While a geo-hazard risk assessment of bridges is crucial for achieving the United Nations’ Sustainable Development Goals, state-of-the-art methods for evaluation of risk neglect the temporal dimension of structural vulnerability, overlooking how monitoring systems like Structural Health Monitoring sensors and Multi-Temporal Interferometric Synthetic Aperture Radar can continuously track bridge conditions. Moreover, despite Structural Health Monitoring systems being sparsely installed, no research has quantified the global potential of this spaceborne radar-based technique as a complementary monitoring solution for bridges. This study introduces a method that integrates monitoring availability into structural vulnerability assessments and evaluates the global risk of long-span bridges affected by subsidence and landslides. Findings revealed that while fewer than 20% of bridges have Structural Health Monitoring systems, spaceborne monitoring could provide monitoring for over 60% of structures, leveraging Sentinel-1’s global coverage. Incorporating this satellite remote sensing approach into routine assessments could decrease the number of bridges classified as high-risk by one-third. Moreover, half of the remaining high-risk structures could benefit from spaceborne monitoring, highlighting the technique’s potential to enhance structural safety and resilience, especially in economically disadvantaged regions.

Landslides that interact with infrastructure, such as bridges, demand a comprehensive analysis to fully understand and address the complexities of this interaction. This study proposes an integrated approach that combines InSAR satellite monitoring with three-dimensional numerical modelling to analyse the effect of a landslide on a bridge. Although the case study is exemplary, the results obtained are of a general nature and applicable to similar contexts. The integration of InSAR and numerical modelling provided complementary and more detailed information compared to the isolated use of each approach. The InSAR analysis offered an overview of surface deformations, allowing for large-scale monitoring of movements, and its limitation in providing complete three-dimensional information was addressed by the numerical modelling, which enabled the decomposition of movements along the main direction of the landslide, precisely identifying the movement trajectory. The results showed predominant movements in the transverse direction, with a less significant vertical component, consistent with the observed kinematics. InSAR data allowed for the comparison of numerical modelling estimates with real observations, enhancing the consistency of the simulations. These data revealed significant movements upstream of the bridge, confirming the critical areas identified by modelling, which compensated for the lack of satellite data downstream, showing intense displacements. The modelling also highlighted significant displacements in the bridge's structural elements, with downstream tilting caused by the horizontal thrust of the landslide. The integrated approach offered a clearer understanding of landslide dynamics and their impact on infrastructure, offering a valuable tool for monitoring and risk management in vulnerable areas.

Evaluating long-term building reconstruction is essential to strengthen resilience to earthquakes. Field investigations provide detailed and accurate information for building assessments, but are often labour intensive, costly, and time consuming, particularly when considering the regional-scale impact of earthquakes. In contrast, satellite Remote Sensing (RS) techniques provide frequent data across vast areas, making them ideal for regional-scale post-earthquake assessments, which can complement field surveys. Despite this, most RS studies have relied on manual change detection of satellite data before and after the event, limiting their potential for automated assessment and reducing their support for field investigations. In this study, we developed a novel RS method designed to assist field investigations of post-earthquake building reconstruction on a regional scale. The method automatically identifies target buildings for field teams to investigate, locating collapsed structures or buildings that have changed due to post-earthquake reconstruction efforts. We applied Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) for the first time to evaluate post-earthquake building reconstruction. The proposed method involves a two-stage analysis: first, a grid-level assessment on a regional scale to detect areas with reconstruction activities following an earthquake, and then a detailed building-level analysis to identify individual buildings that have undergone changes as part of the reconstruction process within these areas. The method was used to assess building reconstruction efforts in Nepal after the 2015 Gorkha earthquake. For the MT-InSAR analysis, we acquired two stacks of 3-m-resolution SAR images, one before and one after the earthquake. The grid-level analysis detected multiple urban areas with significant changes, which were then subjected to a building-level analysis. This analysis pinpointed the locations of affected buildings and determined the extent of changes related to reconstruction activities. A comparison of the building-level results with field observations confirmed that the method successfully identified buildings that have undergone changes. These changes included buildings that were left in a collapsed state, demolished, under construction, or fully reconstructed. The MT-InSAR-based approach introduced in this study has the potential to serve as a valuable tool to guide future field surveys related to post-earthquake reconstruction, significantly reducing the time and effort needed for such assessment.

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.

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.

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.

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.

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.

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.

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.

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.