The railway transportation system is a critical infrastructure that supports the functionality of a country through ways like the transportation of people from point to point to carry out their daily activities i.e., work, study, and leisure; the timely transportation of freight within a country or cross-borders, contributing to economic development, etc., thus it has to be effective in its operation and service provision. Normal operation can get affected when railway systems such as trains, signalling systems, etc. fail to operate, railway infrastructures are damaged, occurrences of collisions or derailment. These events result in inconveniences caused to railway users, loss in service confidence and decline in revenue. Hence, railway organisations continuously put in effort to reduce the occurrence of railway disruptions that are within their means to prevent. However, there have been reports on railway disruptions caused by events that are uncontrollable within the means of the organisations. The railway transportation system faces continuous ‘threats’ despite the continuous effort put in by railway organisations to provide mitigation measures. This thesis has classified these ‘threats’ as expected and unexpected. Expected ‘threats’ refer to events whereby their potential of disrupting the operation of the railway transportation system is anticipated, thus mitigation measures can be implemented in advance so as to eliminate or prevent the event from happening. On the other hand, unexpected ‘threats’ refer to events whereby the occurrences cannot be controlled and sometimes it can be unpredictable. The objective of this thesis is to study how the resilience of a railway organisation, in view of the occurrences of 2 unexpected external events (i.e., climate change and cyber-attack), can be assessed by incorporating the elements of the 4R Resilience Framework to its operation and management. The assessment work is carried out through a semi-qualitative tool created by this thesis which adopts a systematic approach by making use of a 5-stages cycle framework.

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a methodology could lead to timely inspections and interventions, resulting in significant economic benefits and preventing further damage, including potential failure of the structure in use. The approach involves a literature review to establish relevant background knowledge and useful concepts. From this, a methodology is developed utilizing unsupervised machine learning, specifically Gaussian Mixture Models (GMM), to identify abnormal behavior indicative of structural damage. A Finite Element Method (FEM) model of a simple bridge is created and monitored over a three-year period, serving as a testing ground for the methodology and a primary source for data generation. Temperature data and its effects on the natural frequencies of the bridge model are used to establish a baseline for normal or healthy behavior. Synthetic damage, such as settlement and stiffness reduction, is then introduced to the model to create anomalies or abnormal behavior. The developed methodology is tested using three case studies, each with varying types of synthetic damage. By using both the healthy and unhealthy data generated from the model, the healthy behavior of the bridge is captured using GMM. The model then progressively incorporates unhealthy data into the proposed anomaly detection algorithm. The algorithm evaluates the likelihood of each incoming data point of belonging within the healthy distribution, resulting in data points being classified as either healthy or flagged as abnormal. The case studies presented in this research underscore the efficacy of the proposed anomaly detection approach. In scenarios involving sudden or abrupt damage, the algorithm swiftly and accurately labels abnormal points. For gradual damage scenarios, such as settlement, the algorithm consistently identifies abnormal points, with the rate of abnormal point detection accelerating over time. This detection rate is contrasted with the rate of erroneous abnormal point labeling when processing an exclusively healthy data set through the anomaly detection algorithm. This comparison reveals a higher rate of abnormal point identification when actual damage is present, affirming the effectiveness of the unsupervised SHM methodology in pinpointing abnormal behavior within the modeled bridge structure.

The Overhead Catenary Line System (OCLS) is crucial for continuous electrical supply to trains in electrified railway networks. This article outlines the research conducted on the exploration of the sustainable design space of the Overhead Contact Line support structure on the main Dutch rail network. The research began with a comprehensive review of the literature on OCLS support structures, covering publications from 2013 to 2022. A multi-objective optimisation model is developed to explore sustainable design possibilities. Further result analyses elucidate the defining parameters and variables of the support structure design. The findings presented in this research serve as a basis for more in-depth research on the discussion surrounding existing regulations and specifications and sustainability within the rail branch.

The Overhead Catenary Line System (OCLS) is crucial for continuous electrical supply to trains in electrified railway networks. This article outlines the research conducted on the exploration of the sustainable design space of the Overhead Contact Line support structure on the main Dutch rail network. The research began with a comprehensive review of the literature on OCLS support structures, covering publications from 2013 to 2022. A multi-objective optimisation model is developed to explore sustainable design possibilities. Further result analyses elucidate the defining parameters and variables of the support structure design. The findings presented in this research serve as a basis for more in-depth research on the discussion surrounding existing regulations and specifications and sustainability within the rail branch.

Regular maintenance of civil engineering structures is essential for their safety. Current maintenance regimes involve periodic inspections at regular time intervals. In the time between inspections, there can be a critical development in the structural integrity of a structure, which can be expensive to repair or could even lead to structural failure. A more robust maintenance approach would involve continuous monitoring of the structure. This is the research area of Structural Health Monitoring (SHM). Vibration-based monitoring, which is a subset of SHM, aims to provide new cost-effective maintenance solutions that provide long-term life-safety benefits. Vibration-based monitoring uses vibration measurements from sensors to assess the "Health" of a structure. Damage in a structure will alter the structure's stiffness, mass and damping characteristics, which in turn will change the dynamic properties of the system. This change can then be discovered in the vibration data. The growth of the field is partly due to the significant advances in data-driven science and engineering in recent decades. One of the issues in vibration-based monitoring is the presence of operational and environmental variability in the vibration data. With this variability present, it is challenging to determine from vibration data the characteristics of the underlying dynamic system. The aim of this thesis is to use Robust Principal Component Analysis (rPCA) to reduce or eliminate the operational variability from the traffic to allow for environmental and damage detection. rPCA is a matrix factorisation method that decomposes a data matrix into a low-rank matrix L and a sparse matrix S. The reconstructed low-rank matrix L contains the main correlations in the data that are robust to outliers and corrupt data that are contained in the sparse matrix S. By applying rPCA to the frequency representation of the vibration data, it is hoped that the underlying coherent structure corresponding to the dynamic system can be recovered. The vibration data used for this thesis is from two measurement campaigns conducted on the Haringvlietbrug. The Haringvlietbrug is a steel box girder bridge in the Netherlands, and there are several fatigue cracks present in the bridge. This presented an opportunity for damage detection. The goal of the first measurement campaign is to conduct damage detection and discover if there is a difference between vibration data from a damaged area with fatigue cracks and a "healthy" reference area. In the second measurement campaign, the goal was to extract the underlying dynamics of the structure at different temperatures and see if it was possible to distinguish between the different structural states at different temperatures. After applying the rPCA on the vibration data, (regular) principal component analysis (PCA) is used to embed the data into the low-rank subspace of the PCs to distinguish between the different structural states. The rPCA was successful in extracting the coherent structures in the vibration data corresponding to the underlying dynamic properties of the system. In the subsequent PCA, vibration data with underlying different structural states had different scores in the first three PCs. In other words, it was possible to distinguish between the different structural states based on the first three PCs, which correspond to the main correlation within the data. For the first measurement campaign, this meant it was possible to distinguish between vibration data in the damaged area and the "healthy" reference area and detect "damage". However, there was a difference in the structural configuration between the two areas, so it was not possible to conclude that the differences in vibration data were due to damage caused by the fatigue cracks. In the second measurement campaign, the dynamic system properties at different temperatures were recovered. With the low-rank vibration data from the rPCA, it was possible to distinguish between vibration data with a 1°C difference in the first three PCs from the (regular) PCA. This was not possible without lowering the regularisation parameter in the rPCA. Another method, the Sparse Sensor Placement for Optimisation (SSPOC), was used to determine the locations in the frequency spectrum that contained the largest differences between structural states. For both the first and second measurement campaigns, these locations were at specific natural frequencies of the system.

Founded in 1910, Boskalis, a leader in offshore operations, contends with limitations in ABB's Ability Marine Advisory System 'OCTOPUS' in predicting maximum vessel motions for heavy-transport vessels (HTVs). Accurate prediction of these motions, especially roll and pitch, is vital for transporting large, heavy structures, as exceeding predefined limits can jeopardize both vessel and cargo integrity. OCTOPUS's challenges, stemming not only from its reliance on linear theory but also potentially from the quality of its environmental data, underline the need for exploring alternatives, such as Machine Learning (ML) approaches, adept at handling complex, nonlinear phenomena, to ensure operational safety and efficiency. This thesis presents the development and comparison of three new approaches to predict maximum roll and pitch motions. The approaches are compared and evaluated against OCTOPUS. Two validation strategies are used to test their performance under known and unknown loading conditions (LCs). Known LCs in this context refer to the evaluation of data that incorporate LCs that are included in the training dataset for ML-based approaches. On the other hand, unknown LCs refer to the evaluation of data that incorporate LCs that are not included in the training dataset for ML-based approaches. The approaches are trained and validated using sensor data, LC data, and environmental data from 24 different voyages for a specific HTV. They differ in their design and the type of environmental data they use. The superior performance of ML-based approaches over OCTOPUS in known LCs is mainly due to two factors. First, ML approaches inherently incorporate nonlinear phenomena, which is particularly effective in accurately predicting maximum roll motion. Second, they are better equipped to handle flaws in environmental data. Although these advantages contribute to a significantly lower mean absolute percentage error (MAPE) compared to OCTOPUS, ML-based approaches face challenges in unknown LCs and extreme motion response scenarios. However, it is noteworthy that ML approaches quickly adapt to unknown LCs when small portions of these LCs are included in the training dataset. ML shows potential in vessel motion prediction, and this thesis underscores the importance of diverse training data to enhance its reliability in unknown LCs and extreme motion response scenarios. For Boskalis, addressing these challenges with strategies such as adjusting the custom loss function, data augmentation, and implementing ensemble methods could improve the accuracy of these approaches. This progress is significant for Boskalis and the wider maritime industry, paving the way for adaptive and efficient prediction systems. Collaborative efforts between industry and academia, using rich data and expertise, are essential to drive these innovations.