Chao Wang

Sweden’s extensive network of over 4,000 railway bridges calls for innovative, data-driven strategies to ensure structural reliability, extend service life, and to meet evolving sustainability goals and increasing load demands. As part of a larger initiative, this study focuses on a comparative evaluation of three widely used life cycle calculation tools. We examined Trafikverket’s Klimatkalkyl (employed by Swedish government agencies), OneClick LCA (prominent in industrial practice), and ORIS Material Intelligence (a commonly applied approach). This comparative study is based on a unique full-scale replica of a 1970s reinforced concrete trough railway bridge, housed indoors at Luleå University of Technology in Luleå and equipped with over 100 sensors (including Fiber Optic Sensing (FOS) and Strain Gauges). This testbed generates up to 2 TB of data daily, serving as a robust foundation for structural health monitoring, life cycle and life costs assessments. By analysing the same bridge through various tools, we highlight how their differing algorithms may lead to variations in environmental impact estimates, resource optimization strategies as well as life cycle cost projections in the future. This study aims to determine the most suitable tool for guiding sustainable infrastructure decisions. It also aims to illustrate how these insights feed into the overarching initiative of a holistic bridge management platform, intended to provide a comprehensive, real-time overview of bridge conditions at both the asset and network levels. At last, this work supports Sweden’s broader transition to circular, climate-resilient infrastructure, equipping decision-makers with the comparative knowledge needed to optimize bridge longevity, minimize environmental impacts.

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.

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%.

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.

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.