Regional-scale assessment of the damage caused by earthquakes to structures is crucial for post-disaster management. While remote sensing techniques can be of great help for a quick post-event structural assessment of large areas, currently available methods are limited to the detection of severely-damaged buildings. Furthermore, remote sensing-based assessment methods typically provide only qualitative results, as they lack integration with information on the building's behaviour in response to seismic-induced ground shaking. In this study, we developed a new methodology that uses airborne Light Detection And Ranging (LiDAR) data in combination with structural indicators of building response to provide a quantitative assessment of earthquake-induced damage at a regional scale. LiDAR datasets collected before and after an earthquake are used to measure residual displacements of building roofs. The resulting lateral drift estimations are used to quantify the level of damage for a specific building typology. Application to the LiDAR datasets collected before and after the 2014 earthquake in Napa Valley, California, demonstrates the capability of the proposed method to detect moderate levels of structural damage, proving its potential for faster and more accurate support to post-disaster management.

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The Kakhovka Dam on the Dnieper River in Kherson Oblast, Ukraine, was completed in 1956 as the final dam in the Dnieper reservoir cascade. On the morning of June 6^th, 2023, a substantial portion of the dam suffered a collapse while under Russian control. This incident was documented through satellite optical and radar images, providing valuable evidence of the dam’s condition. Here we present the results of multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) monitoring of the Kakhovka dam. The dam is vital for water management and hydroelectric power generation. Utilizing multi-temporal InSAR (MT-InSAR) data, we assessed the dam deformations prior to the collapse. Our findings indicate movements of the south side, facing the Dniprovska Gulf, compatible with several possible damage mechanisms. This study highlights the significance of employing spaceborne advanced monitoring techniques to detect signs of distress and ensure the stability of critical infrastructure.

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Thousands of bridges worldwide face growing risks due to aging materials, increased traffic loads, and climate change-induced weather extremes. Managing these assets is financially demanding, and requires prioritisation strategies for interventions. Consequently, innovative approaches are urgently required to evaluate the structural conditions of these bridges continuously and regularly. Recent advancements in space-borne Interferometric Synthetic Aperture Radar (InSAR) technology offer cost-effective remote monitoring capabilities, ensuring extensive coverage and high spatial resolution. Multi Temporal (MT) InSAR techniques enable the reconstruction of millimetre-scale deformation measurements for a large number of assets, opening opportunities for long-term regional-scale monitoring of bridge deformations. However, a major challenge in utilising MT-InSAR-based displacement data operationally is that MT-InSAR analysis reconstructs only the projection of displacements along the satellite Line of Sight (LOS) direction. Due to the typical availability of only two satellite viewing geometries, in most cases the three-dimensional displacement field cannot be fully reconstructed. Consequently, without accounting for the anticipated motion of a given structure and its alignment with respect to the satellite flight path, the actual asset movement is likely to be underestimated, leading to erroneous interpretation. In this paper, we propose a method using the bridge typologies and their associated likely failure mechanisms to derive assumptions regarding expected displacement directions. Then, the information on bridge alignments with respect to the satellite flight direction is used to assess the MT-InSAR sensitivity to the expected displacement directions and define ad-hoc damage indicators. We tested the proposed method on urban bridges in Amsterdam, the Netherlands, using deformation measurements derived from TerraSAR-X data spanning 2016 to 2020. Findings have potential to enhance current procedures for the structural evaluation of bridges.

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Predicting the availability of measurement points provided by Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) poses a challenge due to a nonuniform distribution of Persistent Scatterers (PSs). This article introduces a novel method to estimate the availability of MT-InSAR results on buildings and infrastructure networks, eliminating the need for labor-intensive and time-consuming analyses of the entire SAR data stack. The method is based on an analysis of the interferometric coherence decay characteristics and data regarding buildings and transport infrastructure location as inputs to a convolutional neural network. Specifically, a U-Net architecture model was implemented and trained to predict the PS density of Sentinel-1 data. The methodology was applied to a regional-scale analysis of the Dutch infrastructure, resulting in a low 1.06pm0.10 mean absolute error in the pixel-based PS count estimation on the test data split, with over 80% of predictions within pm1 from the actual value. The model achieved high accuracy when applied to a previously unseen dataset, demonstrating strong generalization performance. The proposed workflow, with its notable ability to accurately predict areas lacking measurement points, offers stakeholders a tool to assess the feasibility of applying MT-InSAR for specific structures. Thereby, it enhances infrastructure reliability by addressing a critical need in decision-making processes and improving the applicability of MT-InSAR for structural health monitoring of infrastructure assets.

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Bridges play a vital role in the European transport network, and their preservation is of utmost importance. Despite many centuries- old bridges still being in use in European cities, their structural integrity may be compromised due to factors like material degradation, increased traffic loads, extreme events, or slow deformation phenomena. It is essential to regularly assess the current conditions of these structures and monitor their evolution over time to enable timely intervention when necessary. This study presents the first results of a multidisciplinary methodology for the Structural Health Monitoring (SHM) of typical urban bridges in the Netherlands, combining numerical simulations using the Applied Element Method (AEM) with monitoring data derived from various sensing sources. These sources range from standard in situ techniques to satellite remote sensing using Synthetic Aperture Radar Interferometry (InSAR). The methodology is applied to a representative bridge of Amsterdam canals. The nonlinear analyses have led to a numerically predicted crack pattern consistent with on-site observations. The simulated damage progression until collapse identifies critical points of the bridge to be kept under control with monitoring activities.

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Accurate and rapid postearthquake structural damage assessment is of vital importance for humanitarian relief. Remote sensing techniques have the potential to map large areas with reduced data latency but are limited by several factors, including accuracy (compared to in-situ monitoring campaigns) and data acquisition frequency. Current damage assessment techniques relying on remote sensing data enable rapid assessment in situations where on-site reconnaissance is not possible or desirable. Yet, these techniques rely on different scales, measurement methods, and spatial resolutions, making it difficult to assimilate many different damage products in a homogeneous damage map. Here, we present the results of the U.K.'s Earthquake Engineering Field Investigation Team's remote-sensing-based reconnaissance mission, which was carried out in the aftermath of the series of earthquakes that struck Turkey and Syria in February 2023. We use a set of publicly available damage maps based on synthetic aperture radar, optical imaging, and ground-based reports as well as in-house developed damage products and assess their relative accuracies. We describe the process of supporting on-site reconnaissance planning by creating maps that describe the building stock and diversity of damage in southeast Turkey to assist field survey teams in selecting regions that represent a diverse sample of building typologies and damage levels. Our results show that satellite-based remote sensing damage maps disagree with each other, and extensive validation data are still required to characterize the accuracy of each method at both high and medium resolution. Finally, we provide recommendations for planning and validation of future earthquake response efforts.

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Earthquakes have devastating effects on densely urbanised regions, requiring rapid and extensive damage assessment to guide resource allocation and recovery efforts. Traditional damage assessment is time-consuming, resource-intensive, and faces challenges in covering vast affected areas, often limiting timely decision-making. Space-borne synthetic aperture radars (SAR) have gained attention for their all-weather and day-night imaging capabilities. These advantages, coupled with wide coverage, short revisits and very high resolution (VHR), have created opportunities for using SAR data in disaster response. However, most SAR studies for post-earthquake damage assessment rely on change detection methods using pre-event SAR images, which are often unavailable in operational scenarios. Limited studies using solely post-event SAR data primarily concentrate on city-block-level damage assessment, thus not fully exploiting the VHR SAR potential. This paper presents a novel method integrating solely post-event VHR SAR imagery and machine learning (ML) for regional-scale post-earthquake damage assessment at the individual building-level. We first used supervised learning on case-specific datasets, and then introduced a combined learning approach, incorporating inventories from multiple case studies to assess generalisation. Finally, the ML model was tested on unseen study areas, to evaluate its flexibility in unfamiliar contexts. The method was implemented using datasets collected during the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance missions following the 2021 Nippes earthquake and the 2023 Kahramanmaraş earthquake sequence. The results demonstrate the method’s ability to classify standing and collapsed buildings, achieving up to 72% overall accuracy on unseen regions. The proposed method has potential for future disaster assessments, thereby contributing to more effective earthquake management strategies.

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After an earthquake, a rapid identification of the damaged building stock is crucial to prioritise rescue operations, ensure primary services to the most affected regions and support reconstruction. Whilst in-situ reconnaissance missions provide invaluable data on the intensity and distribution of earthquake-induced structural damage, the process of collecting field observations is often dangerous, expensive, and is usually undertaken a few weeks after the disaster. Spaceborne Synthetic Aperture Radar (SAR) can remotely provide imagery data of wide affected areas, enabling to reach locations that are difficult or dangerous to access with traditional survey methods. Furthermore, SAR-based observations are independent from daylight illumination and clear-weather conditions. Thanks to the recent availability of Very-High Resolution (VHR) SAR satellites, post-disaster imagery data with sub-metre resolution are now available within a few hours after a major earthquake, opening unprecedented opportunities for complementing in-situ operations. The textural analysis of post-earthquake VHR SAR images could be used to identify backscattering signatures that are likely associated with building damage. However, application has been limited by the lack of methods that correlate the textural properties of damaged structures in radar images with building survey data. In this paper, we present a method using textural features derived from VHR SAR post-event images in combination with building survey data to classify earthquake-induced building damage at city block-level. We tested the proposed method within the context of a joint Structural Extreme Event Reconnaissance (StEER), GeoHazards International (GHI) and Earthquake Engineering Field Investigation Team (EEFIT) mission that followed the 2021 Haiti Earthquake. The developed method was applied to the city of Les Cayes, Haiti, using a post-event Capella SAR image acquired on the 16th of August 2021. The outcomes can positively impact future earthquake scenarios, with the potential to improve rapid disaster response and remotely aid post-earthquake reconnaissance missions.@en

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.

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Earthquakes are natural hazards leading to the greatest human and economic losses, which are mostly due to structural collapses. Rapid identification and assessment of earthquake-induced damage to structures is therefore an essential component of the emergency response, and instrumental to effective reconstruction plans. Typically, structural damage assessment is conducted through building-by-building inspections during post-earthquake field reconnaissance missions. These missions are expensive and time-consuming, especially if large areas need to be investigated. Remote sensing techniques provide a relatively low-cost, wide-area alternative to in-situ monitoring. Classification and change detection based on pre- and post-event optical and synthetic aperture radar (SAR) satellite images are the most used approaches to detect damaged structures after earthquakes. However, these techniques only provide qualitative observations of collapsed or severely damaged structures. In this work, we present a new approach for the quantitative assessment of earthquake-induced structural damage based on displacement measurements acquired by Airborne Light Detection And Ranging (LiDAR). The approach is based on the integration between LiDAR-based observations and structural indicators of damage. The application to the analysis of 684 buildings affected by the 2014 Napa earthquake, in California, demonstrates a good agreement between the LiDAR-based results and independent in-situ observations. This work sets the basis for the innovative exploitation of remote sensing data in disaster management. @en

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

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Worldwide, countries are facing the challenge of ageing transport infrastructure, as thousands of assets have already reached the end of their life service. Structural monitoring is crucial to identify damage precursors and prevent structural failure, but the health evaluation of so many assets is challenging. Space-borne Interferometric Synthetic Aperture Radar (InSAR) can remotely provide high-resolution and high-density monitoring data over large areas, allowing to reconstruct the displacement field of structures with millimetre-scale accuracy. InSAR displacement measurements have been widely used within the civil engineering field, demonstrating that this technology can detect building and infrastructure deformations. However, to use satellite datasets for structural-monitoring purpose, (i) InSAR displacement measurements need to be related to large infrastructure inventories and (ii) performance indicators for the identification of structural anomalies on a large scale need to be defined. We present a novel methodology based on the automated integration of InSAR-derived displacements with infrastructure databases for the identification of early warnings over large networks. The proposed methodology is applied to the Italian motorway network and bridges. The proposed methodology leads to the creation of risk maps highlighting the assets which exhibit the most rapid variation in monitored deformations and anomalous differential movements within the infrastructure@en

Worldwide, transport infrastructure is increasingly vulnerable to aging-induced deterioration and climate-related hazards. Often, inspection and maintenance costs far exceed the available resources, and numerous assets lack any rigorous structural evaluation. Space-borne synthetic aperture radar interferometry (InSAR) is a powerful remote sensing technology that can provide cheaper deformation measurements for bridges and other transport infrastructure with short revisit times, while scaling from the local to the global scale. As recent studies have shown InSAR accuracy to be comparable to that of traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility of supporting inspection planning and maintenance prioritisation while maximising functionality and increasing the resilience of infrastructure networks. However, despite the high potential of InSAR for structural monitoring, some important limitations need to be considered when applying it in practice. In this paper, the challenges of using InSAR for the purpose of structural monitoring are identified and discussed, with specific focus on bridges and transport networks. Examples are presented to illustrate the current practical limitations of InSAR, and possible solutions and promising research directions are identified. The aim of the paper is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges.

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In fast growing cities, tunnels are increasingly adopted solutions to meet the demand for more effective transportation. As settlements caused by tunnel excavations can damage buildings along the tunnel alignment, a large portion of investments in underground construction projects is typically devoted to the assessment of settlement-induced damage to buildings. To contain the project costs, only a limited number of buildings is usually included in the monitoring scheme, and therefore damage assessment procedures are traditionally based on highly conservative assumptions. Modern space-borne Synthetic Aperture Radar (SAR) missions can provide monitoring data over large areas, guaranteeing high spatial resolutions and short revisit times. Persistent Scatterer Interferometry (PSI) [1,2] can be used to extract building deformations over time from long temporal series of InSAR images, providing measurements with an accuracy comparable to traditional in-situ monitoring, i.e. of the order of millimetre, and at a much lower cost. However, without an integration with structural models, PS-InSAR data cannot provide meaningful information on the building conditions. This integration is particularly demanding for large excavation projects, where hundreds of buildings need to be assessed. In this research, we present a new methodology for the integration of PS-InSAR-based building deformations within damage assessment procedures to estimate the level of vulnerability of buildings adjacent to tunnel excavations. The methodology combines in an automated workflow PS-InSAR data, GIS (Geographical Information System)-building databases and semi-empirical models of the building response to tunnelling, to provide a more accurate estimate of each structure damage level. We tested the proposed methodology on the Crossrail tunnel alignment in London, UK. Crossrail tunnelling activities started in May 2012, and resulted in the excavation of 21 km twin tunnels below central London. We used as an input historical PS-InSAR data obtained by processing 72 COSMO-SkyMed descending images from 2011 to 2015 [3]. The processing led to the identification of 228,000 PSs over the monitored area, which correspond to an average density of about 9000 PS/km2. The map in Figure 1 shows the distribution of cumulative displacements along the Crossrail tunnel alignment, revealing the settlement caused by the excavation. In the region above the tunnels, line of sight (LOS) displacements between -2 cm and -3.5 cm were observed. PS points were automatically associated to the buildings along the tunnel route, and for each building, the corresponding PS-InSAR-based displacements were used to estimate the actual building settlement profile, using the fitting model described in Giardina et al., 2019 [4]. Figure 2 shows an example of a specific building, for which the PS-InSAR measurements were used to reconstruct the settlement below the structure. Then, the actual building settlement curves were analysed through a semi-empirical model of the building response to tunnelling [5] to estimate the maximum building strains. On the basis of its maximum strain, a level of damage was assigned to each building, and damage maps showing the distribution of building damage levels were the output of the proposed methodology (Figure 3). The developed algorithm enabled the identification of the structural damage of 858 buildings, highlighting its capability as a city-scale assessment tool. Additionally, the application of the proposed algorithm made available for the first time a large dataset of field observations of the building response to tunnelling. This allowed the identification of relationships between building construction materials, foundation typologies and global building behaviour. The findings can help improving current damage assessment procedures and advance the understanding of building response to tunnelling, with an impact on future excavation projects all over the world.@en

In western countries, thousands of infrastructure assets have exceeded their intended design life and need continuous monitoring. Space-borne Interferometric Synthetic Aperture Radars (InSAR) are capable of wide-area monitoring, providing inexpensive and high-density measurements of buildings and infrastructure deformations with a millimetre-scale accuracy. Infrastructure catalogues can be used to associate the InSAR-based measurements with the corresponding structures. However, when large infrastructure networks are analysed, the manual extraction of the relevant InSAR-derived displacements is not feasible. In this paper, a new methodology based on the automated integration of InSAR-based displacements and infrastructure databases to warn potentially anomalous deformations over large infrastructure networks is presented. The proposed methodology is applied to the Los Angeles highway and freeway network, using Sentinel data from 2016 to 2019. Results can have a direct impact on transportation network management.@en

Structural deformation monitoring is crucial for the identification of early signs of tunnelling-induced damage to adjacent structures and for the improvement of current damage assessment procedures. Satellite multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques enable measurement of building displacements over time with millimetre-scale accuracy. Compared to traditional ground-based monitoring, MT-InSAR can yield denser and cheaper building observations, representing a cost-effective monitoring tool. However, without integrating MT-InSAR techniques and structural assessment, the potential of InSAR monitoring cannot be fully exploited. This integration is particularly demanding for large construction projects, where big datasets need to be processed. In this paper, we present a new automated methodology that integrates MT-InSAR-based building deformations and damage assessment procedures to evaluate settlement-induced damage to buildings adjacent to tunnel excavations. The developed methodology was applied to the buildings along an 8-km segment of the Crossrail tunnel route in London, using COSMO-SkyMed MT-InSAR data from 2011 to 2015. The methodology enabled the identification of damage levels for 858 buildings along the Crossrail twin tunnels, providing an unprecedented number of high quality field observations for building response to settlements. The proposed methodology can be used to improve current damage assessment procedures, for the benefit of future underground excavation projects in urban areas.

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We would like to thank our colleagues for their comment, as we believe that this discussion further highlights the importance of innovative research in the emerging field of InSAR applications to civil engineering structures. We discuss the statement from Lanari et al. (2020): “Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results”. In this reply we argue that (1) they cannot detect the pre-collapse movements because they use standard approaches and (2) the signals of interest become observable by changing the point of view.

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We present a methodology for the assessment of possible pre-failure bridge deformations, based on SyntheticAperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009-2018. Historical satellite datasets include Envisat data spanning 2003-2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.

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The expansion of modern cities causes a growing demand for efficient transport facilities, motivating the realization of large urban tunnelling projects. A major concern during tunnelling operations is the evaluation of the response of existing buildings to induced ground movements, with a considerable economic impact on the total project cost. Since traditional monitoring is based on costly in-situ installations, only a limited amount of observations is typically provided for each structure. Without a systematic increase in the availability of monitored building deformations, lesson learned from previous projects cannot be effectively used to improve existing damage assessment procedures. Satellite Interferometric Synthetic Aperture Radar (InSAR) techniques, providing high quality measurements of building deformations, have been recently validated to be used in combination with damage assessment procedures. In particular, Multi-Temporal InSAR can provide cumulative displacement maps for a high density of monitoring points with millimetric accuracy, enabling to assess buildings nearby the excavation. In this paper, results from post-tunnelling damage assessment of hundreds of buildings along the Crossrail route in London are presented. By exploiting InSAR-based displacements, a semi-automated assessment tool is used to investigate the structural response to tunnelling-induced settlements. Results are compared to the ones obtained through the greenfield-based assessment, enabling to evaluate their accuracy. The application of the proposed method to real case studies highlights its potential for the quasi-real time identification of building damage levels over extensive area. This semi-automatic procedure can positively impact the construction industry, enabling to improve traditional damage assessment strategies and to complement ground-based monitoring systems.@en

Structural monitoring of surface building displacements is a significant component of the total financial investment for underground construction projects in urban areas. While traditional monitoring requires in-situ (terrestrial) measurements and trigger levels based on preliminary evaluation of vulnerable structures, very recent advances in Interferometric Synthetic Aperture Radar (InSAR) techniques enable remote monitoring over extensive areas, providing rapid, semi-automatic, and dense measurements with millimetre accuracy. Despite the well-established use of InSAR in geophysical applications, only a few studies are currently available on the use of satellite-based monitoring for the assessment of building deformations and structural damage. The aim of this project is to investigate the potential of InSAR monitoring data as an input to post-tunnelling damage assessment procedures. First, InSAR-based measurements of building displacements, induced by the excavation of Crossrail tunnels in London, were acquired and processed. Then, following the definition of a step-by-step procedure, the satellite-based building displacements were used to evaluate structural deformation parameters typically used in extensive damage assessment procedures. Results show that the number of available measures per single building can enable the estimation of deformation parameters, a capability that is not economically feasible for large scale projects using traditional monitoring systems. The comparison with greenfield predictions offers new insight into the effect of soil-structure interaction and demonstrates the suitability of InSAR monitoring for post-tunnelling damage assessment of structures. The outcome of this work can have a significant economic impact on the construction industry and can advance the knowledge of building and infrastructure response to ground subsidence.

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