The passage of trains by railway noise barriers induces vibrations that may affect their fatigue performance and reduce their service life. However, long-term field monitoring of noise barriers under complex environmental and operation conditions remains rare. This study develops an interpretable machine learning (ML) framework to investigate the aerodynamic pressure and dynamic behaviors of noise barriers based on a nine-month long-term field monitoring campaign, yielding 12810 train runs over 105 valid days. Input variables include train type, speed, temperature, wind speed and direction, relative humidity, and air pressure, while the target responses cover train-induced aerodynamic pressure, stress near the base of the steel post, and displacement at the post top. Eight ML models, including four traditional and four ensemble algorithms, were used and systematically compared to evaluate their predictive capabilities and robustness. Ensemble models, particularly Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost), achieved the best predictive performance, with R2 values exceeding 0.935 for stress and displacement, and 0.895 for pressure. XGBoost, offering a strong balance of predictive accuracy and computational efficiency, was selected for SHapley Additive exPlanations (SHAP)-based interpretability analysis to uncover the physical relationships behind the data-driven predictions. Results reveal that aerodynamic pressure was the most challenging response to predict, given its higher sensitivity to turbulent airflow and environmental fluctuations, whereas stress and displacement exhibited more stable and predictable patterns. SHAP analysis identified train speed and type as the most influential factors across all responses. While environmental factors had comparatively lower influence, temperature and instantaneous wind direction consistently showed higher importance among them. Relative humidity has a moderate effect on aerodynamic pressure but a minor impact on dynamic behavior. Air pressure and wind speed exhibit limited influence on all outputs. These findings highlight the novelty and effectiveness of integrating long-term monitoring data, ML methods, and SHAP-based interpretability, offering new insights into the dynamic behavior of railway noise barriers.

This paper presents a multi-level reliability framework for assessing the fatigue life of reinforced concrete (RC) railway trough bridges subjected to cyclic loading. The framework incorporates increasing levels of analytical complexity and real-world data in four steps. First, an analytical model applies S-N curves and the Palmgren-Miner rule with constant stress assumptions. Second, monitored strain data refine stress estimates. Third, a calibrated finite element (FE) model is used to simulate degradation and structural response. Fourth, survival information conditions the reliability on observed performance. The framework is applied to a RC trough bridge tested under representative railway loading using traffic data from Sweden's Iron Ore Line. Results demonstrate the value of combining monitoring, FE modeling, and probabilistic methods for evaluating remaining service life (RSL). From step 1 to step 3, the methodology extended the RSL estimates by 39 years, allowing an increase in mean axle load by approximately 20%.

Train passages generate aerodynamic loads that induces dynamic responses in railway noise barriers and may compromise structural integrity. However, field measurements capturing both aerodynamic pressure and structural response, especially for wooden barriers, remain scarce. This study presents a field investigation of train-induced aerodynamic excitation and response in a noise barrier. A monitoring campaign collected pressure and response signals from four train types operating at speeds of 150-200 km/h. The pressure signals exhibited characteristic positive and negative transitions at the train nose and tail, with tail-wave amplitudes approximately 35%-55% of the nose-wave. Steeper noses produced higher pressure amplitudes and shorter nose-wave peak intervals, while train length showed limited influence on nose-wave pressure. Shape coefficients identified for Swedish trains ranged from 0.87 to 1.28, indicating that Eurocode models underestimate pressure by 2.75%- 25.44%, however, its vertical distribution model agreed well with measurements. Spectral analysis revealed that nose-wave energy is concentrated in the 2-4 Hz range, with additional contributions in the 4-6 Hz. While pressure signals showed intermediate peaks associated with inter-car gaps, the response signals exhibited more complex fluctuations between nose and tail waves. Stress range and displacement exhibited higher sensitivity to train speed than pressure, increasing approximately with the cube of speed. Increasing speed from 155 to 200 km/h raised the load frequency by about 24%, shifting load energy to higher frequencies and enhancing dynamic amplification. Steeper noses induced larger responses due to higher load frequency contents, whereas train length had a negligible effect on dynamic response.

Fatigue significantly impacts engineering structures, especially reinforced concrete, which endures millions of cyclic loads over its service life. Understanding the progressive degradation of concrete’s mechanical properties under fatigue is critical. This paper reviews existing degradation models, encompassing stiffness degradation, strength degradation, and strain development models. Then, these models are compared in terms of accuracy and applicability, and potential research directions for concrete damage modeling under fatigue are proposed. This work aims to serve as a valuable resource for future investigations and engineering applications.

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.

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 R 2 , 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.

With increasing traffic volumes on Malmbanan (the Ore Line) between the ore fields in Northern Sweden and the harbors in Luleå and Narvik, there is a need to raise the axle load allowed on the track, This, in turn, necessitates the analysis and if needed, enhancement of the capacity of the railway bridges. To facilitate this, fatigue tests are being conducted on a full-scale model of a reinforced concrete trough bridge. Calculations of the load effects from dead weight and axle loads are performed using the Eurocodes and the fib Model Codes (2010 and 2020). The results indicate that the axle load on the Ore line can be increased to 35 tons without the risk of bending or shear fatigue failure in the trough bridges. However, the fatigue capacity varies considerably, both between different bridges and across different codes, so continued Structural Health Monitoring is recommended.

Noise barriers play a crucial role in mitigating railway noise, with the aerodynamic pressure exerted by passing trains being a key factor in their structural design, particularly for those installed along high-speed railways. While previous studies have focused on the effects of train speed, geometry, and distance from the track centre, and have developed models incorporating these factors, limited attention has been given to the impact of bilateral layouts and barrier height on this pressure. Quantitative assessments of these two factors remain scarce, and existing pressure calculation models inadequately address their influence. This study addressed these gaps by employing computational fluid dynamics (CFD) simulations, validated by field test data, to qualitatively and quantitatively analyze the effects of barrier layout and height on the aerodynamic pressure acting on vertical noise barriers. The results demonstrate that two distinct transient pressure fluctuations over time are generated by the train’s nose and tail, in agreement with the findings of the field tests. A bilateral layout increases peak pressure by up to 8.5%, particularly as the distance to the train centreline decreases. Moreover, increasing barrier height from 2 to 4 m resulted in a maximum pressure amplification of 13.23%, though the amplification rate diminished with further height increases. To address the limitations of existing pressure calculation models, an exponential model was developed to account for the amplification effect of bilateral layouts, while a logarithmic correction factor was introduced to account for barrier height. These models were integrated into a comprehensive aerodynamic pressure calculation framework, effectively capturing the combined impacts of barrier layout and height. Validated through simulations, the proposed model offers a more accurate and practical approach for predicting train-induced aerodynamic pressure on noise barriers, providing valuable insights to inform their structural design.

A carefully studied evaluation was necessary to replace an existing pre-stressed concrete box girder bridge that had been in service for over 60 years, in Kalix, Northern Sweden. The bridge was 283.6 m long divided into five spans, and it was constructed using the balanced cantilever method. The decision to replace the old bridge created the need to evaluate a demolition procedure for it, one carefully designed not only to avoid damaging the newly built bridge or creating stability-related issues, but also to prevent any debris from falling into the Kalix River, which is part of a Natura 2000 protected area. This article focuses on the comprehensive methodology, on the demolition design, and on the observations related to residual prestress levels in the bridge, indirectly obtained through a reversed-engineering FEM process and on the limitations of the demolition technology to be used in these specific cases. Several variables were monitored during the deconstruction process, to control the structural stability better during all the phases of the project. The main outcome of the full condition assessment is that it provides the information needed to make informed decisions for interventions on these types of structures.

Corrosion of steel reinforcement in concrete is a significant cause of structural failure, particularly in environments exposed to chloride ions and mechanical stress. The passivation film on steel reinforcement, composed of hematite or magnetite, plays a crucial role in protecting the steel from further corrosion. However, the intrusion of harmful ions or mechanical stress can compromise the film's integrity, transforming it into a loose structure and accelerating the corrosion process, leading to structural failure. This study investigates the mechanical behaviors at the interfaces between corrosion products (hematite and magnetite) and C-S-H using reactive molecular dynamics. C-S-H and interfacial models incorporating hematite and magnetite were developed, with stress-strain analysis refined by filtering raw data and using true strain rather than engineering strain to improve the precision of the stress-strain responses. The results indicate that the Magnetite-CSH interface is more prone to loosening under external forces compared to the Hematite-CSH interface, thereby reducing its corrosion resistance. Structural evolution analysis under uniaxial tension highlights the detrimental effects of passivation film degradation on interfacial mechanical properties. This study contributes to improving the precision of stress-strain responses in MD models and facilitates comparison of mechanical properties at the nanoscale with results from other scales. The findings provide valuable guidance for improving the durability and performance of construction materials in corrosive environments, helping to bridge the gap between molecular-level simulations and macroscopic experimental data.

Reinforced concrete (RC) trough bridges form a crucial part of Europe's railway infrastructure. These structures consist of a U-shaped cross-section (two longitudinal beams and a slab) designed to accommodate the ballast. For the case of the Iron Ore Line, a critical corridor located in the north of the country, RC trough bridges represent about 40 % of the railway bridge population. Many of these bridges have surpassed 50 years of service, enduring over 10 million cycles of fatigue loading, with increases of axle loads since their construction due to demands associated with the iron ore extraction and transportation. As these structures approach critical maintenance or replacement decisions, understanding their long-term performance and remaining capacity is essential. This study experimentally investigates the degradation behavior of a full-scale RC trough bridge subjected to progressive cyclic loading, simulating fatigue effects over time. During the controlled laboratory tests, the overall performance of the bridge is assessed, focusing on the stiffness loss of slab and beams, cracking, and force redistribution. Fatigue verifications based on Eurocode EN1992-1-1 and fib Model Code 2020 are performed alongside the reliability analysis using the First-Order Reliability Method (FORM) to evaluate structural safety levels. While fatigue damage is evident in the slab's tensile zone, the overall structural response indicates that the bridge maintains its functional capacity after simulating 48 years of service. However, the codebased reliability analysis indicates lower-than-target reliability levels for reinforcement, suggesting a conservative estimation of reinforcement capacity to withstand cyclic loading.

Molecular dynamics simulations have been increasingly employed to investigate the mechanical properties of cement hydrates at the nanoscale. This technique deepens the understanding of cement-based materials, yet correlating these nanoscale findings with larger scale experiments remains a challenge, particularly due to scaling effects. This study focuses on the scaling impact on calcium silicate hydrate (C-S-H). Two types of C-S-H models were constructed: one with defective silicate chains and the other without. Each model includes three sub-models of varying sizes. Under uniaxial tension along silicon chain direction, the stress and strain responses were recorded. The results show that at the nanoscale, model correction such as silicon chain breakage has a greater impact on the elastic modulus and tensile strength than model size. Additionally, the stress–strain curve obtained during the tension process needs to be corrected before comparison with stress–strain on other scales. The findings provide crucial insights into the mechanical behavior of C-S-H at the nanoscale and offer a theoretical basis for bridging the gap between nanoscale simulations and larger scale experimental results.

This study utilized molecular dynamics simulations to investigate the shear behaviour of the interface between new and old concrete. Two atomic models were developed to represent the new- to-old concrete interface, using hydrated calcium silicate (CSH) substrates with different water-to- silica ratios: CSH1 (H₂O/Si=1.0)-to-CSH3 (H₂O/Si=1.68) and CSH2 (H₂O/Si=1.5)-to-CSH3 interfaces. The results show that the primary bonding type in both interfaces is the Ca-O bond, and shear failure predominantly occurs within the CSH3 substrate with lower strength and unstable bond energy. Furthermore, the shear strength of the CSH1-to-CSH3 interface (0.93GPa) is 34.8% higher than that of the CSH2-to-CSH3 interface, indicating a correlation between shear property and bond energy stability and strength of the substrate. This study provides theoretical support for the design and optimization of new-to-old concrete interface.

Railway noise barriers are an essential piece of infrastructure for reducing noise propagation. However, these barriers experience aerodynamic loads generated by high-speed trains, leading to dynamic effects that may compromise their fatigue capacity. The most common structural design for railway noise barriers consists of vertical configurations of posts and panels. However, there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads. This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers. Analysis of a 40-m-long noise barrier model and a triangular simplified load model, the latter of which effectively represented the detailed aerodynamic load, were first used to establish the model and input of the moving load during dynamic simulation. Then, the effects of different parameters on the dynamic response of the noise barrier were evaluated, including the damping ratio, the profile of the steel post, the span length of the panel, the barrier height, and the train speed. Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses, followed by train speed, post profile, span length, and damping ratio. A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves. The dynamic amplification factor (DAF) was found to be related to both the natural frequency and train speed. A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.

The direct socio-economic consequences of the deterioration of aging infrastructure systems have triggered a continuous process of revising and updating current design standards and guidelines for critical network components. Specifically, long-term degradation processes demand the analysis and evaluation of vital structural assets such as prestressed concrete bridges. It is crucial to develop theoretically consistent, user-friendly, and non-destructive methodologies that engineering professionals can employ to prevent and mitigate potential catastrophic outcomes during the service life of these bridges. This study provides a thorough review of the available testing methods employed over the years for prestressed concrete bridges and introduces a comprehensive framework for evaluating existing methods for residual prestress force assessment. Through a multi-criteria selection process, the three most feasible tests were designed and carried out on an existing 66-year-old balanced cantilever box girder bridge exposed to freezing temperatures that affected the instrumentation plan and test execution. Finally, predictive models compliant with standard codes were calibrated based on the experimental results and the life cycle loss of prestress forces was evaluated to assess relevant bounding intervals. Findings reveal limited on-site testing and discrepancies between calculated residual forces and predictions by standard codes. The saw cut method showed a 18% difference from the initial applied prestress according to the prestress protocol, suggesting the use of a cover meter and concrete modulus evaluation for improved accuracy. The strand cutting method resulted in a 14% difference, emphasizing the need for stress redistribution assessment. The second-order deflection method showed a 6% difference, indicating a focus on enhanced boundary conditions and thorough sensitivity analysis for future investigations.

Geopolymer concrete offers superior mechanical properties and microstructure, yet micro-level compressive properties and structural evolutions remain insufficiently understood. This study employed molecular dynamics to simulate the uniaxial compressions of the sodium aluminosilicate hydrate (N-A-S-H) under UCZ, BCZ, and TCZ (z-axial compressions with zero, one, and two dimensions restrictions, respectively) conditions at 263 K, 300 K, and 800 K. The results provided valuable insights linking mechanical behavior with structural properties. Stress fluctuations in the yield stage were attributed to the continuous formation and fracture of Al-O-H bonds during micro-molecule processes. In the later compression stages, the rapid increase in Si-O-H groups suggested that water molecules equally attacked Al and Si tetrahedra due to limited voids. Under UCZ and BCZ conditions, slight bond contraction occurred, with the main structural resistance arising from bond angle bending within the skeleton. In contrast, TCZ experienced notable changes in both bond lengths and bond angles due to bilateral displacement constraints. The evolutionary molecular processes exhibited insensitive response to the temperature, especially under TCZ conditions. Additionally, varying trends were observed in different bond-angle styles (e.g., within or inside tetrahedra), providing a crucial insight for the design of N-A-S-H to determine optimal components.

Chloride anion attack is a major factor limiting the durability of concrete structures. To clarify the mechanisms by which chloride salts degrade concrete, nanoscale molecular dynamics (MD) simulations were used to study chloride attack on calcium silicate hydrate (CSH), the main component of cement. In MD simulations, a relaxation process is generally required to allow the system to reach equilibrium. However, relaxation is computationally expensive when performing MD simulations of large structural systems. This expense could potentially be avoided by using deep learning techniques. This paper describes the creation of a multi -fidelity physicsinformed neural network model of a CSH gel pore containing an aqueous NaCl solution. The neural network's input variables are the ambient temperature and the NaCl concentration and its output variables are the system's energy, the Na-O radial distribution function, and the Na+ and Cl- ion density distributions. After training the model using the results of low -fidelity MD simulations without relaxation and a smaller number of high-fidelity simulations with relaxation, highly accurate outputs were obtained with prediction errors below 3%. Deep learning can thus greatly reduce the computational cost of MD studies of large and complex systems with no appreciable loss of accuracy.

In this study, tension-tension fatigue tests were conducted to investigate the residual stiffness degradation of carbon fiber-reinforced polymer (CFRP) tendons. Different stress levels were used in the tests, and measurements of residual stiffness and the number of loading cycles were taken. Based on experimental data for CFRP tendons, a quantitative residual stiffness model was developed by modifying Yao's model. This model is applicable to various stress levels. To assess its accuracy and applicability, the predicted results of this model were compared with those of cited models from other researchers. The findings revealed a three-stage degradation of residual stiffness in CFRP tendons under different stress levels. Furthermore, it was observed that the proportion of fatigue life accounted for by Stage III decreased with smaller stress ranges, while the proportion accounted for by Stage II increased. The proposed quantitative residual stiffness model was verified using both experimental and cited data. Tension-tension fatigue tests of CFRP tendons were conducted at various stress levels. A quantitative model was proposed based on the residual stiffness of the CFRP tendon. Stress level influence on stiffness degradation of composite material was discussed. Model accuracy was verified against experimental and cited data.

This paper investigates the current landscape of multiscale studies in concrete composites incorporating molecular dynamics (MD) methods. Through a thorough literature analysis, it was determined that finite element, discrete element, homogenization, microphysical characterization, and machine learning methods are better suited for integration with MD in multiscale studies of concrete composites. The paper delves into MD's application characteristics and the selection of force fields in multiscale studies and provides a summary of the combined applications between MD and various methods. Challenges identified include the optimization of MD simulations and the appropriate selection of combined methods. The conclusions underscore the growing recognition of MD's significance, advocating for rational multi-method integration in multiscale approaches to effectively advance research on concrete composites.