Load tests are one of the most powerful tools for bridge assessment, revealing true system behavior, calibrating models, and building public confidence. However, temperature and thermal gradient effects, especially in sub-arctic climates, pose a significant challenge, as they can dominate the structural response and make performance assessments harder to interpret. This paper addresses this challenge by analyzing a unique dataset from a prestressed concrete bridge, comprising five years of multi-sensor SHM data and a final, controlled load test before its demolition. We present a climate-aware, hybrid-modelling workflow that separates temperature-driven responses from vehicle-induced effects. By systematically normalizing the predicted responses to a defined reference climate, the methodology demonstrates tangible benefits, as reflected in discernible variations in owner-facing performance metrics. Furthermore, an “error vs https://www.w3.org/1998/Math/MathML" display="inline">ΔT ” analysis is proposed to highlight the relationship between thermal gradients and prediction accuracy, offering additional insights into the robustness and applicability of the climate-conditioned model. 

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.

Railway noise barriers are frequently subjected to train-induced aerodynamic loads during long-term service, which may lead to fatigue problems at the welded base of steel posts. Conventional fatigue assessment methods often fail to account for the complex operational and environmental conditions experienced by real structures. To address this issue, this study proposes a fatigue evaluation framework that integrates long-term field monitoring data with machine learning (ML) and numerical simulation. Based on a field test database, a data-driven calculation model was developed to predict the stress responses of barrier steel posts under actual operational and environmental conditions. A finite element (FE) model validated by field data was further used to establish a quantitative relationship between the monitored stress responses and the maximum stress at the steel post base. Fatigue life for weld toe and weld root failure modes was evaluated using Palmgren-Miner linear damage rule and the nominal stress method in the Eurocode. Results show that weld root failure governs the fatigue performance of the steel post base, with significantly shorter fatigue lives than weld toe failure. Nose waves dominate fatigue damage, while tail waves still contribute about 14–23%. The fatigue life is also strongly influenced by train operating speeds and train type distribution, highlighting the importance of considering realistic train operating conditions in fatigue assessment and design. The proposed framework provides a practical and scalable tool for fatigue assessment and lifecycle maintenance planning of railway noise barrier systems.

Reinforced concrete structures, over the long term, gradually exhibit fatigue damage as a result of various external factors such as moving loads, temperature variations, and natural disasters. Fatigue refers to the initiation and propagation of cracks, deformation, and cumulative damage under cyclic loading. It is characterized by its hidden, cumulative, and irreversible nature, posing significant threats to structural integrity. We undertook a comprehensive review of fatigue degradation models for reinforced concrete, including a summary of existing fatigue detection methods for concrete, an overview of the fatigue performance of individual material components in reinforced concrete structures, a summary and evaluation of existing fatigue degradation models, a summary of fatigue life prediction methods, and an investigation into the effects of fatigue loading on bond-slip behavior between steel reinforcement and concrete. This review aims to provide a reference for future research on fatigue in reinforced concrete structures.

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.

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 modelling, 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%.