ce/papers最新文献

This paper presents experiences learned from CO² assessment of bridge repair procedures, why it is done and how it has been assessed in Finnish bridge projects. The EU is aiming for carbon neutrality in 2050 and to cut its emissions 55 % by 2030. Construction industry is carbon heavy, and therefore it is crucial that all fields inspect the possibilities of cutting down CO² emissions. Finnish Transport Infrastructure Agency has published guidelines [1] that set boundaries to the assessment, so that comparison is possible. Certain clients in Finland are searching for major projects to act as an example and to tackle biggest impacts. A reference project was used for assessing the emissions. Smaller and more simple bridge repairs within the same project were assessed as a reference. By assessing emissions for each construction procedure, the most significant emission sources and therefore possibilities to cut emissions can be found. Biggest challenges faced were uncommon construction or repair phases that were hard to assess and nonexisting emission factors and/or Environmental Product Declarations for certain phases and products. The more accurate the assessment, the bigger the emissions tend to be.

Bridges are essential for connectivity, often enduring extreme load conditions that impact their dynamic behaviour. Understanding how modal parameters such as natural frequencies, damping ratios, and mode shapes change under heavy loads is crucial for Structural Health Monitoring (SHM). This paper presents a case study of the San Carlo Bridge, a newly built continuous-isolated steel-concrete composite bridge designed for hydraulic and seismic resilience in a flood-prone region of central Italy. To evaluate its dynamic behaviour, extensive vibration-based testing was conducted under both unloaded and controlled heavy-load conditions. Heavy loads were applied by strategically positioning fully loaded trucks in various configurations to produce extreme effects on the structure in terms of bending moment. Ambient vibration tests and operational modal analysis were performed to compare modal parameters across these scenarios. The findings reveal variations in modal parameters across different loading schemes, defining a range of dynamic responses that characterise the structure in safe but high-load states. These results provide experimental benchmarks for SHM, enabling the establishment of critical thresholds and informing predictive models for extreme events.

Effective bridge management requires structured methods that integrate information gained through visual inspections, non-destructive tests or monitoring systems to prioritize maintenance activities on bridges at a territorial level. The Italian and Swiss authorities offer different perspectives on maintenance prioritization, with Italy emphasizing multi-level classification and flexible data integration for risk assessment, and Switzerland relying on condition assessment and a centralized BMS including damage predictive capabilities to determine maintenance policy, while minimizing the long-term costs. Both approaches highlight the need for interoperable digital solutions to collect, analyse and process large amount of heterogeneous data. In addition, recent federal regulations enforce the “open by default” principle, promoting transparency, participation, and innovation using open-source software to fulfil Public Authority tasks. Moving from these considerations, the study proposes a modular framework for bridge management that includes inventory and data management, inspection workflows, risk analysis, predictive monitoring, GIS/BIM integration, intervention planning, and regulatory compliance. By leveraging open source and widely used tools, the framework improves data accessibility, interoperability, and decision-making efficiency. A comparative analysis with existing proprietary solutions highlights large possibilities of improvement in terms of adaptability and integration, underscoring the need for a flexible, standardized digital approach to bridge infrastructure management.

Structural Health Monitoring (SHM) is crucial for ensuring bridge safety, yet many methods rely on baseline data or known damage states—often unavailable for aging structures. To address this, we propose a new approach that combines Artificial Neural Networks (ANNs) with matrix profiling (MP) to create a “pseudo-baseline” for predicting bridge behavior. Physics-Informed Neural Networks (PINNs) incorporate physical laws into the model, while MP detects patterns and subtle anomalies in structural data. This method links structural responses, like strain and displacement, to environmental factors such as temperature and humidity. By analyzing these relationships, we can model normal bridge behavior without needing complete historical data. The approach is validated using performance metrics such as R², Root Mean Square Error (RMSE), and residual analysis. Our combined method offers an innovative solution for real-time anomaly detection, providing a more accurate and proactive tool for long-term bridge monitoring.

Concrete bridges in cold regions face critical durability and safety challenges. Factors such as corrosion from de-icing salts, freeze-thaw cycles, and hydraulic pressures from ice loads put these structures at higher risk for deterioration and failure. Understanding the primary failure mechanisms—corrosion, freeze-thaw damage, and hydraulic impacts on substructures—is essential for maintaining safe and functional infrastructure in these climates. This study addresses these mechanisms, examining also how climate change may further exacerbate their effects. Gathering information from existing investigations and studies from different countries, this paper examines common vulnerabilities and failure modes in concrete bridges in cold regions, presenting potential solutions and preventive strategies to enhance their resilience. The paper also discusses potential preventive measures to reduce the impact of cold-related degradation and climate change in concrete bridges.

Overloaded transportation has been on the rise worldwide in recent years. However, overloaded vehicles negatively impact the lifespan of road pavements and bridges, and in some cases, can cause catastrophic accidents, such as bridge collapses. To mitigate these risks, accurately detecting, identifying, and classifying vehicles is crucial. This paper presents a conceptual framework that combines slim inductive loops with a camera-based artificial intelligence (AI) solution for detecting lifted truck axles. By synthesizing existing literature, we identify current limitations and challenges in existing approaches and propose a novel method. This framework aims to enhance the ability of WIM stations to classify trucks with lifted axles more accurately, laying the foundation for future experimental and field validations. Moreover, the impact of lifted axles on pavement deterioration is evaluated using the Equivalent Standard Axle Load (ESAL) of standard C3 and T2S3 truck configurations. The results show that lifting a single axle increases pavement damage by a factor of about 1.3 for the C3 and 1.5 for the T2S3. Additionally, when the T2S3 is both overloaded and has one lifted axle, its pavement impact increases by a factor of about 2.

Slender bridges might be prone to excessive vibrations under traffic and wind loadings. Thus, the comfort of bridge users with respect to vibrations might represent an important serviceability concern for this type of structures. Excessive vibrations, in fact, can cause the perception of an unsafe bridge, and have an economic as well as social impact because of the users' complaints. In order to understand the main factors affecting the vibration response of a slender bridge, the San Marco Viaduct in Castellammare di Stabia, Southern Italy, the present paper describes the main outcomes of the extensive experimental campaign carried out on the bridge. Static as well as dynamic tests have been carried out on the bridge for structural identification purposes. The setting of a preliminary while sufficiently accurate numerical model based on the experimental outcomes is also discussed, and the main lessons learned from the present application are discussed.

Prestressed concrete bridges are common on the road networks, especially in Europe, where a significant proportion of them were built from the 1960‘ to 1980‘. Thus, they reach the usual lifetime of prestressed concrete structures and deserve specific diagnosis and maintenance [1]. Structural Health Monitoring (SHM) with measurements gathered continuously over long periods is an emerging and relevant tool to get a consistent knowledge of the effects of ageing and loads on civil structures [2]. In this contribution, a recent case study is presented on a large prestressed concrete highway viaduct located in the French Alps. The 21 spans of the box-girder deck have been monitored with 44 long-basis optical strand strain sensors, from January 2018 to June 2024, gathering high volumes of data, since each passage of a heavy vehicle has been recorded on all sensors with a sampling rate of 100 Hz. We focus on the data analysis process to get useful knowledge on the mechanical behavior of the bridge from the raw strain data, including statistical indicators and synthetic indices, spectral vibration analysis, and the reconstitution of a full image of the movements of the bridge for specific load events.

Detecting the use of lifted axles on heavy goods vehicles (HGV) is a major challenge to enforce weight regulation and to assess road infrastructure damages under moving loads. If some axles are lifted on HGVs, the gross vehicle mass is concentrated on less axles, which increases significantly the aggressiveness of these HGVs.

The paper will first present a new methodology to detect lifted axles by algorithms using only WIM (Weigh In Motion) data. We successfully apply two supervised classification methods, namely logistic regression and Random Forest. In particular, the random forest method enables more than 90% of the axles lifted to be correctly identified. Identifying the lifted axles of HGVs will open new applications.

Among the applications, the deployment of direct enforcement by WIM systems will benefit of the lifted axle identification, while the maximum permitted weight of a HGV depends on its number of axles. If some axles are lifted the vehicle should not be fully loaded. Balancing evenly the gross vehicle mass on all axles may significantly reduce the stresses applied to pavement layers. This article will compare the aggressiveness of heavy goods vehicles of different weights on several types of road, with and without raised axles. Some bridge structures are also sensitive to axle loads, and may also benefit of a better monitoring and control of lifted axles.

A Structural Health Monitoring System was installed on the Queensferry Crossing cable-stayed bridge during its construction in 2017, providing both real-time and long-term data essential for understanding the bridge's behaviour under environmental and operational loads. This paper presents an analysis of four years of high-quality Global Navigation Satellite System and anemometer data to investigate the lateral response of the bridge deck to wind action. Original methodologies were developed to isolate the normal component of wind speed and the lateral displacement of the deck, effectively eliminating confounding variables such as traffic and rotational effects. Results demonstrate a consistent positive quadratic trend in the lateral response to wind loading. Comparative analysis with the adjacent Forth Road Bridge reveals that, for equivalent wind speeds, the Queensferry Crossing exhibits significantly lower lateral displacements, attributed to its aerodynamic deck design. These findings highlight the value of long-term SHM data in providing actionable insights for bridge safety, serviceability, and design, and underscore the importance of continued monitoring to address gaps in understanding the dynamic response of flexible, long-span bridges to non-stationary wind events.