Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

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.Geo-engineerin

Development Of A Neural Network Embedding For Quantifying Crack Pattern Similarity In Masonry Structures

The degree of similarity between damage patterns often correlates with the likelihood of having similar damage causes. Therefore, deciding whether crack patterns are similar is one of the key steps in assessing the conditions of masonry structures. To our knowledge, no literature has been published regarding masonry crack pattern similarity measures that would correlate well with assessment by structural engineers. Hence, currently, similarity assessments are solely performed by experts and require considerable time and effort. Moreover, it is expensive, limited by the availability of experts, and yields only qualitative answers. In this work, we propose an automated approach that has the potential to overcome the above shortcomings and perform comparably with experts. At its core is a deep neural network embedding that can be used to calculate a numerical distance between crack patterns on comparable façades. The embedding is obtained from fitting a deep neural network to perform a classification task; i.e., to predict the crack pattern archetype label from a crack pattern image. The network is fitted to synthetic crack patterns simulated using a statistics-based approach proposed in this work. The simulation process can account for important crack pattern characteristics such as crack location, orientation, and length. The embedding transforms a crack pattern (raster image) into a 64-dimensional real-valued vector space where the closeness between two vectors is calculated as the cosine of their angle. The proposed approach is tested on 2D façades with and without openings, and with synthetic crack patterns that consist of a single crack and multiple cracks.Geo-engineerin

Proving compliance of satellite InSAR technology with geotechnical design codes

In the planning stage of new infrastructure or when designing renovation of existing infrastructure, information about existing slope movements or settlements is essential to make informed design decisions. InterferometricSynthetic Aperture Radar (InSAR) techniques can be of value to identify these risks in an early stage of a project. InSAR can offer insight into the surface movements of an area from historic archives using satellite-based SARdata. Furthermore, InSAR observations can help identify zones with displacements larger than the average of an area, and be used to plan future soil investigation more effectively. Thanks to their high temporal and spatial resolution, InSAR observations can also complement in situ conventional monitoring during the construction and operational stage. Despite these possibilities, the use of InSAR is not yet standard practice in geotechnical projectsand no formal guidelines are currently available to inform engineers, planners and infrastructure stakeholder on the use of InSAR-based monitoring within geotechnical design codes. Here we provide an operational framework for the practical integration of InSAR monitoring into current geotechnical design codes, such as Eurocode-7, for all project stages. The proposed framework is then demonstrated for the planning stage of a highway renovation project, focusing on an area potentially subjected to landslides where no conventional monitoringdata was available at this stage. We concluded that the proposed framework is a practical and operationaltool that can be used by planners and engineers in the whole lifecycle of an infrastructure project.Hydraulic Structures and Flood RiskGeo-engineeringMathematical Geodesy and Positionin

Multi-Temporal InSAR for transport infrastructure monitoring: Recent trends and challenges

Worldwide, transport infrastructure is increasingly vulnerable to ageing-induced deterioration and climate-related hazards. Oftentimes inspection and maintenance costs far exceed available resources, and numerous assets lack any rigorous structural evaluation. Space-borne Synthetic Aperture Radar Interferometry (InSAR) is a powerful remote-sensing technology, which 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 the InSAR accuracy to be comparable with traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility to support 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 reality. This paper identifies and discusses the challenges of using InSAR for the purpose of structural monitoring, with a specific focus on bridges and transport networks. Examples are presented to illustrate current practical limitations of InSAR; possible solutions and promising research directions are identified. The aim of this study is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges.Geo-engineerin

Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment

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.Geo-engineerin

Reply to Lanari, R., et al. comment on “pre-collapse space geodetic observations of critical infrastructure: The morandi bridge, Genoa, Italy” by Milillo et al. (2019)

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.Geo-engineerin

Integrated InSAR monitoring and structural assessment of tunnelling-induced building deformations

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.Geo-engineerin

Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades

This paper proposes an automated approach to predict crack pattern similarities that correlate well with assessment by structural engineers. We use Siamese convolutional neural networks (SCNN) that take two crack pattern images as inputs and output scalar similarity measures. We focus on 2D masonry facades with and without openings. The image pairs are generated using a statistics-based approach and labelled by 28 structural engineering experts. When the data is randomly split into fit and test data, the SCNNs can achieve good performance on the test data ((Formula presented.)). When the SCNNs are tested on ”unseen” archetypes, their test (Formula presented.) values are on average 1% lower than the case where all archetypes are ”seen” during the training. These very good results indicate that SCNNs can generalise to unseen cases without compromising their performance. Although the analyses are restricted to the considered synthetic images, the results are promising and the approach is general.Geo-engineerin

Discussion: Effect of soil models on the prediction of tunnelling-induced deformations of structures

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Geo-engineerin

Roadmap on measurement technologies for next generation structural health monitoring systems

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.Geo-engineerin