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.

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.

Coal bottom ash (CBA) is recovered from thermal power plants; it is an essential byproduct of the coal industry, and dumping on open land is the most significant environmental risk. Sustainably using CBA can help in alleviating ecological problems. Therefore, this study investigates the possibilities of utilizing CBA as a sand replacement in concrete production. A series of tests, including slump test, compressive strength (CS), water absorption, and water sorptivity of various bottom ash-based concrete mixes, were evaluated at curing ages of 7, 28, and 90 days for the desired strength of M25 and M35 concrete. Additionally, to determine the CS of the CBA concrete support vector regression was used, and the hyperparameters were optimized using particle swarm optimization (PSO-SVR) and jellyfish search optimization (JSO-SVR). Besides the outcomes of experiments, the data from previously published studies was also compiled and utilized for the prediction models. Experimental results reveal that M25 and M35 concrete of Sahiwal ash exhibited 10 %, 11 % and 11.8 %, 10.3 % higher CS with 25 % and 50 % CBA at 90 days. Similarly, M25 and M35 concrete of Sheikhupura ash, the CS increased by 8.3 % and 10 % with 25 % CBA at 90 days. Higher CBA content raises water absorption and sorptivity, indicating decreased durability. Increasing CBA content reduces concrete workability due to the hygroscopic nature of CBA particles. The higher specific gravity of CBA enhances strength development, yielding better-quality concrete. In contrast, the outcomes of the JSO-SVR models exhibited R2 for the training, testing, and validation dataset, which were 0.974, 0.961, and 0.9601, respectively. Furthermore, the JSO-SVR predictions were interpreted using SHapley Additive exPlanations (SHAP). The SHAP analysis revealed that sand, curing age, and cement were the most influential features affecting CS.

The thermal power plants generate a significant amount of coal bottom ash (CBA), which poses environmental hazards. Effective utilization of CBA is essential to mitigate its adverse environmental impacts. In this study, the feasibility of using CBA-from two coal-fired power plants in Punjab, Pakistan, namely Sahiwal (SWL) and Sheikhupura (SKP)-as a partial sand replacement in concrete was investigated to promote sustainable concrete production. A series of concrete mixtures and experiments were conducted, including evaluations of Flexural Strength (FS) and Split Tensile Strength (SPT) at curing ages of 7, 28, and 90 days for M25 and M35 grade concrete. To enhance predictive capabilities, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) models optimized using Particle Swarm Optimization (PSO) were employed to accurately predict the FS and SPT of CBA-based concrete. In addition to experimental data, previously published datasets were incorporated to improve model robustness. Experimental results indicated that concrete incorporating SWL-CBA achieved FS and SPT values comparable to control mixes at 28 and 90 days for M25 and M35 grades when CBA replaced 25 % and 50 % of sand, respectively. However, SKP-CBA mixes consistently showed lower strength performance. The PSO-optimized XGBoost and LGBM models exhibited excellent predictive performance, with R² values exceeding 0.98 during training and reaching 0.96 during testing. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to interpret the PSO-LGBM model, revealing that curing age, the specific gravity (SG) of CBA, sand/cement ratio, and CBA, and curing age, cement content, water and CBA were the most influential features affecting FS and SPT, respectively. The study findings support the recommendation of using higher SG CBA as a sustainable partial sand replacement, contributing to natural sand conservation, while also highlighting the effectiveness of machine learning approaches in accurately modeling and optimizing concrete performance.

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.

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.

A concrete cold joint is a weak interface within structures, and is prone to shear failure. Additionally, as potential pathways for external corrosive ions, cold joints experience interface performance degradation in adverse environments, thereby compromising the durability of the structure. This study utilized molecular dynamics simulations to analyze the shear behavior of two models of concrete joints: the CSH-a-to-CSH-b (CC) interface and the CSH-a-to-SiO2 (CS) interface, based on calcium silicate hydrate (CSH) and silicon dioxide (SiO2) as substrates. The study found that the penetration ranges of water into the CC and CS interfaces were 17Å and 10.5Å, respectively. When exposed to a saline-alkaline environment, the penetration range of Na2SO4 solution increased by 26.5 % and 185.7 % compared to the humid environment for the CC and CS interfaces, respectively. Furthermore, corrosive environments influence ion interactions at the interfaces, leading substrate ions to react preferentially with ions in the corrosive solution, thereby weakening the bonding performance of the interface. The effect of environmental conditions on the shear performance of joints is ranked as follows: saline-alkaline > humid > dry. Under dry conditions, the maximum shear stress of the CC and CS interfaces reached 0.93 GPa and 0.88 GPa, respectively. In humid and saline-alkaline environments, the maximum shear stress of the CC interface decreased by 38.7 % and 68.8 %, while that of the CS interface decreased by 31.8 % and 62.5 %. Additionally, shear failure at the interfaces consistently occurred in regions where bond energy was unstable. This study provides atomic-scale insights into the degradation of concrete cold joints, guiding material design and interface optimization in engineering practice.

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.

New-to-old concrete interfaces, which are prone to shear failure, represent a weak point in structures. Current research concerning the shear performance of these interfaces primarily relies on experimental and microscopic methods, such as X-ray diffraction or Scanning Electron Microscopy, and hence provides little data at the nanoscale level. To reduce the knowledge gap, the presented research utilized molecular dynamics simulations to study the shear bonding performance of two models: an interface comprising calcium silicate hydrate CSH-a (H2O/Si=1.68) and CSH-b (H2O/Si=1.0) and a CSH-a-to-SiO2 interface. Analyses of the mechanical response, ionic interactions, and chemical bond breakage behaviors of the interfaces provided nanoscale-level insight concerning the shear characteristics of new-to-old concrete interfaces. Furthermore, this paper explores how shear rate influences the shear resistance of the interface. The research reveals that the CSH-a-to-CSH-b interface mainly involves Ca-O bonds, while hydrogen bonds are more prevalent at the CSH-a-to-SiO2 interface. Both of the models share consistent shear failure modes, more specifically, the shear failure surface occurs within the weaker CSH-a substrate rather than at the interface between substrates, which aligns with experimental observations, as shear failure at new-to-old concrete interfaces is often accompanied by the spalling of low-strength concrete in close proximity to the interface. Additionally, when the shear rate decreases from 0.01 Å/fs to 0.008 Å/fs, and then to 0.005 Å/fs, shear strength declines by 30.2 % and 40.5 %, respectively. The findings of this study clarify the molecular-level mechanisms which govern the shear performance of new-to-old concrete interfaces as well as offers theoretical support for the shear-resistant design and optimization of these interfaces.