Search Results (3,613)

The advancement of urban rail transit is increasingly confronted with environmental challenges related to vibration and noise. To investigate the critical issues surrounding vibration propagation and the generation of structure-borne noise, a two-story frame building was selected for on-site measurements of both vibration and its induced structure-borne noise. The collected data were analyzed in both the time and frequency domains to explore the correlation between these phenomena, leading to the proposal of a hybrid prediction method for structural noise that was subsequently compared with measured results. The findings indicate that the excitation of structure-borne noise produces significant waveforms within sound signals. The characteristic frequency of the structure-borne noise is 25–80 Hz, as well as that of the train-induced vibration. Furthermore, there exists a positive correlation between structural vibration and structure-borne noise, whereby increased levels of vibration correspond to more pronounced structure-borne noise; additionally, indoor distribution patterns of structure-borne noise are non-uniform, with corner wall areas exhibiting greater intensity than central room locations. Finally, a hybrid prediction methodology that is both semi-analytical and semi-empirical is introduced. The approach derives dynamic response predictions of the structure through analytical solutions, subsequently estimating the secondary noise within the building’s interior using a newly formulated empirical equation to facilitate rapid predictions regarding indoor building vibrations and structure-borne noises induced by subway train operations. Full article

Many studies and technology companies highlight the actual or potential value of Digital Twins, but they often fail to demonstrate this value or how it can be realised. This gap constitutes a barrier for infrastructure asset management organisations in their attempt to innovate and incorporate digital twinning opportunities in their decision-making processes and their asset management planning activities. Asset management planning activities often make use of existing value-based decision-support tools to select and prioritise investments in physical assets. However, these tools were not originally designed to consider digital twinning investments that also compete for funding. This paper addresses this gap and proposes a value-based analysis for digital twinning opportunities in infrastructure asset management. The proposed analysis method is tested with three rail and road infrastructure case studies: (i) real-time monitoring of a power transformer; (ii) BIM for the design, construction, and maintenance of a new railway line; and (iii) infrastructure displacement monitoring using satellite data (InSAR). The study shows that the proposed method provides a conceptual construct and a common language that facilitates the communication of digital twinning opportunities in terms of their relevance in different contexts. The proposed method can be used to support the investment decision-making process for investments in both physical and non-physical assets and help derive maximum value from the limited available resources. Full article

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm. Full article

The railway is an essential source of logistics and transportation in cold regions, but low temperatures and icing can be challenging for uninterrupted railway operations in these regions. Icing on railway switches is a safety hazard, and presently, one of the industry’s adaptive approaches for ice mitigation is the use of resistive heaters. This method is efficient but consumes a great amount of electricity, leading to high financial costs in terms of the operation and maintenance of railway tracks in ice-prone regions. In this paper, a study is carried out using experiments and computational simulations to analyze the heat distribution in a cross-section of a rail at below-freezing temperatures. Experiments are performed in a cold room using an actual rail switch, thermocouples, and infrared imaging, while numerical analyses are carried out using a MATLAB-based analytical model to simulate the heat transfer, considering a section of stock rail and a heating element. Results show a considerable loss of heat from the heater to the surroundings of the rail, especially towards the ground ballast. Numerical simulation results provide a good insight into heat transfer along railway sections, and results are validated with experiments, where a good agreement is found. This study provides a good base for further optimization of resistive heating operations for ice mitigation along railway switches. Full article

This paper introduces a multiloop stabilized low-dropout regulator with a DC power supply rejection ratio of 85 dB and a phase margin of 80°. It is suitable for low-power, low-voltage and area-efficient applications since it consumes less than 100 μA. The dropout voltage is only 400 mV and the power supply rails are 1 V. Furthermore, a full mathematical analysis is conducted for stability and noise before the circuit verification. To confirm the proper operation of the implementation process, voltage and temperature corner variation simulations are extracted. The proposed regulator is designed and verified utilizing the Cadence IC Suite in a TSMC 90 nm CMOS process. Full article

The control of the performance of single-walled carbon nanotube (SWCNT) random network-based transistors is of critical importance for their applications in electronic devices, such as complementary metal oxide semiconducting (CMOS)-based logics. In ambient conditions, SWCNTs are heavily p-doped by the H₂O/O₂ redox couple, and most doping processes have to counteract this effect, which usually leads to broadened hysteresis and poor stability. In this work, we coated an SWCNT network with various common polymers and compared their thin-film transistors’ (TFTs’) performance in a nitrogen-filled glove box. It was found that all polymer coatings will decrease the hysteresis of these transistors due to the partial removal of charge trapping sites and also provide the stable control of the doping level of the SWCNT network. Counter-intuitively, polymers with electron-withdrawing functional groups lead to a dramatically enhanced n-branch in their transfer curve. Specifically, SWCNT TFTs with poly (vinylidene fluoride) coating show an n-type mobility up to 61 cm²/Vs, with a decent on/off ratio and small hysteresis. The inverters constructed by connecting two ambipolar TFTs demonstrate high gain but with certain voltage loss. P-type or n-type doping from polymer coating layers could suppress unnecessary n- or p-branches, shift the threshold voltage and optimize the performance of these inverters to realize rail-to-rail switching. Similar devices also demonstrate interesting antiambipolar performance with tunable on and off voltage when tested in a different configuration. Full article

Launcher efficiency is an important index for evaluating the performance of the electromagnetic launcher, and it reflects the ability of the launcher to convert input electrical energy into kinetic energy of the armature. In this paper, the launcher efficiency is taken as the objective function of bore parameter optimization, and particle swarm optimization is used to optimize the initial parameters of the BP neural network to improve the accuracy of the neural network in predicting launcher efficiency. The results show the following: (1) The predicted efficiency of the launcher shows the same trend as the experimental results. When the ratio of rail separation and rail height is greater than 1.75, the rate of change in the launcher efficiency curve decreases as the rail separation increases. (2) The weight of the influence of each parameter on the launcher efficiency follows the following law: convex arc height > rail separation > rail height > rail thickness. (3) The mean absolute error of the BP neural network prediction is 0.70%; after optimization by PSO, the mean absolute error is reduced to 0.28% and the mean relative accuracy is improved from 0.9774 to 0.9910, which indicates the feasibility of the PSO-BP neural network for the prediction of the launcher efficiency of an electromagnetic launcher. Full article

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. Full article

Urban rail transit, a convenient and fast public transportation mode with rapid construction and development, occupies fewer land resources and accommodates large passenger volumes. However, thermal comfort should be given more attention. Stations above ground level experience poor thermal comfort on the platforms, especially in hot summers. This study combines field research with a simulation analysis to propose a strategy for improving thermal comfort on above-ground urban rail transit platforms. This study analyzed the effects of the skylight opening rate, side window opening rate, design of transparent maintenance structure shading, and the platform profile shape on the thermal comfort of above-ground stations using field research, comparative experiments, and a simulation analysis with the PHOENICS (Command Prompt) software. The results indicate that adding longitudinal sunshade louvers to the skylight of the station platform is a cost-effective method to reduce the average temperature and PMV value, thereby improving thermal comfort. Increasing the skylight opening rate can result in a temperature rise. Adjusting the opening rate of the side windows to 20% and adding sun-shading louvers can also significantly enhance the station’s thermal comfort. Taking Wudaokou Station on Beijing Line 13 as an example, the simultaneous installation of additional longitudinal skylight shading and side window shading and increasing the side window opening rate to 20% on the platform resulted in a 2.6 °C decrease in the average temperature, a 4.7% increase in the average wind speed, and a 0.62 decrease in the PMV value, significantly enhancing thermal comfort for passengers. This study confirms that optimizing shading and ventilation systems can significantly reduce the platform temperature and improve passengers’ thermal comfort. This study provides theoretical support and innovative methods for optimizing thermal environments in similar environments. Full article

Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection. Full article

Aiming at the problem of high failure rate and inconvenient maintenance of wind turbine gearboxes, this paper establishes a stochastic differential equation model that can be used to fit the change of gearbox oil temperature and adopts an iterative computational method and Markov-based modified optimization to fit the prediction sequence in order to realize the accurate prediction of gearbox oil temperature. The model divides the oil temperature change of the gearbox into two parts, internal aging and external random perturbation, adopts the approximation theorem to establish the internal aging model, and uses Brownian motion to simulate the external random perturbation. The model parameters were calculated by the Newton–Raphson iterative method based on the gearbox oil temperature monitoring data. Iterative calculations and Markov-based corrections were performed on the model prediction data. The gearbox oil temperature variations were simulated in MATLAB, and the fitting and testing errors were calculated before and after the iterations. By comparing the fitting and testing errors with the ordinary differential equations and the stochastic differential equations before iteration, the iterated model can better reflect the gear oil temperature trend and predict the oil temperature at a specific time. The accuracy of the iterated model in terms of fitting and prediction is important for the development of preventive maintenance. Full article

The development of a soft crawling robot (SCR) capable of quick folding and recovery has important application value in the field of biomimetic engineering. This article proposes an origami-inspired vacuum-actuated foldable soft crawling robot (OVFSCR), which is composed of entirely soft foldable mirrored origami actuators with a Kresling crease pattern, and possesses capabilities of realizing multimodal locomotion incorporating crawling, climbing, and turning movements. The OVFSCR is characterized by producing periodically foldable and restorable body deformation, and its asymmetric structural design of low front and high rear hexahedral feet creates a friction difference between the two feet and contact surface to enable unidirectional movement. Combining an actuation control sequence with an asymmetrical structural design, the body deformation and feet in contact with ground can be coordinated to realize quick continuous forward crawling locomotion. Furthermore, an efficient dynamic model is developed to characterize the OVFSCR’s motion capability. The robot demonstrates multifunctional characteristics, including crawling on a flat surface at an average speed of 11.9 mm/s, climbing a slope of 3°, carrying a certain payload, navigating inside straight and curved round tubes, removing obstacles, and traversing different media. It is revealed that the OVFSCR can imitate contractile deformation and crawling mode exhibited by soft biological worms. Our study contributes to paving avenues for practical applications in adaptive navigation, exploration, and inspection of soft robots in some uncharted territory. Full article

As electricity demand increases, especially in transportation, renewable sources such as solar energy become more important. The direct integration of solar energy in rail transportation mostly involves utilizing station roofs and track side spaces. This paper proposes a novel approach by proposing the integration of photovoltaic systems directly on the roofs of trains to generate clean electricity and reduce dependence on the main grid. Installing solar photovoltaic (PV) systems on train rooftops can reduce energy costs and emissions and develop a more sustainable and ecological rail transport system. This research focuses on the Milan Cadorna-Saronno railway line, examining the feasibility of installing PV panels onto train rooftops to generate power for the train’s internal consumption, including lighting and air conditioning. In addition, it is a solution to reduce the power absorbed by the train from the main supply. Simulations conducted using PVSOL software 2023 (R7) indicate that equipping a train roof with PV panels could supply up to almost 10% of the train’s auxiliary power needs, equating to over 600 MWh annually. Implementing the suggested system may also result in a decrease of more than 27 tons of CO₂ emissions per year for one train. To optimize the performance of PV systems and maximize power output, the gravitational search algorithm (GSA) as an evolutionary-based method is proposed alongside a DC/DC boost converter and its performance is compared with two other main maximum power point tracking (MPPT) methods of perturb and observe (PO), and incremental conductance (INC). The accuracy of the suggested algorithm was confirmed utilizing MATLAB SIMULINK R2023b, and the results were compared with those of the PO and INC algorithms. The findings indicate that the GSA performs better in terms of accuracy, while the PO and INC algorithms demonstrate greater robustness and dynamic response. Full article

Elevated stations are integral components of urban rail transit systems, significantly impacting passengers’ travel experience and the operational efficiency of the transportation system. However, current elevated station designs often do not sufficiently consider the structural dynamic response under various operating conditions. This oversight can limit the operational efficiency of the stations and pose potential safety hazards. Addressing this issue, this study establishes a vehicle-bridge-station spatial coupling vibration simulation model utilizing the self-developed software GSAP V1.0, focusing on integrated station-bridge and combined station-bridge elevated station designs. The simulation results are meticulously compared with field data to ensure the fidelity of the model. Analyzing the dynamic response of the station in relation to train parameters reveals significant insights. Notably, under similar travel conditions, integrated stations exhibit lower vertical acceleration in the rail-bearing layer compared to combined stations, while the vertical acceleration patterns at the platform and hall layers demonstrate contrasting behaviors. At lower speeds, the vertical acceleration at the station concourse level is comparable for both station types, yet integrated stations exhibit notably higher platform-level acceleration. Conversely, under high-speed conditions, integrated stations show increased vertical acceleration at the platform and hall levels compared to combined stations, particularly under unloaded double-line working conditions, indicating a superior dynamic performance of combined stations in complex operational scenarios. However, challenges such as increased station height due to bridge box girder maintenance, track layer waterproofing, and track girder support maintenance exist for combined stations, warranting comprehensive evaluation for station selection. Further analysis of integrated station-bridge structures reveals that adjustments in the floor slab thickness at the rail-bearing and platform levels significantly reduce dynamic responses, whereas increasing the rail beam height notably diminishes displacement responses. Conversely, alterations in the waiting hall floor slab thickness and frame column cross-sections exhibit a minimal impact on the station dynamics. Overall, optimizing structural dimensions can effectively mitigate dynamic responses, offering valuable insights for station design and operation. Full article

Over the last two decades, the United Arab Emirates (UAE) has experienced significant urban growth, prompting the Dubai Roads and Transport Authority (RTA) to advocate for sustainable transport solutions. This led to the implementation of the Light Rail Transit (LRT) to address urban mobility, environmental sustainability, and energy efficiency. Dubai has strategically prioritized infrastructure and transportation network expansion to support its rapid development. This paper aims to examine the critical role of the LRT system, particularly the metro and tramway, in steering Dubai towards sustainability. Metro and tramway systems offer crucial high-capacity public transport, enhance connectivity, stimulate economic growth, and contribute to a sustainable environment. The study assesses the transformative impact of the Dubai Metro on urban development, focusing on key stations like Jabal Ali, Al-Barsha First, and Business Bay. Using qualitative research methods, including GIS, spatial maps, interviews, case studies, and land use investigations, the research analyzes population density, connectivity, accessibility, and urban land use patterns around these stations. Results indicate a positive impact of the Dubai Metro on both commercial and residential land use, improved connectivity, and enhanced accessibility, reinforcing its role in cultivating a sustainable urban environment. Full article