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.

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.

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.