The building stock around the world is exposed to different types of natural actions such as earthquakes or landslides. In particular, Italy is one of the countries worldwide most affected by landslides. Mitigation of landslide risk is a topic of great interest for the evaluation and management of its consequences. Periodical monitoring of the landslide-induced damage on structures require high costs due to the large number of exposed elements. With respect to the reinforced concrete structures, slow-moving landslides can affect primary structural elements, but more frequently damage occurs on the most vulnerable elements of the structure such as infills. The aim of this work is to demonstrate the potential utility of satellite data derived from a remote sensing technique, known as differential synthetic aperture radar interferometry, to support the structural health monitoring of reinforced concrete buildings affected by landslides. This article shows the structural health monitoring process for a reinforced concrete infilled building within a landslide-affected area, using the differential synthetic aperture radar interferometry data as input for the structural analysis in order to investigate the evolution of damage over the years. Three-dimensional structure, including the explicit infills consideration, has been modeled based on the information available from a visual survey, obtaining the missing parameters from a simulated design process and from the literature. In the field of the civil protection programs for the landslide risk reduction, this methodology can be quickly repeated for large sets of reinforced concrete buildings. Evidence of the visual survey showed a significant damage pattern in some infills. A good agreement has been found between analytical previsions and existing damage. Moreover, a global infills damage assessment of the case study building is proposed. Finally, assuming a constant increase in displacements in future years, a prediction of the future expected damage is shown.

The management and the safeguard of existing buildings and infrastructures are actual tasks for structural engineering. Non-invasive structural monitoring techniques can provide useful information for supporting the management process and the safety evaluation, reducing at once the impact of disturbances on the structure’s functionality. This paper focuses on the exploitation of advanced multi-temporal differential synthetic aperture radar interferometry (DInSAR) products for the structural monitoring of buildings and infrastructures, subjected to different external actions. In this framework, a methodological approach is proposed, based on the integration of DInSAR measurements with historical sources, accurate 3D modelling and consistent positioning of the reflecting targets in the GIS environment. Documentary sources can prove particularly helpful in collecting technical information, to reconstruct an accurate 3D geometry of the building under monitoring, limiting in-situ surveys. The analysis of DInSAR-based displacements time series and mean deformation velocity values allows the identification of possible critical situations for buildings to be monitored. The paper presents different approaches, with increasing accuracy levels, to study the active deformative processes of the examined buildings and the related damage assessment. An insight into these interpretative approaches is given through the application of the proposed procedure to two case studies in the city of Rome (Italy), the residential building named Torri Stellari in Valco San Paolo (1951–1953) and the housing complex referred to as Corviale (1967–1983), by exploiting the whole COSMO-SkyMed data archive (both ascending and descending acquisitions), collected during the 2011–2019 time interval. Pros and cons of the various approaches are deeply discussed, together with an estimation of the required computational effort.

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The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011–2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.

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