Search Results (704)

Traumatic brain injury (TBI) is a significant public health concern and its rising prevalence in road traffic accidents underscores the need for deeper understanding and tailored investigation. This study explores the feasibility of employing the female finite element head model (FeFEHM) to analyse biomechanical responses in two distinct road traffic accident scenarios, focusing on strain and stress distribution in critical brain structures. Two collision scenarios from the German In-Depth Accident Study (GIDAS) were reconstructed using validated Total Human Model for Safety (THUMS) simulations. The extracted skull kinematics were applied to the FeFEHM in ABAQUS to compute maximum principal strain, von Mises stress, and intracranial pressure across key brain regions, including the corpus callosum and pituitary gland. Simulations revealed strain concentrations in the parietal and temporal lobes, while the mid-body region was the most affected in the corpus callosum. Pituitary gland deformation was minimal under both loading conditions. Our findings align qualitatively with reported injury sites and injury risk was consistent with those observed in the real-world crashes. The findings highlight the potential of integrating sex-specific biomechanical models into crash biomechanics workflows. Future work should extend this approach across larger datasets and impact scenarios to support its implementation in regulatory and engineering contexts, since the actual sample size prevents conclusions regarding sex-specific biomechanics. Full article

This study investigates the effectiveness of the finite element method for predicting the performance of flexible pavements subjected to varying traffic-loading and environmental conditions. A two-dimensional axisymmetric finite element model of a flexible pavement structure was developed using ABAQUS software v2022. In order to estimate the necessary pavement stiffness for reducing the likelihood of rutting and fatigue failure, the impacts of loading and operational characteristics of modern trucks were taken into consideration. Detailed parametric analyses were conducted to comprehensively assess the effect of substrate temperature profile variations on pavement performance. The findings demonstrate that utilization of a stiffer binder results in substantial reductions in critical tensile and compressive strains. Full article

Residential buildings near transport corridors are exposed to traffic-induced vibrations that can affect their safety. This paper presents a dynamic diagnosis of a masonry building located near a grade-separated junction. The study aimed to determine whether traffic-induced vibrations were responsible for the diagonal cracking of plaster observed in a dormer wall. The methodology included simultaneous acceleration measurements on the ground and building, traffic recording, 3D laser scanning for geometric reconstruction, and finite element modelling with soil–structure interaction. Time history and modal analyses were performed for various soil stiffness values. The results show that vibrations are predominantly attenuated at the soil–building interface, whereas soil flexibility markedly lowers the fundamental (lowest) natural frequencies of the building. The effect of soil stiffness on wall shear stress was more significant than that of dynamic action in load combinations. A comparison of the principal stress trajectories with the observed cracking patterns suggests that the damage was primarily due to the support condition of the wall. Traffic-induced vibrations are not the main cause of the observed damage. The integrated diagnostic procedure was effective in distinguishing vibration effects from other structural factors and was useful in assessing building safety. Full article

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab^®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities. Full article

Macroscopic fatigue tests, mesoscopic finite element simulations, and microscopic molecular dynamics simulations were composed to study the damage and failure of drainage asphalt mixtures in multiscale. The applicability of the fatigue models fit by strain, stress, and the linear fitting slope of the indirect tensile modulus curves were compared. The mesoscopic damage and failure distribution and evolution characteristics were studied, considering the single or coupling effect of traffic loading, hydrodynamic pressure, mortar aging, and interfacial attenuation. The microscopic molecular mechanism of the interface adhesion failure between the aggregate and mortar under water-containing conditions was analyzed. Results show that the fatigue model based on the linear fitting slopes of the indirect tensile modulus curves has significant applicability for drainage asphalt mixtures with different void rates and gradations. The damage and failure have an obvious leap development when traffic loading increases from 0.7 MPa to 0.8 MPa. The hydrodynamic pressure significantly increases the stress of the mortar around the voids and close to the aggregate, promoting damage development and crack extension, especially when it is greater than 0.3 MPa. With the aging deepening of the mortar, the increase rate of the damage degree gradually decreases from the top to the bottom of the mixture. With the development of interfacial attenuation, the damage and failure of interfaces continue increasing, while that of the mortar increases first and then decreases, which is related to the loading concentration in the interface and the stress decrease in the mortar. Under the coupling effects, whether the cracks mainly generate in the mortar or interface depends on their damage degrees, thus causing the stripping of the aggregate wrapped or not wrapped by the mortar, respectively. The van del Waals force is the main molecular effect of interface adhesion, and both acidic and alkaline aggregate components significantly tend to form hydrogen bonds with water rather than asphalt, thus attenuating the interface adhesion. Full article

Suspended monorail systems are increasingly adopted in urban rail transit due to their small land requirements and environmental benefits. However, welded details in track beams are prone to fatigue cracking under repeated service loads, posing risks to long-term structural safety. This study investigates the fatigue performance of suspended monorail track beams through 1:4 scaled fatigue experiments and finite element (FE) simulations. Critical fatigue-sensitive locations were identified at the mid-span longitudinal stiffener–bottom flange weld toe and the mid-span web–bottom flange weld toe. Under the most unfavorable operating condition (train speed of 30 km/h), the corresponding hot-spot stresses were 28.48 MPa and 27.54 MPa, respectively. Stress deviations between scaled and full-scale models were within 7%, verifying the feasibility of using scaled models for fatigue studies. Fatigue life predictions based on the IIW hot-spot stress method and Eurocode S–N curves showed that the critical details exceeded the 100-year design requirement, with estimated fatigue lives of 2.39 × 10⁸ and 5.95 × 10⁸ cycles. Furthermore, a modified damage equivalent coefficient method that accounts for traffic volume and train speed was proposed, yielding coefficients of 2.54 and 3.06 for the two fatigue-prone locations. The results provide a theoretical basis and practical reference for fatigue life evaluation, design optimization, and code development of suspended monorail track beam structures. Full article

Ship detection is critical for maritime traffic management, as infrared imaging in complex marine environments encounters significant challenges, such as strong background interference and weak target features. Thus, we propose EISD-YOLO (Efficient Infrared Ship Detection-YOLO), a high-performance lightweight algorithm specifically designed for infrared ship detection. The algorithm aims to improve detection accuracy while simultaneously reducing model parameters and enhancing computational efficiency. It integrates three core architectural innovations: first, we optimized the backbone C3k2 module by replacing the traditional bottleneck with a PEMA block to significantly reduce the parameter count; second, we integrated a lightweight DS_ADNet module, using depth-wise separable convolution to reduce parameters and alleviate computational load while maintaining robust feature representation; and third, we adopted the DyTAHead detection head, which integrates classification and localization features through dynamic task alignment, thereby achieving robust performance in complex infrared ship detection scenarios. The experimental results on the IRShip dataset demonstrate that, compared with YOLOv11n, EISD-YOLO reduced the parameters by 48.83%, while mAP@0.50, precision, and recall all increased by 1.2%. This breaks the traditional rule that lightweight models inevitably lead to reduced accuracy. Additionally, the model size reduced from 10.1 MB to 5.7 MB, which highlights its enhanced computational efficiency and practical applicability in maritime deployment scenarios. Full article

Due to intensive urbanization, global warming, and increasing energy demands, the impact of urban heat islands is becoming more significant. This study investigates the contribution of vehicular emissions to air pollution and its effects on urban heat island intensity in a selected area of Belgrade, Serbia, between March and September 2015, using a combination of experimental measurements and numerical simulations. Furthermore, this study presents the results of the research on the impact of assessment of traffic-induced air pollution on the appearance of thermal islands in the urban environment, as well as the characterization of thermal islands and their quantification. This study quantifies the effects of traffic-related emissions and urban meteorological parameters on the intensity of the urban heat island by combining field measurements with a validated three-dimensional numerical model and shows that higher traffic density increases pollutant concentrations and cooling energy demand in buildings. The study includes experimental measurements of traffic intensity and modeling of gas emissions from major roads. Using long-term and short-term field measurements, concentrations of carbon dioxide and other pollutants were analyzed with meteorological parameters and their cumulative impact to assess their impact on local air quality. A three-dimensional numerical model for simulating the dispersion of pollutants has been developed, confirmed and validated by experimental data. The results highlight a direct correlation between traffic density and pollutant concentrations, emphasizing the need for strategic urban planning and sustainable transport policies to mitigate the effects of air pollution. A validated numerical model was used to simulate dynamic changes in temperature fields and carbon dioxide concentrations caused by vehicular emissions. The findings reveal that the Urban Heat Island Intensity (UHII) for the selected area in Belgrade reached peaks of up to 12 °C during the summer measurement period, with typical values in July ranging from 5 °C to 9 °C. Furthermore, the validated numerical model demonstrated that the removal of urban trees would lead to a local air temperature increase of 1.5 °C to 3 °C, quantifying the significant cooling potential of green infrastructure. These results highlight a direct correlation between traffic density, pollutant concentrations, and the intensification of urban heat islands, emphasizing the need for strategic urban planning. Furthermore, the findings reveal that increased traffic not only elevates air pollutant levels but also enhances the intensity of urban heat islands, leading to higher cooling energy demands in buildings. These insights are vital for developing effective mitigation strategies to improve the sustainability of urban environments and living conditions. These findings provide a clear directive for urban planners: the integration and preservation of green infrastructure is a highly effective UHI mitigation strategy, capable of reducing local temperatures by 1.5–3 °C. Furthermore, the results strongly support the implementation of targeted traffic management policies in dense urban cores as a dual strategy to improve air quality and reduce local thermal loads. Full article

With the advancement of battery technology and the promotion of the “dual carbon” policy, electric vehicles (EVs) have been widely used in industrial, commercial, and civil fields, and the charging infrastructure of highway service areas across the country has also shown a rapid development trend. However, the charging load of electric vehicles in highway scenarios exhibits strong randomness and uncertainty. It is affected by multiple factors such as traffic flow, state of charge (SOC), and user charging behavior, and it is difficult to accurately model it through traditional mathematical models. This paper proposes a hierarchical Markov chain Monte Carlo (HMMC) simulation method to construct a charging load prediction model with spatiotemporal coupling characteristics. The model hierarchically models features such as traffic flow, SOC, and charging behavior through a hierarchical structure to reduce interference between dimensions; by constructing a Markov chain that converges to the target distribution and an inter-layer transfer mechanism, the load change process is deduced layer by layer, thereby achieving a more accurate charging load prediction. Comparative experiments with mainstream methods such as ARIMA, BP neural networks, random forests, and LSTM show that the HMMC model has higher prediction accuracy in highway scenarios, significantly reduces prediction errors, and improves model stability and interpretability. Full article

Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic risks or the interdependencies between transportation and power systems. This study proposes an adaptive, risk-driven control framework that holistically coordinates power generation infrastructures, microgrids, demand-side loads, energy storage systems, and transport dynamics through continuous risk assessment. This enables the system to dynamically shift operational priorities—from cost-efficiency in stable periods to robustness during emergencies. A multi-objective optimization model is established, integrating infrastructure resilience, operational costs, and traffic impacts. It is solved using an enhanced evolutionary algorithm that combines the non-dominated sorting genetic algorithm II with differential evolution (NSGA-II-DE). Extensive simulations under extreme weather scenarios validate the framework’s ability to autonomously reconfigure operations, achieving 92.5% renewable energy utilization under low-risk conditions while elevating critical load assurance to 98.8% under high-risk scenarios. This strategy provides both theoretical and technical guarantees for securing highway renewable energy system operations. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO₂, NO_x, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems. Full article

This article aims to perform system identification of a nearly 30-year-old cable-stayed steel footbridge over the Wisłok River in Rzeszów (Poland). The design documentation of the bridge has been lost, and since its construction, the footbridge has been subject to renovations. The structure is highly susceptible to pedestrian traffic, and before any actions are taken to improve the comfort of use, it is necessary to create and validate a numerical model and assess the force distribution in the structure. Models are often built as mappings of an ideal structure. However, real structures are not ideal. The comparison of numerical and measured data can allow for an indication of potential damage areas. Two main purposes of the article have been formulated: (1)Development of a numerical model of an old footbridge, whose components have been degraded due to long-term use. Changes, compared to the ‘original’, focused on elongation of the cables due to rheology and a decrease in their tension. (2) Demonstrate the challenges in modeling and validating this type of bridge. In the article, the result of the numerical simulation (Finite Element Method and Ansys2024 R2 was applied, the verification was made in RFEM6) for models with different boundary conditions and varied pre-tension in cables was compared with the results of static and dynamic examination of a real object. The dynamic tests showed an uneven distribution of pre-tension in cables. The ratio of the first natural frequencies of inner cables on the north side is as high as 16%. The novelty demonstrated in the article is that static tests are insufficient for proper system identification; the same value of vertical displacement can be obtained for a selected static load, with varied tension in cables. Therefore, dynamic testing is essential. Full model updating requires a multicriteria approach, which will be made in the future. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

As the core component of electrified railway power supply systems, the fatigue performance and reliability of catenary support structures are directly related to the operational safety of high-speed railways. To address the problem of structural fatigue damage caused by increasing train speed and high-frequency operation, this study develops a refined finite element model including a support structure, suspension system and support column, and the dynamic response characteristics and fatigue life evolution law under train operation conditions are systematically analyzed. The results show that under the conditions of 250 km/h speed and 100 times daily traffic, the fatigue lives of the limit locator and positioning support are 43.56 years and 34.48 years, respectively, whereas the transverse cantilever connection and inclined cantilever have infinite life characteristics. When the train speed increases to 400 km/h, the annual fatigue damage of the positioning bearing increases from 0.029 to 0.065, and the service life is shortened by 55.7% to 15.27 years, which proves that high-speed working conditions significantly aggravate the deterioration of fatigue in the structure. The reliability analysis based on Monte Carlo simulation reveals that when the speed is 400 km/h and the daily traffic is 130 times, the structural reliability shows an exponential declining trend with increasing service life. If the daily traffic frequency exceeds 130, the 15-year reliability decreases to 92.5%, the 20-year reliability suddenly decreases to 82.4%, and there is a significant inflection point of failure in the 15–20 years of service. Considering the coupling effect of environmental factors (wind load, temperature and freezing), the actual failure risk may be higher than the theoretical value. On the basis of these findings, engineering suggestions are proposed: for high-speed lines with a daily traffic frequency of more than 130 times, shortening the overhaul cycle of the catenary support structure to 7–10 years and strengthening the periodic inspection and maintenance of positioning support and limit locators are recommended. The research results provide a theoretical basis for the safety assessment and maintenance decision making of high-speed railway catenary systems. Full article