Search Results (35)

This study provides the design, analysis, and prototype fabrication of a remotely controlled actuation system for railcar-mounted sensors. Frequent railway inspections are essential for detecting and preventing major defects that could lead to train derailments or accidents. Integrating supplemental automated inspection systems into existing trains can aid inspection crews without interfering with standard railway operations. However, many sensors and cameras require protection during transit, motivating the need for a deployable mounting assembly. The feasibility of a deployable sensor system was successfully assessed by creating and demonstrating a functional prototype mounting assembly that can be used with future automated inspection systems. Typical loads and accelerations experienced by a train were used to design a lead screw and stepper motor system capable of working within desired tolerances. Optimized inputs controlling this motion with an Arduino Uno were found through the iterative testing of digital signals and direct port manipulation. Further research testing in a field-like environment is suggested. Full article

Construction workers’ unsafe behaviors (CWUBs) are a primary cause of construction safety accidents in railway tunnel engineering (RTE). Understanding the causation mechanism between construction safety accidents, CWUBs, and their influencing factors is crucial for improving safety management. However, research in this area remains insufficient. This study systematically identifies 9 types of construction safety accidents, 11 types of CWUBs, and 35 influencing factors, covering three core dimensions: organizational management, individual safety capacity, and safety environment. Using the 24model, this study qualitatively elucidates the causation mechanism and identifies the primary and secondary causation relationships among 55 factors. On this basis, a network model of CWUBs in RTE is developed and quantitatively analyzed using social network analysis from the perspectives of the overall network, block network, and individual network, resulting in the identification of a critical network comprising 27 key factors. Based on the findings, nine targeted intervention measures are proposed, encompassing pre-emptive prevention, on-site control, and emergency management. This study innovatively integrates the 24model and social network analysis, systematically analyzing the causation mechanism of CWUBs in RTE from both qualitative and quantitative perspectives. This research not only provides a systematic and innovative analytical framework for CWUBs in RTE, addressing a critical gap in the study of unsafe behaviors and accident causation in complex systems, but also offers practical guidance for safety risk management. Additionally, it enriches the theoretical framework of unsafe behavior research, providing valuable insights for further studies in related fields. Full article

Railroad construction sites are high-risk environments where monitoring personnel safety is critical for preventing accidents and enhancing construction efficiency. Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments. Full article

Benchmarking railway infrastructure managers (RIMs) has become a crucial tool in the context of European transport market liberalization, facilitating efficiency improvements and strategic decision-making. RIMs face challenges in increasing capacity, optimizing operations, and ensuring competitive, safe, and economically sustainable services. To address these challenges, this study proposes a hybrid benchmarking model that integrates Principal Component Analysis (PCA) to identify key performance indicators (KPIs) and reduce data dimensionality, the Grey Best–Worst Method (G-BWM) to determine KPI weight coefficients based on expert evaluations, and Assurance Region Data Envelopment Analysis (AR-DEA) to assess the relative efficiency of RIMs while incorporating real-world constraints. The research findings confirm that RIM8 is the most efficient unit, driven by high electrification levels, strong accident prevention measures, and optimal use of infrastructure. In contrast, RIM2 and RIM4 record the lowest efficiency scores, primarily due to poor safety performance, high infrastructure-related delays, and suboptimal resource utilization. By introducing weight constraints through AR-DEA, the model ensures that efficiency assessments reflect actual operational conditions, rather than relying on unrestricted weight allocations. The main contribution of this study lies in developing a systematic and objective framework for evaluating RIM efficiency, ensuring consistency and reliability in performance measurement. The practical implications extend to policy development and operational decision-making, providing insights for infrastructure managers, regulatory bodies, and policymakers to optimize resource allocation, enhance infrastructure resilience, and improve railway sector sustainability. The results highlight key efficiency factors and offer guidance for targeted improvements, reinforcing benchmarking as a valuable tool for long-term railway infrastructure management and investment planning. By offering a quantitatively grounded efficiency assessment, this model contributes to the competitiveness and sustainability of railway networks across Europe. Full article

Rail accidents have decreased in recent years, although not significantly if measured by train accidents recorded in the last six years. Therefore, it is essential to identify weaknesses in the implementation of security and prevention systems. This research aims to study the trend and classification of railway accidents, as well as analyze public databases. Using the business management method of benchmarking, descriptive statistics, and a novel approach to the Ishikawa diagram, this study demonstrates best practices and strategies to reduce accidents. Unlike previous studies, this research specifically examines public databases and provides a framework for developing the standardization of railway accident causes and recommendations. The main conclusion is that the proposed classification of railway accident causes, and its associated database, ensures that agencies, researchers, and the government have accessible, easily linkable, and usable data references to enhance their analysis and support the continued reduction of accidents. Full article

The steel–concrete composite beam, as a structural form that combines the advantages of steel and concrete, has been applied in railway engineering. However, with the increase in railway operation time, the degradation pattern of the service performance of steel–concrete composite bridges remains unclear. This paper proposes a method for calculating the long-term service performance of railway steel–concrete composite beams, considering the cumulative fatigue damage of steel beams and studs, based on the vehicle–bridge coupling theory and Miner’s linear cumulative damage criterion. The proposed method is validated using measured data from an in-service steel–concrete composite railway bridge with spans of 40 + 50 + 40 m. The calculated mid-span vertical displacement and the first two natural frequencies of the composite beam deviated from the measured results by only 2.1%, 7.7%, and 9.5%, respectively. The research results can provide a basis for extending the service life of composite beams and preventing the occurrence of safety accidents. Full article

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions. Full article

Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models. Full article

In recent years, railway safety accidents have repeatedly occurred. Any omission in the process of management or operation can easily have very serious consequences. This study aimed to examine the causes and transmission mechanisms of safety risks in railway engineering departments. First, the multi-objective particle swarm optimization algorithm was employed to determine the key risk factors, allowing for indicator screening that was in line with the requirements of practical applications. Then, Bayesian networks were used, and their structure was optimized to analyze the propagation diagnosis and probability of key risk indicators, obtaining the causal logic chain that produces accidents and, from that, the four aspects (human, machine, environment, management) of the corresponding prevention of risk recommendations. Finally, in this article, it is shown that combining the indicators and Bayesian networks can improve the accuracy of risk prediction and provide more accurate results than using existing research and, hence, it can fill the gap in research on railway safety risks in risk transmission mechanisms. Full article

The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle’s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D–CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate. Full article

The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways. Full article

Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas. Full article

Monitoring the surface subsidence in mining areas is conducive to the prevention and control of geological disasters, and the prediction and early warning of accidents. Hunan Province is located in South China. The mineral resource reserves are abundant; however, large and medium-sized mines account for a low proportion of the total, and the concentration of mineral resource distribution is low, meaning that traditional mining monitoring struggles to meet the needs of large-scale monitoring of mining areas in the province. The advantages of Interferometric Synthetic Aperture Radar (InSAR) technology in large-scale deformation monitoring were applied to identify and monitor the surface subsidence of coal mining fields in Hunan Province based on a Sentinel-1A dataset of 86 images taken from 2018 to 2020, and the process of developing surface subsidence was inverted by selecting typical mining areas. The results show that there are 14 places of surface subsidence in the study area, and accidents have occurred in 2 mining areas. In addition, the railway passing through the mining area of Zhouyuan Mountain is affected by the surface subsidence, presenting a potential safety hazard. Full article

Railway trespassing is a growing problem in both rail and road transport. A high percentage of rail accidents are a result of the former. Factors that contribute to trespassing accidents range from poor decision-making by the trespasser and general ignorance of rail traffic rules to poor infrastructure (e.g., a lack of fences along tracks to prevent trespassing). The objective of this study was to provide a systematic review of the known literature on the problem of trespassing on railway tracks. The methodology implemented for literature collection was in accordance with the PRISMA method. The literature was searched using keywords: railway trespassing, railway trespassing accidents, trespassing factors, trespassing prevention, railway trespassing detection, and railway trespassing education in the Web of Science Core Collection and an additional search was conducted through other literature databases. The starting point was the collection of n = 291 studies of which a total of 72 publications were included in the literature review ranging between 1953–2023. The literature review consisted of 73.6% journal papers, 18.1% conference papers, and 8.3% expert reports. The results were the formation of: (1) Factors that influence the occurrence of trespassing accidents: (a) locations of frequent railway trespassing, (b) the temporal frequency of railway trespassing, (c) trespasser profile and behaviour, (d) motivation for and general knowledge of railway trespassing, and (e) other factors and models for railway trespassing accidents; (2) Measures for trespassing prevention: (a) education measures, (b) signalization, technological and infrastructure measures for trespassing prevention, and (c) pilot studies of railway trespassing preventive measures. The main findings were summarised and discussed with considerations for future work. Full article

Highway railway level crossings, also widely recognized as HRLCs, present a significant threat to the safety of everyone who uses a roadway, including pedestrians who are attempting to cross an HRLC. More studies with new, proposed solutions are needed due to the global rise in HRLC accidents. Research is required to comprehend driver behaviours, user perceptions, and potential conflicts at level crossings, as well as for the accomplishment of preventative measures. The purpose of this study is to conduct an in-depth investigation of the HRLCs involved in accidents that are located in the northern zone of the Indian railway system. The accident information maintained by the distinct divisional and zonal offices in the northern railways of India is used for this study. The accident data revealed that at least 225 crossings experienced at least one incident between 2006 and 2021. In this study, the logistic regression and multilayer perception (MLP) methods are used to develop an accident prediction model, with the assistance of various factors from the incidents at HRLCs. Both the models were compared with each other, and it was discovered that MLP supplied the best results for accident predictions compared to the logistic regression method. According to the sensitivity analysis, the relative importance of train speed is the most important, and weekday traffic is the least important. Full article