Modal properties are widely used for structural model updating and damage detection, yet their sensitivity to environmental conditions, particularly temperature can complicate interpretation. This paper investigates how seasonal temperature variations affect structural identification based on modal properties, using a falsification-based approach. A railway bridge in service is used as a case study, with data from both forced vibration testing and long-term monitoring. Freezing temperatures are found to increase overall stiffness by up to 90%, including a significant rise in boundary stiffness, an increase in ballast modulus of up to 10 GPa, and the activation of rail continuity stiffness. The set of plausible physical models shifts between winter and summer, demonstrate that structural behaviour varies seasonally and that identification results are not directly transferable across temperature ranges. Compared to traditional residual minimisation, model falsification provides a more comprehensive and robust set of plausible models and avoids unreliable parameter estimates. These findings underscore the need for temperature-sensitive identification strategies and adaptive models to support accurate interpretation, damage detection, and seasonally informed maintenance decisions in structural health monitoring.

Effective anomaly detection and classification in bridge monitoring data critically depend on robust feature extraction to ensure reliable performance across diverse sensors, loading scenarios, and anomaly types. This study presents a novel deep metric learning-based framework that automates feature extraction, addressing the limitations of traditional manual methods that often lack generalization. The proposed approach employs continuous wavelet transforms to convert time-series signals into time-frequency representations, followed by a convolutional neural network trained with metric learning to produce low-dimensional feature optimized for anomaly classification. These embeddings capture similarities between anomalies, enabling the model to distinguish normal loading events from anomalous ones while classifying specific anomaly types. The method was validated on bridge monitoring data collected from strain gauges and accelerometers under varying loading scenarios, achieving high detection and classification accuracy. This robust and scalable framework highlights the potential of deep metric learning to advance automated bridge monitoring.

Increasing the operating speed of trains in modern railway networks can induce greater actions on the infrastructure than was previously the case. This is due, in particular, to the occurrence of the resonance phenomenon in railway bridges, which is the focus of this paper and was not traditionally considered as a concern. In this context, the vibrations experienced by bridges, both vertical accelerations and displacements, are limited by design regulations to ensure that the safety of train passages over bridges and the comfort of passengers are guaranteed. However, previous studies have shown that the conventional dynamic design methods do not always result in conservative designs, nor is the achieved safety always consistent. Therefore, a probabilistic approach is adopted in this study to optimize the cross-section properties of various railway bridges in a wide design range including section types, span lengths, and number of spans. For this purpose, an iterative line search-based optimization problem is formulated to minimize the thickness of the cross-sections under consideration and consequently the linear mass of the bridges. Meanwhile, the associated failure probabilities of the above dynamic limit states are constrained to be less than the desired level of safety by incorporating them into the optimization constraint. In this regard, First-Order Reliability Method (FORM) is adopted to perform reliability analyses. Thus, the obtained results are presented in the form of design curves that may assist designers to select minimum cross-section dimensions satisfying the desired level of safety in terms of dynamic limit states. This objective can be achieved using the proposed design curves without the need to construct associated complex computational models and perform computationally expensive dynamic analyses.

The increasing speeds of modern trains lead to excessive vibrations on the bridges, which have the potential to destabilize the ballast particles. The occurrence of this phenomenon not only increases the track maintenance cost, but can also disrupt the load path from the rail level to the bridge deck, posing a risk to the train running safety. The design regulations indirectly control this limit-state by restricting the vertical acceleration of the bridge deck. The assessments pertaining to this purpose often neglect the soil-structure interaction (SSI) effects considering that as a conservative assumption. Such effects can positively contribute by increasing the system damping, but they can also increase the bridge flexibility making it more susceptible to vibrations due to reduction on critical speed. Therefore, this study investigates the influence of considering/disregarding SSI effects on the ballast destabilization phenomenon using a probabilistic methodology. The results are classified based on the maximum permissible train speeds and the bridge span length. Due to the high computational costs of the reliability analyses, the associated limit-state is approximated by an ensemble of classification-based surrogate models using the stack-generalization concept. Subsequently, the upper/lower bounds of the failure probability in the presence of SSI effects are compared with those obtained for simply-supported bridges. It is pointed out that neglecting SSI effects for shorter span bridges may lead to an underestimation of system safety. For longer span bridges, however, this may lead to an overestimation of safety, which means that a non-conservative system can be designed.

Accurate differentiation between damage-related anomalies and data errors is a critical challenge in bridge monitoring. This paper presents a data-driven framework for anomaly detection and classification, addressing the question: How can anomalies be classified in multi-sensor bridge monitoring to distinguish structural changes from noise? The framework combines an adapted anomaly taxonomy with a deep neural network trained on synthetic data. It is validated using long-term monitoring data from a railway bridge, incorporating strain gauges, accelerometers, and an inclinometer. In offline training, the model achieves high precision, recall, and F1-scores, effectively detecting anomaly classes across sensor types. For online prediction, it provides anomaly type percentages and visualizations over daily, weekly, and annual timeframes, distinguishing frequent noise-related anomalies from rare anomalies signaling structural changes. Requiring one month of training data, the framework delivers a scalable solution for bridge monitoring and lays the groundwork for future self-learning anomaly detection in infrastructure management.

As far as the authors are aware, the threshold for vertical acceleration of the bridge deck was chosen based on the assumption that the induced dynamic loads would overcome gravity at higher accelerations, resulting in loss of contact between wheels and rail; however, the previous studies do not support this hypothesis. Considering these inconsistencies, a better understanding of the simplified design criteria is essential before conducting further studies suchas the calibration of partial safety factors. Therefore, this study considers a set of representative design scenarios to statistically compare wheel-rail contact loss with other criteria that can bederived from moving load models, such as vertical accelerations and bridge deck deflections. Based on the analyzes performed, deflection seems to be a better criterion than acceleration to control the running safety limit-state; although the results presented do not necessarily show avery strong correlation between these two criteria. Therefore, the k-means clustering approach isused together with 5% lower quantiles of the collected data to propose potential new thresholds. It should be noted that due to the limited number of analyzes, the approach presented in this study can be considered as a possible framework for further updates of the current design method rather than drawing general conclusions.

The bridge network is progressively aging, with an alarming proportion of bridges over 100 years. This situation engenders substantial risks to the overall reliability of transportation networks, requiring innovative methods for efficient management. Monitoring can provide a direct source of information about structural behavior generating alerts when changes occur. Real-time alerts enable effective infrastructure management and decision-making during damage or anomalous situations. However, monitoring can result in a large amount of data that is often difficult to convert into valuable information in real time. This paper presents an approach for real-time detection of abrupt damage occurrence in bridges using unsupervised anomaly detection algorithms and strain/acceleration measurements. The approach incorporates the separation of measurements into events having the same loading nature and the construction of three feature matrices based on statistical features, time-frequency features, and wavelet spectrum features. It includes the evaluation of five anomaly detection algorithms including Isolation Forest, One-Class Support Vector Machine, Robust Random Cut Forest, Local Outlier Factor, and Mahalanobis Distance. The approach is illustrated with a case study of a steel-bascule-railway bridge, that has experienced a brittle cracking event during monitoring. Results highlight the robustness of One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor algorithms in promptly detecting abrupt changes across different features. The separation of strain and acceleration data into loading-based events, coupled with the comparison of previous and new event features, provides robust feature matrices for effective damage detection. Enhanced detection and higher scores are particularly attributed to time-frequency domain features during damage occurrence. The presented approach can be used as a base on how to perform real-time anomaly detection within the context of bridge monitoring.

The trend in recent years to build longer and slender bridges has created new challenges in terms of safety and structural integrity. This study focuses on the effects of windinduced vibrations on the hangers of the Bomarsund Bridge in Åland, Finland. Those close to the mid-span have exhibited significant vibrations, requiring a thorough investigation to understand the response of the bridge to wind action. Computational Fluid Dynamics (CFD) simulations were performed using ANSYS Fluent to calculate the aerodynamic coefficients characterizing the given section (i.e. Strouhal number, drag, shedding frequencies, etc.) and to investigate the vortex-induced vibration (VIV) phenomena at different wind speeds. The results showed inconsistent drag coefficients at varying wind speeds and lower drag for geometries with rounded edges. The study highlighted the significant dependence of the Strouhal number on wind speed, challenging traditional geometry-based estimations. These findings can be used to implement effective measures to mitigate wind-induced vibrations.

The assessment of running safety of railway bridges is conditioned by the Eurocode EN 1990 A2 by limiting vertical deck acceleration. On ballastless track bridges, this value is 5 m/s2. The background for this value is not clear, and it is believed that it originates in the application of an arbitrary safety factor of 2 on accelerations around 1 g to avoid loss of wheel–rail contact. However, studies show that the level of acceleration may not be directly related to the occurrence of derailment. In this work, this idea is expanded by assessing both vertical and lateral dynamics, comparing acceleration values with the Unloading and Nadal derailment criteria. The parametric study is comprised of a set of five representative single-track slab bridges with spans between 10 m and 30 m with two levels of track irregularities, corresponding to a well-maintained track and an Alert limit situation. A three-dimensional articulated FE model based on the load properties of the EN 1991-2 High-Speed Load Model A is presented, crossing the bridges at running speeds from 150 km/h to 400 km/h. Despite the complexity of the models, a large amount (1461) of full 3D train–track–bridge interaction dynamic analyses are performed, to produce a data set representative of the phenomenon. Results show a weak correlation between the criteria and deck acceleration (maximum r2 of 0.47 for Unloading and 0.15 for Nadal). Additionally, track quality is shown to be a more conditioning factor for derailment when compared to resonance. This work contributes to discussing the thesis of using deck acceleration as an indicator of running safety, considering lateral dynamics.

Digital inspections methods have gained increasing interest in the civil engineering field for assessing the condition of various structures including buildings and bridges. These methods are used primarily to evaluate the external surface condition of the structure. However, in the case of bridges, their application when inspecting the interior of these structures remains uncommon. This paper presents a case study for inspecting the interior of a concrete box girder bridge, the Strängnäs bridge. The inspection involved gathering data using a commercial drone, creating a photogrammetrical model, detecting and quantifying damages by employing a Convolutional Neural Network (CNN). The analysis successfully detected 60 cm long concrete cracks, a total area of 3.5 m2 leakage and corrosion over 40 cm. The study addressed the difficulties such as insufficient lighting, lack of GPS signal and dust clouds reducing visibility. Despite these obstacles, the study demonstrated the effectiveness of indoor drone-based inspections.