Application of spatial multi-criteria analysis (SMCA) to assess rockfall hazard and plan mitigation strategies along long infrastructures

We present a slightly abridged and adapted translation of the paper “Application of spatial multi-criteria analysis (SMCA) to assess rockfall hazard and plan mitigation strategies along long infrastructures” by Italian researchers (Foria et al., 2021). It was published in the journal “Earth and Environmental Science” by the publishing company of the British scientific society “Institute of Physics” (IOP) that is now virtually international. It is an open access article under the CC BY 3.0 license that allows it to be distributed, translated, adapted, and supplemented, provided that the types of changes are noted and the original source is referred to. In our case, the full reference to the original paper (Foria et al., 2021) used for the presented translation is given in the end. Long infrastructures often cross areas with a high probability of landslides, causing eventually serious problems to the serviceability and compromising safety. The identification and prediction of hazardous zones are difficult, especially for what concerning rockfalls, as they can occur quickly and suddenly. In order to assess rockfall hazard, detailed data such as slope geometry, geotechnical and geomechanical properties of materials, drainage system pattern, etc. are needed. Even though thematic datasets are available and easily downloadable for the majority of the Italian territory, their scale is not adequate and ad-hoc input data must be gathered. An original multi-disciplinary procedure (GEO4) has been developed by the authors based on a mobile mapping system (ARCHITA) integrated with Airborne Lidar and ILI (In- Line Inspections), geomatics, geological models, geotechnical-geomechanical characterization and geomorphometric approach. A Spatial Multi-Criteria Analysis (SMCA) is then used to create a composed and spatially distributed index of landslide hazard based on normalized values of triggering factors. Such index is used to identify and classify the morphological unstable element along the infrastructure, supporting decision-makers in defining the most appropriate mitigation measures and planning their implementation in a clearer, more repeatable and more objective orientated-way. The presented method has been successfully applied so far to hundreds of km of railway lines in Italy.