

[40]  Evelyne Foerster,et al.  Parametric studies and quantitative assessment of the vulnerability of a RC frame building exposed to differential settlements , 2010 .

[41]  Gianfranco Fornaro,et al.  A procedure for the analysis of building vulnerability to slow-moving landslides , 2016 .

[42]  Bengt B. Broms,et al.  Behaviour of foundations and structures , 1977 .

[43]  J. Maccabiani,et al.  Investigating building settlements via very high resolution SAR sensors , 2016 .

[44]  S. Calcaterra,et al.  Long-term measurements using an integrated monitoring network to identify homogeneous landslide sectors in a complex geo-environmental context (Lago, Calabria, Italy) , 2018, Landslides.

[45]  M. Calvello,et al.  Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides , 2017, Landslides.

[46]  Gerardo Herrera,et al.  Subsidence activity maps derived from DInSAR data: Orihuela case study , 2013 .