In recent years, unstable quay walls are a problem in The Netherlands. 100-year-old quay walls in cities like Amsterdam are collapsing and endanger people and property. The government needs to renovate unstable quay walls quickly. With 600 kilometre of quay wall in Amsterdam alone, this is a great challenge. Currently, unstable walls are found by deformation monitoring using tacheometry, which takes too much time for large scale monitoring. To increase both speed and coverage, a photogrammetric deformation analysis is proposed. In multiple epochs, at months interval, a series of images of the quay wall is made from a boat. In these images, feature points are identified and matched, where part of the feature points are matched across multiple epochs. All feature point observations are put in a multi-epoch least squares adjustment. This adjustment integrates both feature point observations of individual epochs and point deformations between multiple epochs. Using photogrammetry in combination with such a deformation adjustment has not been done previously, but has great advantages. The least squares adjustment allows to take the stochastic nature of the observations into account. This enables proper error propagation, such that not only quay wall stability can be assessed, but also the corresponding error budget. Results show that using two epochs 300 multi-epoch feature points can be found per square meter quay wall. With these points, sub-centimetre deformation can be estimated.

Various tasks in the construction industry are tedious due to the high amount of repetition or time-consuming nature. In recent years Deep Learning within computer vision has made it possible to automate various tasks using images. The Hoofdvaarweg Lemmer-Delfzijl has been assessed using images and a pointcloud. The images were being worked with two employees over a month. This is time-consuming and there are a lot of images to go through. Our project statement is thus: Develop a tool using computer vision techniques to reliably detect problematic corrosion on piling sheet within 4-5 months to understand what the state is of this topic for asset managers. We first start with an analysis in which we looked at the existing the literature, the data, the existing methods and how Witteveen+Bos is assessing the images. We then set the requirements to which the algorithm should adhere to. Literature study has shown that most models, with data-sets of above 3000 images, achieve above 90% for both accuracy and mean average precision. Afterwards we start writing the algorithm and model testing various model structures as part of the synthesis procedure. The models are variating in structures, filters, depth, and augmentation. We created a classifier, of four and six classes, and an object detection algorithm and conducted various evaluation techniques. The four-class classifier performed better than the six-class classifier. This could be due to the six-class classifier being made up of less data, classes that are vague, parts of the data showing imbalance problems. An object detection algorithm was created to detect dimensional features to estimate the height above water and distance of the bumps. To convert the pixel distance to actual distance, we trained the model to detect a reference object. The object detector performed well, but did not meet the requirements we set. The dimension estimation provided can only provide a rough estimation. This may be the result of not every image, in the training set, contained a reference object. Creating the data-set was a tedious task and our data-set with two classes, took around eight hours to finish training. We can conclude that for image classification, the structure of the model and the trainable parameters play a role. The object detector can count elements, but the predicted bounding box is sometimes larger than expected. Some recommendations are to increase data and classes. A robust feasibility for Witteveen+Bos regarding AI. Repurposing the algorithm for progress monitoring and exploring the interoperability between software relevant for the manager.

Photogrammetry is a well-established technique that has a significant impact on the use of images for mapping purposes. Employing feature extraction, matching, and the bundle adjustment, a large number of images can be automatically processed. Ingenieursbureau Geodelta developed an application for measurements in processed (oblique) aerial images. However, the quality of these measurements and the added value of oblique images on the adjustment's quality were unclear. This will be assessed by means of the theoretical standard deviation resulting from the bundle adjustment. Upon estimating this quality, an adapted RANSAC method is proposed that incorporates this quality as weights within its algorithm to extract geometric features. The objective is to evaluate whether this enhances the RANSAC results and could be applied to 3D reconstructions. The results indicate three key factors that influence the theoretical standard deviation: high tie point availability, larger observation angles, and image viewing direction. With this, the theoretical standard deviation for tie points in both Nadir and Oblique image sets separately approximates 3 centimeters in the horizontal direction and about 10 in the height direction. Combining the two sets enhances the results by nearly a factor of three in all directions because the Nadir images connect the Oblique images, combining the strong characteristics of both sets. This demonstrates the value of both Nadir and Oblique imagery. For image exteriors, the improvement is even more pronounced, yielding an improvement factor of 4 or 5. However, propagating this quality metric through a dense matching algorithm in an adapted, weighted RANSAC algorithm does not show significant improvements in the number of planes found or the percentage of points classified as inliers of those planes. Furthermore, the RANSAC method does not converge to a better result in fewer iterations using the proposed method.

The Eastern Scheldt storm surge barrier (ES-SSB) is the largest hydraulic structure in the Netherlands. Its semi-open inlets allow for North Sea waters to enter and leave the Eastern Scheldt estuary with each tidal cycle, and can be closed during extreme storm events. The flow through the barrier is strongly contracted, and complex flow patterns emerge. Among characteristic flow features are the shallow jet and shallow mixing layer, generated as a result of large transverse shear stresses with horizontal lengths scales greatly exceeding the water depth. Large mean velocities in combination with the developing lateral non-uniformity of the flow between slack water and maximum flood gives rise to higher bed shear stresses. A bed protection up to a distance of about 600 m from the barrier is applied to stabilize the bed against increased hydraulic loading. Scour hole development adjacent to the applied bed protection was anticipated for, but expected equilibrium depths have not yet been reached. Reaching local depths of 60 m with respect to the water surface, these scour holes may on the long term be a threat to the stability of the barrier. Broekema (2020) concluded that during flow contraction, separation of flow near the bed of the scour hole is suppressed and high flow velocities in streamwise direction are found near the bed. Cyclic variations in lateral non-uniformity affect turbulence intensities and subsequent mixing of mass and momentum. Scour growth is therefore enhanced in two ways: i) velocities in the main flow remain high due to horizontal flow convergence, and ii) lateral velocity gradients are associated with larger turbulence intensities that are likely leading to larger bed shear stresses...

The demand for commercial aviation is growing at an annual rate of 4%, posing significant environmental challenges. Large-capacity aircraft designed for long ranges are often used on short-to-medium range routes, leading to inefficient fuel consumption and higher emissions. With a future market need for aircraft seating 211 to 300 passengers, there is a clear gap for such a passenger airliner. This report examines the feasibility of the X-300 EcoFlyer, a proposed short-to-medium range aircraft with reduced environmental impact. Designed to carry 300 passengers over 3000 km, the X-300 aims to achieve 25% lower CO2 emissions, 50% lower NOx emissions, and 20% lower noise emissions compared to the Airbus A320neo. The study covers aircraft functions, system design, performance analysis, manufacturing, sustainability, operations, logistics, business viability, and technical risks. The findings confirm the X-300 EcoFlyer's potential to meet future demands with lower environmental impact. Innovations include a noise-shielding fuselage, a water-injected turbofan engine, in-wheel electrical taxing, and an electrical environmental control system. Overall, the X-300 EcoFlyer represents a promising solution to the challenges facing the future of high-capacity air transport.

The demand for commercial aviation is growing at an annual rate of 4%, posing significant environmental challenges. Large-capacity aircraft designed for long ranges are often used on short-to-medium range routes, leading to inefficient fuel consumption and higher emissions. With a future market need for aircraft seating 211 to 300 passengers, there is a clear gap for such a passenger airliner. This report examines the feasibility of the X-300 EcoFlyer, a proposed short-to-medium range aircraft with reduced environmental impact. Designed to carry 300 passengers over 3000 km, the X-300 aims to achieve 25% lower CO2 emissions, 50% lower NOx emissions, and 20% lower noise emissions compared to the Airbus A320neo. The study covers aircraft functions, system design, performance analysis, manufacturing, sustainability, operations, logistics, business viability, and technical risks. The findings confirm the X-300 EcoFlyer's potential to meet future demands with lower environmental impact. Innovations include a noise-shielding fuselage, a water-injected turbofan engine, in-wheel electrical taxing, and an electrical environmental control system. Overall, the X-300 EcoFlyer represents a promising solution to the challenges facing the future of high-capacity air transport.

The demand for commercial aviation is growing at an annual rate of 4%, posing significant environmental challenges. Large-capacity aircraft designed for long ranges are often used on short-to-medium range routes, leading to inefficient fuel consumption and higher emissions. With a future market need for aircraft seating 211 to 300 passengers, there is a clear gap for such a passenger airliner. This report examines the feasibility of the X-300 EcoFlyer, a proposed short-to-medium range aircraft with reduced environmental impact. Designed to carry 300 passengers over 3000 km, the X-300 aims to achieve 25% lower CO2 emissions, 50% lower NOx emissions, and 20% lower noise emissions compared to the Airbus A320neo. The study covers aircraft functions, system design, performance analysis, manufacturing, sustainability, operations, logistics, business viability, and technical risks. The findings confirm the X-300 EcoFlyer's potential to meet future demands with lower environmental impact. Innovations include a noise-shielding fuselage, a water-injected turbofan engine, in-wheel electrical taxing, and an electrical environmental control system. Overall, the X-300 EcoFlyer represents a promising solution to the challenges facing the future of high-capacity air transport.

The demand for commercial aviation is growing at an annual rate of 4%, posing significant environmental challenges. Large-capacity aircraft designed for long ranges are often used on short-to-medium range routes, leading to inefficient fuel consumption and higher emissions. With a future market need for aircraft seating 211 to 300 passengers, there is a clear gap for such a passenger airliner. This report examines the feasibility of the X-300 EcoFlyer, a proposed short-to-medium range aircraft with reduced environmental impact. Designed to carry 300 passengers over 3000 km, the X-300 aims to achieve 25% lower CO2 emissions, 50% lower NOx emissions, and 20% lower noise emissions compared to the Airbus A320neo. The study covers aircraft functions, system design, performance analysis, manufacturing, sustainability, operations, logistics, business viability, and technical risks. The findings confirm the X-300 EcoFlyer's potential to meet future demands with lower environmental impact. Innovations include a noise-shielding fuselage, a water-injected turbofan engine, in-wheel electrical taxing, and an electrical environmental control system. Overall, the X-300 EcoFlyer represents a promising solution to the challenges facing the future of high-capacity air transport.

The railway industry is constantly growing to meet the demand of society for stable, accessible and sustainable transportation. With this growth, the need for the railway to be reliable increases, requiring frequent surveying and maintenance. Fugro's RILA (Rail Infrastructure aLignment Acquisition) mobile mapping system contributes by making the surveying more accessible to the relevant railway network stakeholders. However, the system has its limitations in environments where the GNSS (Global Navigation Satelite System) signal is occluded, such as tunnels and underground stations. The geo-data collected by RILA in those areas is poorly georeferenced due to the poorly tracked trajectory of the system, which introduces spatial data misalignment up to a meter or more. The current methods to fix data misalignment rely on manual data corrections, which is not cost-effective, or on automatic solutions, which have limited applicability. Thus, the aim of this research is to develop an improved trajectory optimization method, thereby ensuring accurate geo-referencing and alignment of the survey data. This thesis proposes a newly developed methodology to achieve this aim: features are extracted from point cloud surveys, matched and utilized by g2o optimizer and GNSS processing software to optimize the trajectory. The development is described and results are evaluated on two different scales - locally, within a point cloud tile and globally, within a sequence of tiles. It is done by using Glasgow's underground railway network as a test case. Results from the implementation demonstrate significant improvements in trajectory accuracy - a misalignment of point cloud data was reduced from a 1.5 m to a cm level within an optimization time frame that took approximately 10 hours. This improvement in accuracy was present under different complex environments using both the local and global versions of the algorithm. However, the area near the railway tunnel entrance saw a limited benefit from the implementation of the proposed algorithm. In conclusion, the developed trajectory optimization algorithm optimizes the trajectory and improves the alignment of the survey data. Moreover, the method outperforms the currently employed solutions by being automatic and applicable in different environments. However, further research is required to optimize the algorithm itself (accuracy and computationally speed of the algorithm) and to more accurately define its limitations in terms of the surveyed environments.

There are multiple types of objects distributed along the rail trackside to support the operation of railroads. Damage to those objects will result in delay and cancellation of trains. Therefore, the maintenance of the objects is important to ensure the safety of railway operations, and the key to maintenance is to know the accurate position of the objects. RILA - a railway mobile mapping system developed by Fugro - provides continuous monitoring of the rail environment without interrupting the daily operation of rail tracks. The RILA dataset contains different types of data including video frames, point clouds, and measurement trajectories from the Global Navigation Satellite System and Inertial Measurement Unit (GNSS-IMU). This enables a remote and automatic object positioning process based on object recognition in video frames and positioning using photogrammetric techniques. Most of the rail trackside objects are parts of the railway electrification system and the railway signaling system. The available ground truth data contains more information about signaling objects, so this thesis mainly focuses on the positioning of signaling objects. Three typical components: signal lights, railway cabinets, and railway side markers are selected to make case studies and develop the object detection and localization pipeline for them. There are two main issues that existed in former research: One is object recognition only gives a 2D pixel position for an object, but we want to know its 3D position in the world; the other is the contextual information between frames is ignored in object positioning. Based on that, this thesis developed a new workflow: firstly, this thesis proposes the use of an existing 3D object detection model, Single-Stage Monocular 3D Object Detection via Keypoint Estimation (SMOKE), to recognize objects of interest from the RILA video frames. This model can not only predict an object's 2D pixel position on the image, but also its 3D position with respect to the camera. This enables the localization of an object in each of the frames it is detected in, which provides redundancy to define its final position. Secondly, this thesis developed a pipeline to clean the detection output of SMOKE so that most false detections can be removed. In addition, this thesis developed a method to use the contextual information in image sequences to position objects: first, extract the sequences by classifying all the predictions in the detection output through the Euclidean distances, then for each sequence, analyze the varying trend of predicted positions to select reliable positions. The mean of the selected positions is the final improved position of that object. By making tests on a part of the railway near Ely, UK, with 2983 frames, the workflow detected and positioned all of the 4 signal lights, all of the 10 markers, and 13 of the 14 cabinets in the testing dataset. Most signal lights and cabinets can be positioned within 1 meter to the ground truth, and the mean positioning offset for the railway side markers can be controlled around 2 meters. Complex scenarios could be solved, when up to three cabinets were located close together.