Scour has become one of the most significant hazards affecting masonry bridges. Existing scour monitoring techniques often fail to meet the requirements for continuous and time-domain tracking of scour evolution. Moreover, existing scour early-warning systems predominantly rely on threshold-based risk assessment and management frameworks. A critical limitation of these systems is the difficulty in accurately defining threshold values, which are often derived from historical monitoring experiences. Given the variability in foundation conditions and river scour characteristics across different bridges, standardized threshold setting is highly challenging. Furthermore, threshold determination often lacks correlation with the health condition of the superstructure, which is an aspect engineers care most about. These deficiencies highlight the urgent need for a more intelligent monitoring framework that can integrate multiple monitoring techniques and facilitate interactive associations between monitoring data and the health condition of the superstructure.

This study explores the use of digital twin (DT) technology to overcome the shortcomings of current monitoring and maintenance strategies. By integrating real-world monitoring measurements with finite element modeling, the DT framework provides the opportunity to simulate "what-if" scenarios under high-fidelity conditions. Such advancements offer novel prospects for detecting scour-induced damage and intervening for the maintenance. This study utilizes DT technology within the context of a scour monitoring project for a masonry bridge in Northern Ireland, United Kingdom. A digital twin-based SHM and maintenance framework is developed to achieve seamless communication between the virtual model and the physical structure using sensor data. The developed model addresses limitations associated with traditional monitoring and maintenance approaches and demonstrates the potential of digital twins in forward model calibration and backward decision-making.

In the Netherlands, there is a significant backlog of infrastructure maintenance and deferred projects, exacerbated by the increasing demand for infrastructure renovation funds due to bridges reaching the end of their life cycles. Monitoring and assessing bridges typically involve visual inspections, which are subjective, expensive, and time-consuming. Remote sensing techniques, particularly Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR), offer a quick and objective solution for monitoring and analysis. MT-InSAR shows promise for low-cost, network-wide continuous structural monitoring. However, there is no defined assessment method with specific damage indicators linked to bridge failures that can be applied to various types of bridges. This thesis presents a weighted multilevel assessment method using MT-InSAR techniques. In the first level, four damage indicators (DIs) are proposed and analyzed on a point spatial scale: velocity DI, relative velocity DI, six-month velocity DI, and deviation from mean time series DI. In the second level, three DIs are proposed and analyzed on a grid cell spatial scale: velocity DI, relative velocity DI, and mean cumulative displacement DI. In the final level, two DIs are proposed and analyzed along the length of the bridge: largest differential displacement DI and deflection ratio DI. This method is applied to two bridges, each facing distinct issues: settlement and fatigue. The main findings demonstrate that the proposed damage indicators can be effectively utilized in the assessment system to qualitative rate the two case studies. The assessments reveal slow downward movements and large localized upward movements, which characterize the distinct underlying issues. The capabilities of the assessment method show promise for simultaneously evaluating various types of bridge. Additionally, it was found that imposing a coherence (quality) constraint on the MT-InSAR data is not advisable, as it may filter out the most critical data. Furthermore, the data indicates a potential seasonal unwrapping error, significantly affecting the analysis. The proposed method confirms the potential capabilities of MT-InSAR techniques in bridge assessment and suggests that this approach could serve as a foundation for network-level bridge assessments in the future, contributing to a much-needed early warning system.

Understanding the propagation of a flood is crucial for effective emergency response measures. While traditional numerical models provide reliable flood simulations, their high computational costs pose significant limitations during emergencies. Deep learning models have recently demonstrated potential in accelerating hydrological calculations while preserving high accuracy. Although various deep learning flood models have been developed, many are limited to specific case studies or neglect the dynamic propagation of flood waves, constraining their application during emergencies. To address this, Bentivoglio et al., 2023 proposes the use of a physics-based surrogate model for spatio-temporal flood modelling; the shallow water equation graph neural network (SWE-GNN). The model demonstrates promising results on small virtual landscapes, showcasing strong generalizability to unseen breach locations and domains, while achieving computational speed ups. In this research, the real-world applicability of the SWE-GNN for time-sensitive situations is analyzed. Two dike rings in the Netherlands are selected as our case study areas. The model is trained and tested within the same domain to evaluate its application during a crisis. Performance is assessed using statistical metrics and practical evaluations, including direct and indirect damage models. The SWE-GNN model is able to correctly predict the spatio-temporal evolution of floods for unseen breach locations. The mean average errors in time are of 0.027 m and 0.029 m for water depth and of 0.007 m^2/s and 0.006 m^2/s on units discharge. The resulting flood maps prove viable for practical applicability, presenting good results for both direct as indirect damage assessment. Additionally, the SWE-GNN demonstrates a speedup of roughly 5 to 6 times for the test case areas compared to a traditional numerical model. In this project, we affirm that the SWE-GNN represents a promising innovation for a new approach to time-sensitive flood modeling, providing a reliable alternative to numerical models in situations with time constraints.

Making decisions when future conditions are uncertain is a challenging endeavor. This thesis develops a framework to analyse flood risk and create Dynamic Adaptive Policy Pathways, which can provide insights in the behaviour of flood risk protection measures in many future scenarios. The pathways are used to identify robust measures, dead ends and compare measures. The framework is applied to a case study in the Galveston Bay area.