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.

Slow-moving landslides are natural phenomena that shape mountainous landscapes over long time scales. These landslides creep at rates ranging from meters to millimeters per year, often unnoticed by people who build houses on them, unaware of the risks. Although slow-moving landslides rarely cause fatalities, they can be triggered into fast-moving landslides by earthquakes, leading to catastrophic outcomes. Therefore, understanding and monitoring these landslides is crucial to mitigate potential hazards, particularly in seismically active regions.

Traditional methods for identifying and monitoring slow-moving landslides rely on fieldwork and in-situ instruments. While these methods are accurate, they are limited in spatial and temporal coverage. Recent advancements in remote sensing, particularly Interferometric Synthetic Aperture Radar (InSAR), offer extensive, high-resolution monitoring capabilities, enabling detailed observations of ground movements. InSAR studies have used Line Of Sight (LOS) velocities to monitor the be- havior of slow-moving landslides. However, these findings do not provide details on the direction of movement, as LOS velocities are always measured along a single dimension. This limitation makes it impossible to determine the direction and type of movement, which are essential for accurate risk as- sessment and developing early warning systems by identifying deformation patterns that may indicate imminent failure.

This thesis aims to investigate the temporal evolution of a slow-moving landslide subjected to an earthquake, focusing on changes in rate and direction, to identify the type of movements. To achieve this, the Multi-Temporal InSAR (MT-InSAR) technique is used, resulting in Persistent Scatterers (PSs) as measuring points to observe changes in the rate and direction of landslide movements post- earthquake. Sentinel-1 data from both ascending and descending orbital configurations were used to obtain LOS velocity time series at the PSs both before and after the earthquake. By combining LOS velocities from both PS orbital configurations, the method decomposed LOS velocities into vertical and horizontal components, providing both changes in velocities and direction, which together enabled the identification of the type of movement.

To test this method, a slow-moving landslide located in Büyükçekmece, Istanbul, was chosen as a case study. This landslide was impacted by a Mw 5.7 earthquake on 26 September 2019. Within the landslide, an urbanized area was selected to analyze both the uniform and spatially variable re- sponses. The uniform results involved averaging the velocities at the PSs, while the individual PSs were analyzed to identify spatially variable movement patterns.

The uniform results indicated three main phases in landslide behavior after the earthquake: an initial translational movement following the earthquake, a roto-translational phase combining rotation and translation, and a new constant state characterized by horizontal spreading. At an individual point level, the results showed that most points transitioned from moving vertically downward to upward, indicating a significant change in underlying mechanisms, highlighting the landslide’s complexity.

The monitored behavior resembles that of an inverse creeping landslide, where after the initial disturbance, the landslide decelerates to a new constant state. Based on the collected real data, it is possible to construct hypothetical risk scenarios with behavior curves characterized by continuous acceleration, which would pose an immediate threat of catastrophic failure. Although this earthquake did not cause the Büyükçekmece landslide to fail, and the landslide decelerated to a constant state, it is crucial to continue monitoring as there are signs of instability and conditions may change.

Structural health monitoring of buildings is useful for a few reasons. It provides information on the usage and cause of damage to a building. This can result in targeted maintenance or allow for potential improvements.

Traditionally, a building can be monitored by installing sensors during construction or maintenance work. These can be strain gauges, inertial measurement units or surveying equipment. However, not all buildings have such sensors installed due to lack of space or the cost of the equipment. To overcome this, a spaceborne technique shows potential, namely multi-temporal interferometric synthetic aperture radar (MT-InSAR). This technique has already been applied to monitor damage to sections of buildings or large structures, for instance facades or deformations of bridges. In the context of entire buildings, the result of an MT-InSAR analysis has not yet been paired with a computational model. This is mainly due to the relatively small scale of a building and low spatial density of the displacement data.

This thesis integrates remote sensing data, acquiring displacements due to mining, with a computational structural finite element model of a church structure. The displacements have been interpolated using MT-InSAR data, which contains projections of nonlinear displacements in vertical and West-East directions. The interpolation has been performed using two techniques. The first is Ordinary Kriging, which is used to obtain a general insight into the deformations and the shape of the deformed region near the church. The second uses the least squares method to fit polynomial shape functions. The resulting displacements of the least squares analysis have been integrated into a nonlinear structural finite element model. The structural model consists of a soil-structure interaction model and nonlinear material properties, and is used to assess crack propagation.

Integrating remote sensing with computational modelling, shows potential in providing a monitoring technique for buildings. The interpolation method can be used to obtain displacements at a building, even when the spatial density of the InSAR analysis is limited. The main limitation is the information on the horizontal displacements obtained by the InSAR technique, where the displacement along the North-South is unknown. Furthermore, the structure and integration can then be performed using a finite element model, which can follow crack propagation and account for soil-structure interaction.

Cracks in masonry structures are a cause for concern as they signal a potential lack of functionality and/or aesthetics. It thus becomes important to identify the cause of damage in order to mitigate it and to prevent its occurrence in the future. Similarities in crack patterns may correlate to similarities in the damage cause. Currently, the assessment of similarities in crack patterns and their corresponding damage causes is done by masonry experts and structural engineers. This process is often expensive and subjective. The use of a Convolutional Neural Network (CNN) may offer an alternate robust and dependable means to automate the assessment of masonry crack patterns by processing their images.

The main research goal of this MSc thesis is to answer how accurately can the CNN -- fitted to data generated from finite element models -- estimate masonry crack pattern similarities. To develop a neural network that can perform such an automated assessment of masonry crack patterns with a high degree of accuracy, a large number of crack patterns with similarity ratings given by human experts are required. This data is collected in increasing complexity, first from a statistics-based approach by generating synthetic crack patterns from Markov walks. This is followed by a computational physics-based approach, such as the Finite Element Method (FEM), that generates crack patterns on 2D masonry façades subjected to differential settlements and out-of-plane loads. Finally, real-world data is also collected. This data is used to fit and test a convolutional neural network developed by Kleijn (Kleijn, 2022). Continuing along the previous line of research done at TNO (where 12 crack patterns were chosen and developed using the statistics-based approach), this thesis focuses on developing parametric finite element models of 8 out of these 12 Pattern IDs. Additionally, real-world images are also collected from Gouda in The Netherlands. This data is then used to form crack pattern image pairs that can be assessed for their similarities by 28 raters using three similarity label categories: crack pattern similarity label, damage severity label, and the overall similarity label. Using these labels, the raters assessed 2587 image pairs generated from the statistics-based approach, 500 image pairs from the computational physics-based approach, and 50 image pairs from the combination of images from the statistics-based approach, computational physics-based approach, and the real-world cases.

An inter-rater agreement analysis is performed on the similarity assessments using Krippendorff’s alpha measure. Additionally, the agreement of each rater with a chosen standard rater is studied using Lin’s Concordance Correlation Coefficient (CCC). Using Lin’s CCC, the intra-rater agreement is also assessed for the standard rater to see how consistent a rater is with their own annotations. These labelled image pairs are then used to fit and test the regression neural network to evaluate its accuracy in predicting the similarity labels. The neural network is also fitted to and tested with various combinations of labelled data to study its generalisability.

It is found that in all three sets of data, Krippendorff’s alpha is less than 0.80 for all the labels, which indicates an insufficient agreement among the raters. It is also seen that, in general, agreement among the raters increases with their experience level, i.e. the descending order of agreement within the rater group is: industry experts, PhD students, and MSc students. Studying the Lin’s CCC of each rater’s performance compared to that of the standard rater helps to choose the raters who can be considered as reliable as the standard rater. Additionally, the intra-rater agreement analysis of the chosen standard rater shows that the highest self-consistency (agreement) is achieved for the crack pattern similarity label, followed by the overall similarity label and finally the damage severity label, with corresponding Lin's CCC values of 0.96, 0.86 and 0.72, respectively.

The neural network is tasked to predict the similarity level in each similarity rating for each image pair in the test sample. The ground truth of this neural network is established by averaging the similarity ratings given to each image pair by multiple raters. It is found that the neural network is able to achieve a sufficiently high degree of accuracy when fitted to and tested with all the image pairs generated from the computational physics-based approach. The crack pattern similarity label, the damage severity label, and the overall similarity label achieve an accuracy of 87%, 82%, and 69%, respectively. However, the generalisability experiments on the neural network that consist of predicting the similarity of a type of crack pattern image pair that is not included in the fitting data set, show very poor performance with respect to the prediction accuracy of the similarity labels. When the neural network attempts to predict the similarity of Pattern ID or a façade geometry that it did not see in the fitting procedure, it predicts all three labels with an accuracy that varies from 40% to 50%. Additionally, the neural network is also fitted to images generated from the computational physics-based approach and then tested with a pool of image pairs generated from the statistics-based approach, computational physics-based approach, and real-world images. The average accuracy with which the three similarity labels are predicted is even lower, lying between 25% and 40%.

This MSc thesis concludes that the neural network fitted to data generated from the computational physics-based approach and assessed by all the raters is able to predict the crack pattern similarity label, the damage severity label and the overall similarity label with sufficiently high degrees of accuracy. However, the generalisability experiments on the neural network show very poor results. This indicates that in order to achieve a greater prediction accuracy, the neural network may need to be fitted to a considerably larger sample of crack patterns that covers all of the relevant situations. Furthermore, the substantial inter-rater variability in the labelling of crack pattern image pairs suggests that even an ideal neural network architecture may not be able to overcome the inconsistencies in the fitting data.

Glacial lake outburst floods (GLOFs) are outbursts caused by the failure of glacial lake moraine dams. Longer ongoing processes, such as moraine dam degradation, or instantaneous events, such as landslides, can trigger dam failure. GLOFs have a catastrophic downstream impact leading to significant economic damages and more than 12000 casualties worldwide until 2015, with Bhutan and Nepal being impacted the most. Climate change causes increasing temperature and precipitation, leading to the expansion of glacial lakes and the destabilisation of glaciers, slopes and moraine dams. Consequently, GLOFs are likely to become more frequent, and glacial lakes require continuous monitoring and analysis to understand and predict GLOF-related hazards.

Since glacial lakes often lie in inaccessible mountainous regions, on-site monitoring is challenging and remote sensing proposes a safe and cost-effective solution. Satellite radar is unaffected by nighttime and clouds, enabling continuous displacement measurements. Interferometric synthetic aperture radar (InSAR) using Sentinel-1 data from 2014 to 2021 was applied at six Himalayan glacial lake areas (Imja, Lunana, Barun, Rolpa, Thulagi and Lumding) to identify potential GLOF hazards and to investigate InSAR's capability as a monitoring tool. Optical, meteorological and topographical data were used to aid in interpreting the InSAR observations; linking displacements to potential hazards and evaluating the limitations of an InSAR-based analysis.

Significant deformation was detected at the terminal moraines of Imja, Thulagi, Rolpa, Lunana and Barun Lakes; on lateral moraines at Rolpa and Lunana Lakes; and on rock glaciers at Imja, Rolpa, Barun and Lunana Lakes. In addition, significant seasonal variation could be distinguished, showing the impact of temperature and precipitation on geomorphological processes and potential hazard developments at glacial lakes. InSAR-related limitations arose in regions with significant topographic variations, extant snow or vegetation covers, and rapid displacements.

This study demonstrates the capability of satellite InSAR as a glacial lake monitoring tool. An InSAR-based analysis is instrumental in highlighting areas from where GLOFs could originate, requiring mitigation measures or further investigation to map the impact of failure. By extending the research frame over multiple years, continuous and long-term monitoring could demonstrate the climatic influence on displacements and GLOF trigger developments.