In the Netherlands there are many bridges that crosses waterways, which are key nodes in the transportation system. The safety and integrity of these bridges is nowadays assessed by (visual) inspections at regular intervals. This approach cannot provide information about damage development in between inspections, leading to potential failure or high intervention costs. A maintenance strategy for bridges based on continuous vibration-based monitoring provides a possible cost-effective solution that may replace the periodic (visual) inspections. Vibration-based structural health monitoring of civil engineering structures is receiving increasing attention in recent years. This is due to new developments in related areas such as sensing technology, system identification, data mining and condition assessment. A particular damage scenario that frequently occurs in steel orthotropic deck structures is the development of fatigue cracks in the welds between the longitudinal U-shaped stiffeners and the transverse beams or the steel deck plate. This damage scenario was also observed in the steel deck structure of the Zwartewaterbrug, located in Hasselt in the Netherlands. Vibration tests were performed on the bridge to develop a damage detection algorithm for early detection of the fatigue cracks in the bridge deck. In order to simulate the effect of damage, small masses were attached to the bottom of the bridge deck. The measurements were performed over a relatively short time period to exclude – in as far as possible – the effects of environmental variability. However, the bridge was kept open to traffic during the testing and structural response was primarily triggered by traffic load. The vibration data (accelerations) obtained during these tests was used for validation of the proposed methodology in this thesis. The aim of the thesis is to detect the added masses from the vibration data. To achieve this, a data-based approach for damage detection was proposed, which, besides the data acquisition, consists of four main stages: data preprocessing, feature extraction, pattern recognition and decision making. The analysis is focused on the high-frequency acceleration response, because the associated higher-frequency local modes are more sensitive to the small changes induces by the added mass. The similarity filtering preprocessing procedure was applied to filter out the variability related to the operational loading, and the Frequency Domain Decomposition system identification technique was used to extract damage indicators. Subsequently, a support vector machine algorithm was applied to “learn” patterns in the extracted damage indicators, which enables the algorithm to assign a damage state to a given measurement vector. Alternatively, a novelty detection technique was used to distinguish data from the “healthy” structure from data corresponding to a “damage” state. It was not possible to reliable detect the added masses from the high-frequency acceleration data due to the large variations in the damage indicators of a mass class. These variations are attributed to the changing environmental conditions over the measurement period. The principal component analysis was used in an attempt to remove these variations from the damage indicators, but the results were still not satisfactory. In conclusion, it was not possible to properly test the proposed methodology for damage detection using the data of the Zwartewarterbrug, mainly because the data was not suitable for the machine learning algorithms employed. Recommendations are given related to the methodology for the similarity filtering, determination of the high-frequency range, and machine learning algorithm to be used. Additionally, recommendations are given for future measurement campaigns to obtain data that is more suitable for successful application of the methodologies employed in the present project.

A railway turnout, which is a combination of a switch and a crossing is an important component in the railway network to divert trains. Due to the change in contact between the wheels and the rails as the train negotiates a turnout, severe impact loading occurs in two-directions: vertically and laterally. These cause negative environmental impacts in terms of ground vibrations. Therefore, understanding the ground response due to a railway turnout qualitatively and quantitatively is essential for soil surface in the vicinity. This thesis focuses on qualitatively and quantitatively describing the ground response through a dual load model setup through semi-analytical approach. A two-dimensional load on an Euler Bernoulli beam which is placed on a three-dimensional half-space is considered to describe the ground response. Followed by which, a load spectrum defining the interaction between the wheels and the rails is considered and the ground response is analysed. Measurements from the vicinity of a turnout is also analysed in order to compare the performance of the model to that of the real-life scenario. It is further concluded that qualitatively the ground response caused due to impact like loading that occurs at a crossing, and the presence of lateral loading is able to be explained by the model. Quantitatively, to analyse the response recommendations are to be followed to acquire the accurate load spectrum along with considering the complex track geometry for the lateral loading.

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.