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 1.06 ± 0.10 mean absolute error in the pixel-based PS count estimation on the test data split, with over 80% of predictions within ± 1 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.@en

Dykes provide protection from the risk of flooding to approximately 60% of the Netherlands, by area. Unfortunately, most of the country’s major cities lie within this zone, placing a large proportion of the population at risk should these flood defences fail. In total the Netherlands have over 3800 km of primary flood defences protecting the coast and inland river systems with another 14000km protecting individual polders. Recent flood events in Europe caused by extreme rainfall have raised concerns about the ability of the Dutch river dykes to resist similar events. This paper describes a quantitative assessment of the vulnerability to flooding of a primary river dyke outside Dordrecht. The dyke has shown significant signs of distress over recent years developing large tension cracks during periods of drought on its leeward side, which have progressively increased in size. It is regularly inspected by the local water authority due to its concerning visible deterioration. In this study, soil uncertainty is quantified from in-situ geotechnical tests and laboratory tests. Several failure mechanisms are then considered probabilistically, namely global stability, rapid drawdown, internal erosion and overtopping. Fragility curves are generated for each failure mechanism describing how the probability of failure would change if a given flood level were to occur, event trees are used to link the individual failure mechanisms and quantify the system probability of failure. This paper illustrates the use of fragility curves for earthwork asset management and advantages and limitations of the methodology utilised are discussed.@en

The paper describes a study on the liquefaction potential of flood defences along the rivers running through the delta area of the Netherlands. The study concentrates on an area south of Rotterdam. The dykes used as primary flood defences protect an urban, rural, and industrial area of 102,400 ha. In this paper the data from more than 4200 Cone Penetration Tests, CPT traces are used to assess more than 200 km of dykes. The pore pressure, u2 data is analysed, then used to separate the material response into contractive and dilative zones. Using the separation of liquefaction susceptible soils, and geometry of the riverbed a regional hazard map is generated. The choices for the data visualisation and their effect on the generated map are discussed and presented. The final liquefaction susceptibility map is used by the water governing authority Waterschap Hollandse Delta as a decision-making tool to improve the efficacy of liquefaction hazard assessment such as the location and return period of bathymetry measurements, and the scale of site-and laboratory investigation.

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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 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

Aged earthworks constitute a major proportion of European rail infrastructures, the re-placement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multi‐attribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization.

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Earthworks such as cuttings and embankments account for a major part of the entire transport network infrastructure. Large parts of that infrastructure in Europe are susceptible to a range of geohazards, landslides being the most prevalent. These landslides frequently result in direct damage to assets, deaths and injuries, while indirectly also leading to traffic disruptions. There is a need therefore to identify critical assets where remediation efforts should be prioritised in order to prevent such events from occurring. Current state of the art practice involves using qualitative risk matrices, where the hazard and consequence components are determined through subjective visual survey observations. Landslide hazard analysis determines the spatial (susceptibility) and temporal probability of landslides of a certain intensity occurring over an observed area. A number of quantitative methods for landslide hazard and risk assessment have been developed recently; generally these methods are considered more effective due to their reduced subjectivity and their consideration of additional factors. A number of studies outline the application of these methods to natural terrain, but to date these methods have not been developed for transport network earthworks. This study presents and compares the results of two landslide susceptibility analysis approaches for cuttings and embankments on a section of Irish Rail network. The first, “geotechnical” approach uses probabilistic slope stability calculations to rank the assets by their reliability index. The second, “statistical” or “data driven” approach, uses logistical regression as a statistical tool to obtain the susceptibility ranking of the earthworks, using the database of previous failures on the network as an input. Furthermore, several methods for obtaining the temporal hazard characteristics are presented and applied, these methodologies combine to provide a full hazard assessment map of the network.@en

A significant proportion of European rail networks are built upon earthworks that are over one hundred years old. These earthworks are under increased pressure as they have to contend with heavier and more frequent traffic, far outside the scope of their design. To compound this problem further, recent years have seen unpredictable weather patterns develop with prolonged intense rainstorms commonplace. This has led to increased incidence of slope failures along rail networks, as many aged earthworks struggle to withstand such drastic changes in loading. Marginal engineered slopes fail depending on the triggering mechanism which presents itself first. Therefore the failure surface is intrinsically linked to the applied load i.e. surcharge loading will instigate a different type of landslide than prolonged rainfall. Therefore this paper proposes to analyse marginal slopes probabilistically as a system, where multiple slip circles are considered. A multi-modal optimisation algorithm LIPS (locally informed particle swarm optimisation) is used to locate all significant slip circles. In a slope with multiple potential failure surfaces the consequence of failure is not necessarily the same across the different slip surfaces. This paper addresses this deficit by examining the consequence of the different landslides should they occur. When combined with previously calculated probabilities of failure this will entail amount to a full geotechnical risk assessment of engineered slopes.@en

Landslides cause hundreds of deaths and billions euros of damage to infrastructure and the environment each year. In order to predict the locations most susceptible to landslides, the field of landslide hazard assessment has gone through a massive development in the last twenty years by introducing a wealth of statistical and geotechnical landslide susceptibility models. However, these efforts have been largely restricted to landslides occurring in natural terrain even though landslides occurring on geotechnical assets on transportation networks can result in even greater consequences. Current risk assessment approaches for earthworks on large transportation networks still largely take form of subjective risk matrices with inputs gathered by visual walkover surveys using data stored in an asset database. This paper shows the application of two distinctive objective landslide susceptibility approaches on a case study of Irish rail. The first is a ‘statistical’, or ‘data-driven’ approach uses logistic regression as a statistical tool to establish the influence of slope-describing variables that have led to landslide occurrence. This approach draws the data from the asset database containing records of slope variables, and the adjoining landslide register. The same asset database is used as a basis for the second, ‘geotechnical’ or ‘deterministic’ approach. In this approach, geometrical and geotechnical properties of each slope are used to carry out probabilistic slope stability analysis, resulting in probability of failure for each slope. Both approaches result in susceptibility zoning for earthwork assets across the network, effectively ranking them in the criticality terms. This study compares the requirements, applicability and outcomes of each approach, and discuss the methods needed for developing each of them into hazard and risk assessments.@en