Railway Bridge Monitoring with Minimal Sensor Deployment: Virtual Sensing and Resonance Curve-Based Drive-by Monitoring

The global railway bridge infrastructure is aging and exposed to increasing traffic loads. In Germany, the average lifespan of a railway bridge is 122 years. During this time, railway vehicles have evolved significantly, leading to higher axle loads and speeds. The urgent need to extend the lifespan of these bridges has prompted an intensive search for efficient methods to assess existing bridge structures. One approach is vibration-based monitoring, which can be categorised into direct and indirect monitoring. In direct monitoring, sensors are installed directly upon the structure. Conversely, in indirect monitoring, a passing vehicle equipped with sensors is utilised, commonly referred to as drive-by monitoring. Direct monitoring of bridges is still rare, and when bridges are instrumented, it is typically with a sparse deployment of sensors. Hence, methods are needed to extrapolate the measured structural responses to unmeasured locations. These methods are summarized under the term virtual sensing. Existing research on virtual sensing for railway bridges reveals gaps in long-term studies, especially considering different environmental and operational conditions. Notably, 95 % of all railway bridges in Germany are short-spanned with spans less than 30 m, and there is a significant lack of research in this segment. A main focus of this dissertation was to address this research gap. The high effort in direct instrumentation currently does not allow for monitoring of the entire railway bridge network using available technology. A cost-effective approach to monitoring railway bridges is drive-by monitoring. Despite the potential of this approach, field tests and comprehensive experiments in the context of railway bridges are rare. There is a need for a robust methodology for frequency identification of railway bridges using drive-by monitoring, especially at regular operating speeds. The development and validation of a suitable methodology form the second focus of this work. In this dissertation, three experimental investigations were conducted to explore both the direct and indirect monitoring methodologies. The HUMVIB bridge, the railway bridge over the Schmutter river with an instrumented ICE 4, and a railway bridge in Düsseldorf (Germany) with an instrumented ICE TD were thoroughly analysed. During the investigations, modal expansion, a method that uses the structure's eigenmodes to reconstruct unmeasured structural responses, was confirmed as a suitable methodology for virtual sensing. The investigations under different operational and environmental conditions showed that the influence of operational conditions, such as train type and speed, is dominant over environmental conditions. The experiments also validated the developed methodology for frequency identification using drive-by monitoring for two different trains and bridges. This work aims to make contributions to the monitoring of railway bridges by providing practical, cost-effective, and reliable methods

Raw Data Is All You Need: Virtual Axle Detector with Enhanced Receptive Field

Rising maintenance costs of ageing infrastructure necessitate innovative monitoring techniques. This paper presents a new approach for axle detection, enabling real-time application of Bridge Weigh-In-Motion (BWIM) systems without dedicated axle detectors. The proposed method adapts the Virtual Axle Detector (VAD) model to handle raw acceleration data, which allows the receptive field to be increased. The proposed Virtual Axle Detector with Enhanced Receptive field (VADER) improves the $F_1$ score by 73\% and spatial accuracy by 39\%, while cutting computational and memory costs by 99\% compared to the state-of-the-art VAD. VADER reaches a $F_1$ score of 99.4\% and a spatial error of 4.13~cm when using a representative training set and functional sensors. We also introduce a novel receptive field (RF) rule for an object-size driven design of Convolutional Neural Network (CNN) architectures. Based on this rule, our results suggest that models using raw data could achieve better performance than those using spectrograms, offering a compelling reason to consider raw data as input

Inverse Identification of Cable Forces using its Modal Behavior by Direct and Non-Contact Vibration Measurements

Cables are essential in civil engineering for constructing slender, lightweight structures with large spans. To ensure serviceability and load-bearing capacity, a monitoring of the cable forces is necessary. Conventional, static methods are not suitable for systems with highly pre-stressed cables or large cable diameters, so dynamic measurements using the cable's vibration behavior offer an alternative. This study presents laboratory test results on inverse identification of cable forces using eigenmodes and the corresponding frequencies, comparing contact and non-contact dynamic measurement methods. Two methods for determining the cable force will be investigated within this study: (1) the linear theory of vibrating strings neglects internal sag and bending stiffness, and (2) an inverse identification of the cable force for a cable tensioned on both sides, accounting for bending stiffness. Contact based measurement with accelerometers can identify many eigenmodes and frequencies unambiguously and is suitable for simple systems like single span systems. In the conducted investigations, the non-contact measurement with microwave interferometers could only identify up to 4 natural frequencies. The study also examines the influence of the free vibration length, which, in addition to the bending stiffness of the cable, the fork fitting and utilization, has a significant influence on the determined cable forces. The implications for using different fork fittings and cable cross-sections are discussed. This study offers valuable insights into the challenges and limitations of cable force identification and highlights the importance of choosing the appropriate measurement method based on the design of the cable structure

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions

Long‐term validation of virtual sensing of a railway bridge with ballasted superstructure

Railway bridges have a long lifespan, which is challenged by the constant development of vehicles leading to increased loads that they were not originally designed for. To ensure the longest possible use of existing structures, a sensor‐based structural health monitoring system can make a significant contribution. However, due to economic reasons and the inaccessibility of many points of interest, sensors cannot be installed everywhere. Therefore, in most cases, only a few sensors are available at a few points of interest, and methods that aim to reconstruct structural responses at unmeasured points from these measurements are referred to as virtual sensing. In this paper, we have analyzed 19,075 passages recorded on a steel trough bridge with a ballast superstructure and a span of 16.4 m, together with weather data. Our findings show that the influence of train type and speed has a significantly higher impact on the results than environmental factors. The investigation revealed that the model‐based analysis produced similar results to the data‐driven analysis concerning acceleration signals. However, when analyzing strain signals, the two approaches yielded distinctly different results

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Inverse Identification of Cable Forces using its Modal Behavior by Direct and Non-Contact Vibration Measurements

Cables are essential in civil engineering for constructing slender, lightweight structures with large spans. To ensure serviceability and load-bearing capacity, a monitoring of the cable forces is necessary. Conventional, static methods are not suitable for systems with highly pre-stressed cables or large cable diameters, so dynamic measurements using the cable's vibration behavior offer an alternative. This study presents laboratory test results on inverse identification of cable forces using eigenmodes and the corresponding frequencies, comparing contact and non-contact dynamic measurement methods. Two methods for determining the cable force will be investigated within this study: (1) the linear theory of vibrating strings neglects internal sag and bending stiffness, and (2) an inverse identification of the cable force for a cable tensioned on both sides, accounting for bending stiffness. Contact based measurement with accelerometers can identify many eigenmodes and frequencies unambiguously and is suitable for simple systems like single span systems. In the conducted investigations, the non-contact measurement with microwave interferometers could only identify up to 4 natural frequencies. The study also examines the influence of the free vibration length, which, in addition to the bending stiffness of the cable, the fork fitting and utilization, has a significant influence on the determined cable forces. The implications for using different fork fittings and cable cross-sections are discussed. This study offers valuable insights into the challenges and limitations of cable force identification and highlights the importance of choosing the appropriate measurement method based on the design of the cable structure