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Wildfires can significantly affect mountain hillslopes through the combustion of trees and shrubs and changes in soil properties. The type and magnitude of the associated post-fire effects depend on several factors, including fire severity and soil physical–mechanical-hydraulic features that, coupled with climate and topographic conditions, may cause increased runoff, erosion, and slope instability as consequence of intense rainfall. The post-fire response of slopes is highly site-specific. Therefore, in situ surveys and laboratory tests are needed to quantify changes in key soil parameters. The present study documents the post-fire physical and hydromechanical properties of pyroclastic topsoil collected from three test sites that suffered wildfires and rainfall-induced post-fire events in 2019 and 2020 in the Sarno Mountains (Campania Region, southern Italy). The tested pyroclastic soils in burned conditions show (i) no significant changes in grain size distribution, soil organic matter, and specific gravity; (ii) a deterioration in shear strength in terms of decreased soil cohesion caused by the fire-induced weakening of root systems; and (iii) a decrease in hydraulic conductivity. Accordingly, it can be argued that the documented post-fire erosion responses were mainly caused by the reduced cohesion and hydraulic conductivity of the burned topsoil layer, as well as by the loss of vegetation cover and the deposition of fire residues. Although deserving further deepening, this study can represent the necessary background for understanding the initiation mechanism of post-fire erosion processes in the analyzed area and on several natural slopes under similar conditions.

This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed.

Transition zones between bridges and embankments are the most maintenance-prone locations in the road network of The Netherlands due to the very compressible soft soil layers that are widespread in the subsoil of the country. This aspect causes (differential) settlements at the ground surface, damage and maintenance costs for the road owner, and delays to road users. This paper provides the proof-of-concept of an innovative multi-source data-driven method based on the assimilation of both conventional settlement plate and satellite DInSAR data in numerical geotechnical modelling with the aim of supporting informed maintenance decisions.

Interferometric methods are drivers of the recent exponential growth in the use of synthetic aperture radar (SAR) for monitoring both natural and anthropogenic hazards. Since the first use of interferometric SAR (InSAR) in the late 1990s to detect deformations associated with earthquakes and volcanoes, important developments have improved sensor performance and data-processing capabilities for the extraction of information of interest in geophysical applications. Here, we describe the improvements that have enabled the 3D reconstruction and monitoring of buildings, with reference to their protection against and prevention of risks. We focus on buildings affected by systematic displacements related to natural hazards-specifically, landslides. We address the straightforward use of InSAR methods in the detection and characterization of the hazard as well as in higher-level exploitation for geotechnical and structural studies and, more generally, vulnerability analysis.

The monitoring and prediction of the groundwater level (GWL) significantly influence the landslide kinematics. Based on the long-term fluctuation characteristics of the GWL and the time lag of triggering factors, a dynamic prediction model of the GWL based on the Maximum information coefficient (MIC) algorithm and the long-term short-term memory (LSTM) model was proposed. The Sifangbei landslide in the Three Gorges Reservoir area (TGRA) in China, wherein eight GWL monitoring sensors were installed in different locations, was taken as a case study. The monitoring data represented that the fluctuation of the GWL has a specific time lag concerning the accumulated rainfall (AR) and the reservoir water level (RWL). In addition, there were spatial differences in the fluctuation of the GWL, which was controlled by the elevation and the micro landform. From January 19, 2015, to March 6, 2017, the measured data were used to set up the predicted models. The MIC algorithm was adopted to calculate the lag time of the GWL, the RWL, and the AR. The LSTM model is a time series prediction algorithm that can transmit historical information. The Gray wolf optimization (GWO) algorithm was used to seek the most suitable hyperparameter of the LSTM model under the specific prediction conditions. The single-factor GWO-LSTM model without considering triggering factors and the support vector machine regression (SVR) model were considered to compare the prediction results. The results indicate that the MIC-GWO-LSTM model reached the highest accuracy and improved the prediction accuracy by considering the factor selection process with the learner training process. The proposed MIC-GWO-LSTM model combines the advantages of each algorithm and effectively constructs the response relationship between the GWL fluctuation and triggering factors; it also provides a new exploration for the GWL prediction, monitoring, and early warning system in the TGRA.

This study presents a novel framework in which numerical modelling contributes to the performance of district-scale, subsidence-induced damage assessment in cities where ground settlements affect entire quarters. Therein, the implementation of expeditious procedures offers geotechnical engineers the possibility of contributing beyond the typical site scale. For this purpose, several “typified” hydro-geomechanical-loading (HGL) models, which represent (simplified) scenarios of masonry buildings undergoing settlements, were set up to account for different predisposing or triggering factors (i.e., soil heterogeneity, loading conditions, and groundwater variations) of settlement occurrence in built-up areas. These models exploit multi-source, wide-area input datasets encompassing the hydro-mechanical properties of geomaterials, in situ investigations and measurements (e.g., groundwater levels in wells), and innovative remote sensing data (i.e., DInSAR techniques). With reference to a district in Rotterdam City (the Netherlands), which was built on “soft soils”, the numerical simulations of different scenarios (i) provide an overview of the comparative role of predisposing or triggering factors on settlement occurrence and (ii) allow assessments of the expected induced damage to masonry buildings over 30 years with the exploitation of fragility curves. Considering the widespread diffusion of such geohazards, the proposed approach could help prioritise (rather expensive) maintenance work to the built heritage within sustainable strategies for subsidence risk mitigation.

This paper shows the results of a numerical analysis carried out using the equivalent frame method, which aimed to investigate the three-dimensional behaviour of masonry buildings with shallow foun...

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period.

This paper presents some results of a multidisciplinary research about the assessment of damages to ordinary buildings at the urban scale in landslide areas. The methodology represents part of a multi-level approach for landslide vulnerability assessment that has been recently developed. It is based on rapid visual inspections of the buildings, the application of ‘simple models’ to interpret the structural response, the geological and geotechnical knowledge of the site. The end-product is the landslide damage geotechnical chart, including: i) the damage grade of the buildings, ii) the geomorphological and geotechnical map of the area, iii) the direction of the settlements causing damages. The application of the methodology to an historical site in southern Italy is also outlined. Finally, the contribution of innovative non-invasive spaceborne remote sensing techniques to monitor landslide-affected urban areas is highlighted.

The paper presents a multi-source approach tailored for the analysis of ground movements affecting the village of Barberà de la Conca (Tarragona, Catalonia, Spain), where cracks on the ground and damage of different severity to structures and infrastructure was recorded. For this purpose, monitoring of ground displacements performed by topographic survey, geotechnical monitoring and remote sensing techniques (ground-based synthetic aperture radar, GBSAR) are combined with multi-temporal damage surveys and monitoring of cracks (crackmeters) to get an insight into the kinematics of the urban slope. The obtained results highlight the correspondence between the monitoring data and the effects on the exposed facilities induced by ground displacements, which seem to occur predominantly in the horizontal plane with diverging directions (northward and southward) from the main ground fracture crossing the centre of the village. The case study stands as a further contribution to fostering this kind of integrated approaches that via cross-validations can improve data reliability as well as enrich datasets for slope instability recognition and analysis, which are crucial to plan risk mitigation works.

The analysis of consequence induced by landslides to the built environment is a key step in the landslide risk management process. For this purpose, a thorough knowledge of both the landslide mechanisms and the behaviour/characteristics of the exposed elements turn out to be crucial. Currently, scientists and technicians have access to unprecedented big data sources that provide a variety of useful information on landslide-affected areas. With reference to slow-moving landslides, this paper presents some case studies of an ongoing research aimed at defining and testing original procedures pursuing the exploitation of innovative monitoring/surveying techniques for the analysis and forecasting of vulnerability of buildings and roads.

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.

The paper presents a probabilistic analysis aimed at identifying the most appropriate subsidence related intensity (SRI) parameter (e.g., differential settlement, relative rotation, deflection ratio) that can be used to forecast the severity level of building damage at municipal scale through the generation of empirical fragility and vulnerability curves. The analysis refers to a rich sample of more than seven hundred monitored (by remote sensing techniques) and surveyed masonry buildings – mainly resting with their (shallow or piled) foundations on highly compressible fine-grained “soft soils” – affected by settlements in four urban areas in The Netherlands. The achieved outcomes, once further calibrated and validated, could allow for an improvement of existing geotechnical damage criteria for buildings as well as help local authorities in charge of the management/protection of subsiding urban areas to plan adequate foundation repairing/replacing measures before damage reaches intolerable severity levels.

Differential settlements can affect transition zones between bridges and road embankments in countries where very compressible soft soil layers are widespread in the subsoil. The related damage makes these locations the most maintenance-prone locations, resulting in high direct and indirect costs for the road owner and the road users as well. Accordingly, approaches capable of analysing current conditions and forecasting future settlement scenarios associated with different possible maintenance operations can turn out to be a valuable tool for the road network management process. With reference to a case study representing typical conditions in the Netherlands, this paper proposes the proof of concept of a novel multi-source data-driven method that exploits the assimilation of settlement data – acquired by both conventional and satellite DInSAR monitoring techniques – in simplified geotechnical modelling. The forecasted settlement scenarios can support informed road maintenance decisions within risk mitigation strategies.

Buildings in subsiding areas may suffer from settlements causing damages of different severity levels with high impact in terms of yearly economic losses. In these contexts, a systematic damage assessment jointly with continuous monitoring of relevant parameters (e.g. settlements exhibited by points located on the roof) can be extremely useful to control the building behaviour and develop forecasting models. In this regard, the paper presents the results of an integrated analysis carried out on a subsidence-affected urban area in the Netherlands where the availability of multi-temporal building damage surveys and a long DInSAR monitoring dataset allowed both retrieving quantitative empirical relationships between the cause (magnitude of the selected intensity parameter, IP) and the effect (recorded damage severity level, DL) and generating empirical fragility and vulnerability curves. The results pointed out the importance of considering the exact dating of the onset of building damage and the corresponding magnitude of the considered IP in the generation of quantitative forecasting models.

This paper presents the results of a numerical analysis aimed at investigating the response – in terms of damage occurrence and development – of a masonry building undergoing settlements induced by a slow-moving landslide. The analysis is performed by applying the Equivalent Frame Method to a model representative of a low-rise building with shallow foundations located in the historic centre of Lungro town (Calabria region, southern Italy). As a main novelty, data collected by way of both conventional (i.e. inclinometers) and innovative (i.e. DInSAR) monitoring techniques are used to derive the three-dimensional settlement trough to be imposed to the building model’s foundation in the numerical analyses. The obtained outcomes are compared with information gathered from multi-temporal damage surveys to the modeled building. Based on previous studies aimed at typifying the slow-moving landslides, the results obtained could concur to the generation of certain building-foundation-landslide typified models helpful for damage forecasting at the municipal scale.

Como historic centre, located at the SW branch of Lake Como (northern Italy), is prone to subside because of a thick sequence of late Pleistocene to Holocene glacio-lacustrine, palustrine and alluvial sediments in the subsoil. After the 1950s, the combination of natural causes and anthropogenic activities amplified subsidence-induced differential settlements at building foundation depths, resulting in damage on the superstructures. This work presents the first subsidence vulnerability analysis of the historic buildings in Como city centre by combining hydrogeological and stratigraphic features, in situ damage investigations, and remote sensing Synthetic Aperture Radar (SAR) data acquired by Cosmo-SkyMed mission. First, the relationships between local hydrogeological features and vertical displacements retrieved by SAR Interferometry (InSAR) analysis were qualitatively assessed. This highlighted that cumulative vertical InSAR-derived settlements have a stronger linear correlation with the groundwater level rather than the thickness of compressible soil units at the city scale. The largest vertical displacements are located in the NW sector of the city centre and along the shore of Lake Como, where they remark the pre-Roman shoreline. Then, the cause-effect relationships between building damage severity and Subsidence-Related Intensity (SRI) parameters were investigated using a probabilistic approach based on empirical fragility curves. To this aim, two InSAR-derived SRI parameters were tested for both masonry and reinforced concrete buildings: differential settlements and relative rotations. The former resulted to relate better to distinct damage levels in Como historic centre. The analyses performed can contribute to the management of the inestimable architectural and cultural heritage of Como historic centre.

Highly compressible clayey and peaty (soft) soils may expose structures (e.g. buildings) and infrastructures (e.g. roads and embankments) resting on them to absolute/differential settlements. These latter, with passing of time, can induce damages whose level of severity depends on several factors such as: the spatial distribution of the thickness pertaining to soft soil layers; the groundwater regime; the characteristics of the exposed structures and infrastructures along with their state of maintenance. The analysis of damages and the resulting management of the built-up environment usually require high costs due to the amount of data necessary for setting up reliable forecasting models as well as for defining the most suitable restoration works. This paper presents a multi-scale procedure tailored to analyze the settlement-induced building damage. The selected case study deals with an urban area in The Netherlands where, at medium scale, the role of soft soils in predisposing the occurrence of ground surface settlements is first investigated. Then, at large scale the relationship between cause (i.e. settlements) and effects (i.e. damage) is analyzed for building aggregates via the combination of high-resolution advanced differential interferometric synthetic aperture radar (DInSAR) and in-situ damage survey data. Finally, as main novelty of the proposed multi-scale procedure, both the above datasets are used to generate, at detailed scale, empirical fragility curves for single buildings that, once further validated, could be valuably adopted for damage forecasting purposes in similar urban areas.

Statistical analyses have been often used for landslide susceptibility zoning at small to medium scale when relevant base and thematic maps are available. Since the beginning of the last decade, images remotely acquired by spaceborne Synthetic Aperture Radar (SAR) and processed via Differential SAR Interferometry (DInSAR) proved extremely useful for non-invasive and non-destructive monitoring of displacements of the topographic surface. The present paper proposes an original procedure for the definition of the state of activity of slow-moving landslides via the combined use of multivariate statistical analyses and DInSAR data. The procedure is based on the following essential elements: distinction between terrain units used for computational purposes and the final zoning units; independent statistical and DInSAR analyses and activity models leading to first-level state of activity zoning maps; a consistency model between statistical and DInSAR analyses; two confidence and combination models leading, respectively, to second- or third-level state of activity zoning maps. The application in a test area including 19 municipalities in southern Italy, where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria, allowed the generation of the three above-mentioned levels of zoning maps. The results were successfully crosschecked by exploiting a different DInSAR dataset and the results of previous works based on the use of slow-moving landslide-induced damage to facilities surveys.

Several urban areas in Euro-Mediterranean countries are affected by slow-moving landslides that, even if rarely associated with the loss of human life, can cause damage to structures and infrastructure. In such contexts, the progressive decay of the built environment can bring along a generalized increase of the physical vulnerability and, as a result, slow-moving landslide risk increases over the time. Under these conditions, as long as suitable risk mitigation measures are lacking, the level of risk (also related to earthquakes) could turn out to be no longer acceptable within an a priori unknown time interval. This problem has a relevant social-economic impact, thus requiring the adoption of risk mitigation strategies that need to be effective and, at the same time, sustainable for the involved stakeholders. In this regard, this paper proposes a multi-scale methodological approach—based on the joint use of satellite-derived displacement monitoring data and the results of building damage surveys—whose applicability is tested with reference to urban areas affected by slow-moving landslides in Calabria region (southern Italy).

Slow-moving landslides are widespread natural hazards that can affect both social and economic activities causing damage to structures and infrastructure networks. This paper aims at providing an innovative simplified procedure to analyse road damage induced by slow-moving landslides based on the joint use of landslide inventory maps, a road damage database (collecting both the results of in-situ surveys and Google Street View images) and ground displacement measurements derived from the interferometric processing of satellite SAR images (DInSAR data). The procedure was applied to the case study of the Calabria region (southern Italy) following a two-scale approach. First, a regional-scale analysis was carried out to gather information on the level of road exposure (namely, Index of Exposed Roads) to slow-moving landslides in order to identify the most representative case studies. Then at local scale, relationships between road damage severity level and a selected parameter (i.e. differential settlement computed in two different ways) of slow-moving landslide intensity were derived for two road stretches that differ in geo-lithological and topographic conditions. The results underline the importance of the availability of both reliable landslide inventories and rich road damage databases as well as the potential of high-resolution DInSAR data for obtaining quantitative information on movement rates of roads useful to carry out in-depth studies pursing vulnerability analyses of the infrastructure.

The analysis and prediction of damage to buildings resting on highly compressible fine-grained “soft soils” containing (organic) clay and peat are key issues to be addressed for a proper management of subsidence-affected urban areas. Among the probabilistic approaches suggested in literature, those oriented to the generation of empirical fragility curves are particularly promising provided that a comprehensive dataset for both the subsidence-related intensity (SRI) parameters and the corresponding damage severity to buildings is available. Following this line of thought, in the present paper, a rich sample of more than seven hundred monitored (by remote sensing) and surveyed masonry buildings – mainly resting with their (shallow or piled) foundations on soft soils – is analysed in four urban areas of The Netherlands. Probabilistic functions in the form of fragility curves for building damage are retrieved for three different SRI parameters (i.e., differential settlement, rotation and deflection ratio) derived from the processing of Synthetic Aperture Radar (SAR) images by way of a differential interferometric (DInSAR) technique in combination with the severity levels of the damage recorded from the visual inspection of over 700 masonry buildings. As a novelty with respect to earlier similar studies, the work points out the methodological steps to be followed in order to identify the most appropriate SRI parameter among the selected ones. Thus, the objective of the paper is to improve the existing geotechnical forecasting tools for subsidence-affected urban areas, in order to target areas that require more detailed investigations/analyses and/or to select/prioritize foundation repairing/replacing measures.

The geometric and kinematic characterization of landslides affecting urban areas is a challenging goal that is routinely pursued via geological/geomorphological method and monitoring of ground displacements achieved by geotechnical and, more recently, advanced differential interferometric synthetic aperture radar (A-DInSAR) data. Although the integration of all the abovementioned methods should be planned a priori to be more effective, datasets resulting from the independent use of these different methods are commonly available, thus making crucial the need for their standardized a posteriori integration. In this regard, the present paper aims to provide a contribution by introducing a procedure that, taking into account the specific limits of geological/geomorphological analyses and deep/surface ground displacement monitoring via geotechnical and A-DInSAR data, allows the a posteriori integration of the results by exploiting their complementarity for landslide characterization. The approach was tested in the urban area of Lungro village (Calabria region, southern Italy), which is characterized by complex geological/ geomorphological settings, widespread landslides and peculiar urban fabric. In spite of the different level of information preliminarily available for each landslide as result of the independent use of the three methods, the implementation of the proposed procedure allowed a better understanding and typifying of the geometry and kinematics of 50 landslides. This provided part of the essential background for geotechnical landslide models to be used for slope stability analysis within landslide risk mitigation strategies.

Investigating the behaviour of buildings is extremely useful for planning interventions aimed at their safeguarding, although hardly affordable over large urbanized areas. In this regard, DInSAR-derived building settlements can provide a valuable contribution. This paper presents a procedure tailored for deriving empirical fragility curves for buildings that might be affected by differential settlements on the basis of the combined use of DInSAR data and the results of damage surveys. The case studies deal with two municipalities in The Netherlands where masonry buildings mainly rest on soft soils (i.e. peats and organic clays). The behaviour of buildings with different foundation types (i.e. shallow and deep) is analyzed and a check of the performance of a restored foundation is carried out via the analysis of pre- and post-intervention DInSAR-derived settlement trends.

In the last decade Differential Synthetic Aperture Radar (DInSAR) data were successfully tested in a number of case studies for the detection, mapping and monitoring of ground displacements associated with natural or anthropogenic phenomena. More recently, several national and regional projects all around the world provided rich data archives whose confident use, however, should rely on multidisciplinary experts in order to avoid misleading interpretations. To this aim, the present work first introduces a general framework for the use of DInSAR data; then, focusing on the analysis of subsidence phenomena and the related consequences to the exposed facilities, a set of original procedures is proposed. By drawing a multiscale approach the study highlights the different goals to be pursued at different scales of analysis via high/very high resolution SAR sensors and presents the results with reference to the case study of the Campania region (southern Italy) where widespread ground displacements occurred and damages of different severity were recorded. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

This paper aims at providing helpful tools (i.e. empirical fragility curves) for the analysis and prediction of the damage to masonry buildings in areas affected by slow-moving landslides. This goal is pursued through the development of an innovative procedure based on the joint use of DInSAR data and building damage surveys and applied in two study areas of the Calabria region (southern Italy).

This paper presents a mass movement inventory map at 1:5000 scale of the Verbicaro area (about 13 km2) located in the Calabria region (southern Italy). The Main Map results from the visual interpretation of aerial photographs, multi-temporal geomorphological field surveys, and field investigations of damage suffered by buildings. Some 53% of the study area is affected by a total of 252 landslides, comprising different types, state of activity, and size. The mapped landslides, mainly complex type, involve low-grade metamorphic rocks; among these, 15% are active and slow-move on pre-existing sliding surfaces. Moreover, out of 492 surveyed buildings, 347 are located on landslide-affected areas and experienced damages covering a broad range of severity levels. The Main Map can represent a useful tool for authorities in charge of land-use planning and urban management and can be used to pursue landslide risk analyses.

The presence of highly compressible soft soil layers can expose infrastructure networks (roads, bridges, pipelines, etc.) to settlements that can compromise their serviceability or even their stability. The analysis/forecast of the induced effects can follow empirical, analytical, numerical approaches at different levels of analysis referring to samples of exposed elements whose number decreases as the level of required details increases. The present work proposes an original procedure for investigating bridge settlement-induced damage via the generation of empirical fragility curves based on the integration of visible damage surveys on statically indeterminate bridges (both masonry and concrete) with widespread remote sensing-derived settlement measurements. The procedure is tested in Amsterdam city (The Netherlands) worldwide famous for its dense network of bridges resting with their foundations on a subsoil of thick layers of organic clay and peat alternating with sands. These bridges currently suffer from settlement-induced damages of different severity that yearly require expensive maintenance and repair works. The obtained results seem promising under the perspective of prioritizing interventions on those bridges for which detailed and demanding coupled/uncoupled geotechnical-structural models need to be set up.

This paper proposes a multi-sensor a priori PSI visibility map for Austria in order to evaluate the feasibility of Differential SAR Interferometric (DInSAR) applications for landslide-affected slopes. For this purpose, the range index RI, introduced for the determination of areas in layover and foreshortening on both ascending and descending acquisition geometries, is computed and applied to the most diffuse X-C-L band SAR sensors. A new method is introduced to improve the accuracy of those products by fusing CORINE data with sharper European JRC forest map and Imperviousness Copernicus map. The results are tested with six different available PSI datasets over Austria. Then, a priori visibility map and a PSI density map are also derived for seven different satellites by combining the RI index and an enhanced CORINE land cover map. Finally, PSI velocity values, along the Line of Sight (VLos) and projected along the steepest slope direction (VSlope), are used in order to produce a landslide velocity map for the Austrian region of Vorarlberg.

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.

Secondary settlements in soft soils represent a significant fraction of the total settlement induced by external loads. Consequently, these settlements can play a key role in performance, serviceability, and safety of engineering works such as buildings, roads, embankments, and pipelines. This paper addresses the development of a predictive settlement model for a railway embankment built on soft clayey–peaty soils by following an original procedure consisting of three cascading steps: (i) preliminary detection of the most settlement-affected portions of the infrastructure; (ii) development of an equivalent subsoil model to study secondary settlements; (iii) back-calculation of the parameters of a predictive settlement model (design subsoil model) via a variational data assimilation scheme that exploits ground displacement measurements derived from differential interferometric synthetic aperture radar (DInSAR) data. The main achievement relies on the retrieval of a stochastic prediction of secondary settlements that can contribute to rationalize both conventional monitoring campaigns and management of key infrastructure.

A proper analysis of slow-moving landslides calls for several efforts aiming at their characterization and mapping. Considering the uncertainties related to the landslide inventory maps the integration of conventional techniques with remote sensing data, such as differential SAR interferometry (DInSAR), can furnish a valuable contribution in a number of case studies. However, standardized procedures for the interpretation and the confident use of DInSAR data, according to landslide zoning developments, have not been fully investigated and validated, although algorithms for image processing have become more and more sophisticated. This work addresses a new methodology for the use of DInSAR data, at both full- and low-resolutions, in landslide analyses at different scales via the integration of remote sensing data with simple geomorphological models and geometric considerations. The methodology is tested inside a well documented area in Central–Southern Italy where an advanced dataset on base and thematic maps is available.

Geological and geomorphological criteria complemented with geotechnical monitoring of ground displacements are routinely applied for geometric and kinematic characterization of landslides. However, these activities may be challenging in densely built-up historic centres. In order to tackle all constraints posed by the presence of complex urban fabric, this paper proposes a procedure for integrating the results of geological/geomorphological analyses with deep and surface ground displacement data acquired via geotechnical monitoring and spaceborne Differential Interferometric Synthetic Aperture Radar (DInSAR) data. The selected test site is a village in southern Italian Apennines characterized by complex geological/geomorphological settings, widespread distribution of landslides and a peculiar urban fabric. The results of the integration procedure show that it is possible to overcome specific limits and provide a better understanding and definition of the geometry and kinematics of slow-moving landslides to be used for slope stability analyses and landslide risk management in historic centres.

In this study Differential Interferometric Synthetic Aperture Radar (DInSAR) is used to investigate the effects of a subsidence phenomenon – related to groundwater withdrawal –on ancient low‐rise masonry buildings located in the urban area of Sarno, Italy. ERS‐1/2 SAR data from 1992 to 2002 were analysed and results were validated with geodetic data. The results encourage future developments for study and use of the multi‐temporal DInSAR technique in the mitigation of subsidence risk.