Search Results (500)

In recent years, China has vigorously promoted the EPC mode in the construction industry. However, under the weak trust environment of China’s construction industry, both owners and general contractors are involved in the design stage of EPC projects. Owing to conflicting interests in the design stage, there is a two-stage design interest paradox between the owners and general contractors of Chinese EPC projects, and this causes significant difficulties and challenges for project implementation. To resolve this paradox, this study proposes the “DART-PDCA” design management model by integrating value co-creation theory with the PDCA cycle. Applied to the Yuzhou High-speed Rail Station Square and Related Infrastructure PPP Project and the extended case, the model demonstrates how it resolves the paradox by (1) establishing structured dialogue platforms for aligning evolving design intentions, (2) enhancing information access and transparency through agreed protocols, and (3) facilitating dynamic risk assessment and allocation mechanisms. The results confirm that (1) the two-stage design interest paradox negatively impacts design management quality in China’s low-trust environment; and (2) the “DART-PDCA” design management model effectively resolves this paradox, leading to demonstrable improvements in design management quality, efficiency, and stakeholder alignment. This research forges novel interdisciplinary linkages among owner–general contractor relationships, design management, and EPC projects, providing critical insights into managing multi-organizational dynamics in complex EPC project environments. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

Rail freight transport represents a key tool for the decarbonisation and greening of logistics chains within the European Union. However, in many Central and Eastern European countries, including the Slovak Republic, a vast network of industrial sidings (rail spurs) remains underutilized or neglected. This reduces the overall efficiency of transport infrastructure and represents a missed opportunity for sustainable transport development. This paper proposes a comprehensive public support model for rail sidings. It combines legislative analysis, a tax incentive mechanism, and analytical evaluation of transport and investment benefits. The methodology calculates the potential transport output of reactivated sidings. It also introduces three quantitative indexes: the Siding Efficiency Index (IEV), the Comprehensive Importance Index (ICV), and the Reactivation Value Index (RVI). These indicators allow for a structured, objective assessment of siding suitability for restoration and public funding. We applied the model to a sample of five sidings in Slovakia, deriving values from expert evaluations. The results show that objective indicators, performance estimates, and targeted public support can identify infrastructure with high revitalization potential. These tools help reintegrate such assets into sustainable transport flows. The analysis indicates that reactivating 5% of existing sidings could shift hundreds of thousands of tonnes of freight annually from road to rail. This change would reduce emissions and improve network efficiency. Full article

Traffic-induced vibrations from road and rail systems pose a significant threat to the structural integrity and operational safety of buildings, especially masonry structures located near planned infrastructure such as tunnels. This study investigates the dynamic impact of such vibrations on a representative early 20th-century masonry building situated within the influence zone of a design railway tunnel. A comprehensive analysis combining geological, structural, and vibration propagation data was conducted. A detailed 3D finite element model was developed in Diana FEA v10.7, incorporating building material properties, subsoil conditions, and anticipated train-induced excitations. Various vibration isolation strategies were evaluated, including the use of block supports and vibro-isolation mats. The model was calibrated using pre-construction measurements, and simulations were carried out in the linear-elastic range to prevent resident-related claims. Results showed that dynamic stresses in masonry walls and wooden floor beams remain well below critical thresholds, even in areas with stress concentration. Among the tested configurations, vibration mitigation systems significantly reduced the transmitted forces. This research highlights the effectiveness of integrated numerical modelling and vibration control solutions in protecting structures from traffic-induced vibrations and supports informed engineering decisions in tunnel design and urban development planning. Full article

This study explores the potential for reducing CO₂ emissions in China’s express freight sector by promoting a modal shift from air and road transport to high-speed rail (HSR) through the implementation of a carbon tax policy. A bi-level optimization model is employed to analyze the decision-making processes of three key stakeholders: the government, HSR operators, and shippers. The government aims to maximize consumer surplus while reducing CO₂ emissions through a carbon tax policy; HSR operators seek to maximize transportation profit; and shippers select the most efficient transportation mode based on cost and service considerations. A solution algorithm combining particle swarm optimization, the CPLEX solver, and a custom convergence procedure is designed to solve the bi-level programming model and determine the optimal carbon tax rate. The findings from the Beijing–Shanghai corridor case study indicate that a well-designed carbon tax policy, when integrated with robust HSR services, can effectively encourage a modal shift towards HSR. The extent of emission reduction is influenced by both the capacity of HSR infrastructure and the stringency of the carbon tax policy. This research highlights the importance of addressing asymmetries in transportation mode preferences and market demands. The integration of carbon tax policies with HSR services not only mitigates emissions but also promotes greater symmetry and efficiency within the transportation network. Full article

Bratislava is currently experiencing massive development, and its developers are very active. As the city develops, the improvement of its public transport becomes increasingly crucial. Public transport (PT) must be ecological, economical, and accessible to all social groups of the population. Bratislava currently has the opportunity to change the modal split in favor of PT and thus end the decline that began in the early 1990s. Rail transport is an ecological type of PT incorporated into smart cities, contributing to city land use. The current PT rail track in Bratislava comprises tram and train infrastructure. Trains ensure the transportation of people from the municipalities surrounding Bratislava, while trams ensure the transportation of people within the city. Tram and train PT must be merged, as their integration could improve traveling times. Bratislava is suitable for the creation of a dual rail transport system covering the urbanized area. The goal of this article is to present a technical solution for a double-gauge system for operation, considering traffic engineering and planning to aid decision making. Considerable professional and expert work was undertaken, in contrast to the political administration’s “decision making”. Cases from Central Europe are presented. Full article

Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time parameter estimation are essential for ensuring safety, efficiency, and reliability. Traditional empirical and black-box data-driven approaches often fail to account for the underlying physical mechanisms, thereby limiting interpretability and generalizability. To address this, we propose a unified framework that integrates physics-informed scenario-based modeling with data-driven parameter inversion. In the first stage, critical system parameters—such as friction coefficients in drill string movement or contact forces in rail–wheel interactions—are explicitly formulated based on mechanical theory, leveraging symmetries and boundary conditions to improve model structure and reduce computational complexity. In the second stage, model parameters are identified or updated through inverse modeling using historical or real-time field data, enhancing predictive performance and engineering insight. The proposed methodology is demonstrated through two representative cases. The first involves friction estimation during tripping operations in the SU77-XX-32H5 ultra-long horizontal well of the Sulige Gas Field, where a mechanical load model is constructed and field-calibrated. The second applies the framework to rail transit systems, where wheel–rail friction is estimated from dynamic response signals to support condition monitoring and wear prediction. The results from both scenarios confirm that incorporating physical symmetry and data-driven inversion significantly enhances the accuracy, robustness, and interpretability of engineering analyses across domains. Full article

Floods pose a substantial risk to human well-being. These risks encompass economic losses, infrastructural damage, disruption of daily life, and potential loss of life. This study presents a state-wide and county-level spatial exposure assessment of the Iowa railway network, emphasizing the resilience and reliability of essential services during such disasters. In the United States, the railway network is vital for the distribution of goods and services. This research specifically targets the railway network in Iowa, a state where the impact of flooding on railways has not been extensively studied. We employ comprehensive GIS analysis to assess the vulnerability of the railway network, bridges, rail crossings, and facilities under 100- and 500-year flood scenarios at the state level. Additionally, we conducted a detailed investigation into the most flood-affected counties, focusing on the susceptibility of railway bridges. Our state-wide analysis reveals that, in a 100-year flood scenario, up to 9% of railroads, 8% of rail crossings, 58% of bridges, and 6% of facilities are impacted. In a 500-year flood scenario, these figures increase to 16%, 14%, 61%, and 13%, respectively. Furthermore, our secondary analysis using flood depth maps indicates that approximately half of the railway bridges in the flood zones of the studied counties could become non-functional in both flood scenarios. These findings are crucial for developing effective disaster risk management plans and strategies, ensuring adequate preparedness for the impacts of flooding on railway infrastructure. Full article

This study presents a method for evaluating and optimizing the service network capacity of Urban Rail Transit Networks (URTNs) based on existing infrastructure conditions. By integrating passenger route choice behavior, the method assesses the network’s potential maximum capacity through the actual utilization rates of throughput capacity across various sections and routes. Furthermore, by incorporating route design and Transit-Oriented Development (TOD) strategies, the approach achieves a dual enhancement of network capacity and service quality. An optimization model was developed to maximize the network capacity while minimizing passenger travel costs, and it was solved using Adaptive Large Neighborhood Search (ALNS) and the Method of Successive Averages (MSA) algorithms. A case study of the Chongqing URTN demonstrated the model’s effectiveness. The results indicate that integrating route design and TOD strategies can significantly enhance the service capacity of urban rail networks. This method will assist decision-makers in understanding the current utilization status of the network’s capacity and evaluating its potential capacity. During TOD planning at stations, it simultaneously assesses changes in network capacity, thereby achieving a balance between land development, passenger demand, and the transportation system. Full article

With accelerating urbanization, the development of rail transit systems—particularly subways—has become a key strategy for alleviating urban traffic congestion. However, existing studies on subway station site selection often lack a spatially continuous evaluation of site suitability across the entire study area. This may lead to a disconnect between planning and actual demand, resulting in issues such as “overbuilt infrastructure” or the “island effect.” To address this issue, this study selects Kunming City, China, as the study area, employs the K-means++ algorithm to cluster existing subway stations based on passenger flow, integrates multi-source spatial data, applies a random forest algorithm for optimal positive sample selection and driving factor identification, and subsequently uses a LightGBM-SHAP explainable machine learning framework to develop a predictive model for station location based on mathematical modeling. The main findings of the study are as follows: (1) Using the random forest model, 20 key drivers influencing site selection were identified. SHAP analysis revealed that the top five contributing factors were connectivity, nighttime lighting, road network density, transportation service, and residence density. Among these, transportation-related factors accounted for three out of five and emerged as the primary determinants of subway station site selection. (2) The site selection prediction model exhibited strong performance, achieving an R² value of 0.95 on the test set and an average R² of 0.79 during spatial 5-fold cross-validation, indicating high model reliability. The spatial distribution of predicted suitability indicated that the core urban area within the Second Ring Road exhibited the highest suitability, with suitability gradually declining toward the periphery. High-suitability areas outside the Third Ring Road in suburban regions were primarily aligned along existing subway lines. (3) The cumulative predicted probability within a 300 m buffer zone around each station was positively correlated with passenger flow levels. Overlaying the predicted results with current station locations revealed strong spatial consistency, indicating that the model outputs closely align with the actual spatial layout and passenger usage intensity of existing stations. These findings provide valuable decision-making support for optimizing subway station layouts and planning future transportation infrastructure, offering both theoretical and practical significance for data-driven site selection. Full article

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article

Urban rail transit networks’ rapid expansions have led to increasing intersections between existing and new lines, particularly in dense urban areas where new stations must underpass existing infrastructure at zero distance. Deformation controls during construction are critical for maintaining the operational safety of existing stations, especially in soft soil conditions where construction-induced settlement poses significant risks to structural integrity. This study systematically investigates the influence mechanisms of different construction schemes on base plate deformation when an open-top UBIT (underground bundle composite pipe integrated by transverse pre-stressing) underpasses existing stations. Through precise numerical simulation using PLAXIS 3D, the research comparatively analyzed the effects of 12 pipe jacking sequences, 3 pre-stress levels (1116 MPa, 1395 MPa, 1674 MPa), and 3 soil chamber excavation schemes, revealing the mechanisms between the deformation evolution and soil unloading effects. The continuous jacking strategy of adjacent pipes forms an efficient support structure, limiting maximum settlement to 5.2 mm. Medium pre-stress level (1395 MPa) produces a balanced deformation pattern that optimizes structural performance, while excavating side chambers before the central chamber effectively utilizes soil unloading effects, achieving controlled settlement distribution with maximum values of −7.2 mm. The optimal construction combination demonstrates effective deformation control, ensuring the operational safety of existing station structures. These findings enable safer and more efficient urban underpassing construction. Full article

Forest trees have emerged as a promising passive solution for mitigating low-frequency ground vibrations generated by railway operations, offering ecological and cost-effective advantages. This study proposes a three-dimensional semi-analytical method developed for evaluating the dynamic responses of the coupled track–ground–tree system. The thin-layer method is employed to derive an explicit Green’s function corresponding to a har-monic point load acting on a layered half-space, which is subsequently applied to couple the foundation with the track system. The forest trees are modeled as surface oscillators coupled on the ground surface to evaluate the characteristics of multiple scattered wavefields. The vibration attenuation capacity of forest trees in mitigating railway-induced ground vibrations is systematically investigated using the proposed method. In the direction perpendicular to the track on the ground surface, a graded array of forest trees with varying heights is capable of forming a broad mitigation frequency band below 80 Hz. Due to the interaction of wave fields excited by harmonic point loads at multiple locations, the attenuation performance of the tree system varies significantly across different positions on the surface. The influence of variability in tree height, radius, and density on system performance is subsequently examined using a Monte Carlo simulation. Despite the inherent randomness in tree characteristics, the forest still demonstrates notable attenuation effectiveness at frequencies below 80 Hz. Among the considered parameters, variations in tree height exert the most pronounced effect on the uncertainty of attenuation performance, followed sequentially by variations in density and radius. Full article

The significant energy intensity of high-speed railway necessitates integrating renewable technologies to enhance grid resilience and decarbonize transport. This study establishes a coordinated carbon–green certificate market mechanism for railway power systems and develops a tri-source planning model (grid/solar/energy storage) that comprehensively considers the full lifecycle carbon emissions of these assets while minimizing lifecycle costs and CO₂ emissions. The proposed EDMOA algorithm optimizes storage configurations across multiple operational climatic regimes. Benchmark analysis demonstrates superior economic–environmental synergy, achieving a 23.90% cost reduction (USD 923,152 annual savings) and 24.02% lower emissions (693,452.5 kg CO₂ reduction) versus conventional systems. These results validate the synergistic integration of hybrid power systems with the carbon–green certificate market mechanism as a quantifiable pathway towards decarbonization in rail infrastructure. Full article