http://hdl.handle.net/20.500.14044/33794 Sun, 26 Jul 2026 08:42:06 GMT 2026-07-26T08:42:06Z http://hdl.handle.net/20.500.14044/32838 A Fuzzy Framework for Assessing and Prioritizing Railway Infrastructure Retrofitting Against Seismic Hazards – A Case Study Hajimirzajan, Amir; Kazemian, Milad; Fischer, Szabolcs The railway system plays a crucial role in a nation's economy and society, extending beyond mere transportation. In earthquake-prone regions like Razavi Khorasan in Iran, railway infrastructure is highly vulnerable to natural disasters, which can severely disrupt train operations. Ensuring the safety of critical infrastructure, including stations, bridges, tunnels, and railway lines, is essential for maintaining operational integrity and public safety. This study evaluates and prioritizes seismic retrofitting measures for railway infrastructures in Razavi Khorasan. The fuzzy Delphi method is used to gather expert opinions, while the Fuzzy VIKOR method facilitates the prioritization process. Key assessment criteria include seismic intensity potential, vulnerability potential of the zone in terms of distance from the fault, the degree of criticality of the infrastructure in terms of the possibility of continuing transportation operations and the current state of the infrastructure in terms of the state of retrofitting against seismic hazards. The findings reveal critical railway segments that require immediate retrofitting interventions and highlight overall vulnerabilities within the system. This paper underscores the effective application of fuzzy logic methodologies in complex decision making scenarios, offering actionable recommendations to enhance the seismic retrofitting of railway infrastructures. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/32838 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/32832 Influence of Rail Surface Geometry on Wear and Service Life of Crossing Rails Kanteewong, Suwitcha Malfunctioning components on railroads can lead to the decrease of efficiencies. Switch frog is costly, but has a short service life. However, for safety reasons, regular inspection is necessary. Determining the service life helps to plan maintenance, as well as renewal of the switch frog, in an easy and timely manner. This study aims to determine the geometry model and the influence of it’s relevant parameters, for predicting the service life of switch frogs, based on the measured data of 13 switch frogs. The data were provided by DB Systemtechnik GmbH, Germany. Using the relationship between contact pressure and vertical wear area the model will be performed. In this study, the geometry of the wing rail is also considered, because it affects the change in the geometry of the frog tip. The deviation between the measurement data and the model could be minimized by calibrating the model results with the measured data in order to achieve higher accuracy. As an outcome, the service life of switch frogs will be determined, and the optimal initial geometry identified. In future studies, the model needs to be modified, to predict the remaining service life, for the case of any non-initial geometry. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/32832 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/32039 A Preprocessing Method to Improve Edge Crack Detection from Railway Tunnel Lining Images Zhang, Tiantao; Lv, Chengshun; Liu, Jian; Kou, Lei; Xie, Quanyi; Duan, Meidong; Zhang, Xiao; Zhu, Debao Crack detection is critical for guaranteeing the safety of bridges, railways, and other infrastructures; however, it is a difficult task, particularly for tunnels. Tunnel lining images are primarily acquired using vision sensors, and cracks typically appear throughout an entire image. For crack detection using convolutional neural networks, the recognition accuracy is unsatisfactory when the cracks are at the edge of the image. Hence, an image preprocessing method is proposed to process railway tunnel data. In this method, the relative position of cracks in an image is changed by adding different sizes of borders to the crack images, and four different detection models are used for training to examine the effectiveness of the preprocessing method. Experimental results show that the proposed preprocessing method achieves better detection results for all four models. In the custom dataset, the border size is set to 1/9 of the original image size, which is the most effective size for improving edge crack recognition, where a maximum improvement of 8.4% compared with the control group is achieved. Additionally, black pixels (pixel value 0) are used to fill the border, which is better than using white pixels (pixel value 255). Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/32039 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/32038 Estimation of Vertical Forces and Track Degradation Considering, Wheel-Rail Interaction, Railway Traffic Severity and Seasonal Conditions Matijošius, Jonas; Kilikevičius, Artūras; Vaičiūnas, Gediminas; Bureika, Gintautas; Steišūnas, Stasys; Marinkovic, Dragan Investigation of train speed, axle load and environmental conditions impact on the vertical forces that are exerted on railway tracks is an actual railway track condition monitoring issue. Authors studied this problem by focusing on the dynamic interaction between the wheels and rails. The researchers considered how seasonal temperature variations, track irregularities and rail vehicle weight influence force distribution, potentially leading to the rail degradation and safety risks. Using both experimental data and advanced diagnostic tools, such as optical sensors and correlation analysis, measured vertical forces across varying conditions, including different speeds (30-80 km/h) and axle loads, under summer and winter conditions, are made. The summer time results revealed that higher train speeds, especially with loaded wagons, caused significant increases in dynamic vertical forces (up to160 kN). In winter, the snow and ice accumulation on rails contribute to track irregularities and noticeably influence the force variability, particularly at higher speeds. This comprehensive analysis provides valuable insights for optimizing railway infrastructure management, particularly in adjusting for seasonal and train operational circumstances. It is concluded that advanced monitoring of vertical forces and rail conditions, along with purposive maintenance strategies, is essential for reducing rail track degradation and ensuring essential safety train operations. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/20.500.14044/32038 2025-01-01T00:00:00Z