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Drive-by bridge health monitoring offers a promising, low-cost approach for detecting subsurface damage using instrumented vehicles, reducing reliance on manual inspections and fixed structural sensors on individual structures. This study focuses on detecting scour damage, permanent settlement at bridge supports, using onboard measurements. A novel, self-calibrating approach based solely on onboard measurements, is developed to estimate vehicle properties through an optimization process. With identified vehicle properties, the inverse Newmark-Beta algorithm infers the profile and/or apparent profile, a combination of road surface profile and bridge deflection under the wheels. Scour damage is detected by observing changes in the inferred apparent profiles in healthy and damaged conditions. The approach is validated through simulations and field testing on a near-full-scale bridge at the European Commission's Joint Research Centre in Ispra, Italy. Simulations target the detection of 2 mm scour settlement, while the field testing successfully detects an average settlement of 4 mm over a number of runs. The results show a clear distinction in the apparent profile patterns between healthy and damaged states, with strong repeatability across multiple runs of the same vehicle. This demonstrates the method's reliability and potential for practical deployment in real-world environments.

Drive-by monitoring technologies offer a potentially transformative solution for enhancing transportation infrastructure monitoring and improving the enforcement of vehicle regulations across road and rail networks. Current monitoring of infrastructure is occasional, single point-in-time, and labour-intensive. Current overload enforcement methods are similarly limited by a labour-intensive, sporadic nature, which results in insufficient oversight of vehicle overloading and often poor compliance with legal limits. This paper proposes the use of accelerometers mounted on vehicles to (i) self-weigh the vehicles and (ii) detect the surface profiles over which they pass. Vehicle self-weighing, where the weights are shared with enforcement agencies, has great potential to improve compliance with legal weight limits. A bonus is that the surface profile provides valuable information on road/rail roughness and has potential for bridge monitoring. The on-board accelerometer data can provide continuous, tamper-proof weight monitoring by processing the acceleration signals to extract surface profiles and vehicle loads. For road vehicles, where vehicle weight enforcement is a challenge, this has the potential to monitor compliance with legal gross weight limits without disrupting traffic flow. In rail transport, drive-by monitoring of the track and bridge stock has the potential to accommodate heavier train loads, hence reducing operational costs and the carbon footprint of freight. By equipping trains with sensors, the system continuously assesses track stiffness and condition as trains pass, minimizing the need for dedicated monitoring vehicles. The rail monitoring model incorporates stochastic variations in factors such as train weight, speed, and suspension. Through a form of Inverse Dynamics, it determines track stiffness beneath each sleeper, supporting dynamic stability assessments to minimise risks such as track deterioration and train overturning.

Hall structures are among the most important engineering structures serving various purposes. They are mostly production halls, but they are also used in large numbers as warehouses. Many of these halls were built approximately 50-60 years ago and were made of concrete. They were prefabricated elements to speed up construction. Vertical columns were fixed into the foundation footings and transferred the load from the roof structure, which consisted mainly of prestressed truss beams of the SPP type. It was typical structure that was commonly used throughout the former Czechoslovakia at that time. Several hundred such halls were built. The SPP beams were connected in several parts due to the span and were additionally prestressed. However, as it turns out today, the prestressing technology was not perfect at that time, and cable ducts were often not injected, which causes major problems today. Several halls were even damaged and collapsed in the Czech Republic and Slovakia. The Department of Structures and Bridges, University of Zilina, was asked to diagnose one such hall. Another problem was that there was a fire in the hall, which significantly damaged it. The paper presents the results of the diagnosis and calculation of the hall damaged by the fire.

Due to the deterioration and aging of bridge structures over the past decades, structural health monitoring (SHM) systems have garnered significant attention from researchers worldwide. SHM systems encompass multiple modules, including sensing, data collection, transmission, management, damage detection, and safety assessment. As a highly interdisciplinary field, SHM integrates various technologies such as sensor sensing, data acquisition, signal processing, and optimization. One of the promising approaches in bridge health monitoring (BHM) is vibration-based monitoring, which provides critical information for bridge condition assessment and maintenance. In recent years, advancements in computer hardware and Artificial Intelligence (AI) algorithms have significantly enhanced the capability of vibration-based BHM systems. AI, with its advanced analytical power and high sensitivity to anomalies, has been widely adopted in these applications, enabling more efficient and accurate damage detection. This paper presents a state-of-the-art review of vibration-based BHM using various AI techniques over the past two years. It explores how AI can facilitate data-driven BHM systems for bridges and discusses key aspects of the BHM process, including existing methodologies and current challenges. Additionally, the paper highlights potential research directions to guide future studies, offering insights and opportunities for researchers in the field.

The infrastructure of ASFiNAG, predominantly composed of prestressed and reinforced concrete bridges, is subject to natural aging and material-specific degradation processes despite regular maintenance efforts. The primary degradation mechanisms affecting these structures include chloride ingress and carbonation, both of which lead to corrosion of prestressing and reinforcing steel, posing long-term challenges to durability. An advanced monitoring system has been implemented and analysis were performed on multiple structures, including retaining walls and bridge abutments, to track sensitive parameters such as chloride contend, carbonation depths, temperature, humidity, electrical corrosion potential, and surface resistivity. These measurements, alongside environmental data such as temperature, precipitation, and de-icing salt activity, are systematically collected and analyzed to establish correlations. This paper contributes to an improved understanding of the measurement data and provides insights into the assessment of condition and corrosion in reinforced concrete structures.

Climate change and the decline in biodiversity continue unabated. For this reason, these pressing challenges must be increasingly considered in the planning and construction of transport routes. Trees and shrubs can be used in a targeted manner to enhance the climate resilience of infrastructure. Additionally, they sequester greenhouse gases and create new habitats for biodiversity. A diverse range of natural and hybrid measures, including Nature-based Solutions, integrates ecological and infrastructural functions. The characteristics of woody plants vary widely depending on abiotic factors such as soil type, moisture, and climate. This variability complicates the concrete assessment of ecological benefits. We present a first approach to systematically evaluate measures on traffic infrastructure for their contributions to carbon storage and biodiversity. Using collected literature data and existing databases, we provide specific insights into the ecological value of woody plants in transport infrastructure. This approach enables the evaluation and comparison of measures that help to improve the ecological footprint of transport networks.

KEYSTONE and SETO are two EU-funded Horizon Europe projects running from June 2023 to May 2026.

KEYSTONE aims to create an ecosystem for seamless, interoperable, and intermodal transport and logistics by integrating and standardising data sharing across systems. Its five main goals include developing tailored digital solutions, validating a web app through two pilots, creating a replicable European transport ecosystem, defining API standards for secure and efficient data exchange, and enhancing mobility safety while reducing costs and emissions. The project seeks to drive environmental, societal, economic, and industrial benefits, fostering innovation, reducing emissions, enhancing transparency, and creating business opportunities to improve quality of life and competitiveness.

SETO seeks to provide an innovative digital solution that allows authorities and operators to access all relevant information required for enforcing transport and safety legislation in real-time and with a one-click-away solution. The platform will be validated in two multimodal Living Labs, with special focus on the enforcement of overloading of road vehicles. In that context, SETO is developing innovative technologies for direct enforcement using WIM systems and the detection of lifted axles. SETO not only covers the technological aspects of WIM systems, but it is also committed to providing metrological recommendations and guidelines.

In bridge Structural Health Monitoring (SHM), identifying anomalies is challenging due to environmental and operational variability (EOV), such as temperature changes, traffic loads, and else. This study develops a predictive model to isolate normal structural responses, enabling the detection of damage-induced anomalies. Using displacement and temperature sensors, the model evaluates longitudinal displacements at the bridge bearings. Temperature is the primary independent variable, combined with time, to capture daily and seasonal cycles characterised by nonlinear behaviour. Regression-based Machine Learning algorithms, such as Gaussian Process Regression (GPR), are employed to predict the expected displacements. A Physics-Enhanced Machine Learning (PEML) approach, or grey-box model, integrating physical knowledge with data-driven insights is adopted, improving accuracy and interpretability. Tested on real-world data from a highway viaduct, the grey-box model demonstrates superior performance and robustness, even with limited datasets. This confirms the potential of PEML-based approaches for damage assessment with data from static monitoring, paving the way for more reliable SHM systems and enhanced bridge safety.

Bridges are vital components of transportation infrastructure, yet they face increasing risks from climate change, including extreme weather events, flooding, and soil erosion. Nature-based solutions (NbS) offer a sustainable and adaptive approach to enhancing bridge resilience by integrating natural processes and ecosystems into infrastructure design and management. This study investigates the potential of NbS in mitigating climate change impacts on bridges, focusing on strategies such as vegetative reinforcements, riparian buffer zones, wetland restoration, and bioengineered embankments. These methods are assessed for their effectiveness in reducing flood risks, stabilising soils, and improving water flow management around bridge structures. The study also explores hybrid approaches that combine NbS with conventional engineering techniques to optimise resilience and functionality. By analysing previous case studies and evaluating performance metrics, this research highlights the environmental, economic, and operational benefits of NbS. Climate adaptation strategies will be proposed to sustainable infrastructure systems capable of withstanding future challenges.

As infrastructure ages and the number of structures requiring inspection increases, effective monitoring of damage in built structures has become more crucial than ever. Staff shortages and budget constraints can make it difficult for authorities to conduct the necessary frequent inspections. To address these challenges, companies and research institutions are increasingly exploring digital approaches to building inspection. Digitalized inspection processes involve creating a digital shadow of the structure, which combines a BIM model with a record of classified, measured, localized and assessed defects. Key to this approach is the deployment of transformer-based architectures for image-based automated damage recognition. Accurate defect recognition is essential for evaluating the condition of specific areas and assessing the overall integrity of a structure. This paper presents a dataset extension for the dacl10k dataset tailored to steel defect recognition on bridges. We manually assigned polygonal annotations to 3,737 images of dacl10k that showed steel bridges or building parts. Despite the challenging nature of this segmentation dataset extension, our baseline model achieves a mean Intersection-over-Union of 17.37%. This result provides a valuable reference point for future models and highlights the complexity inherent in detecting fine-grained steel defects. We conduct a detailed analysis to uncover the factors limiting model performance and suggest pathways for improvement.