Dependability assessment of large railway systems

2013 ◽  
pp. 2989-2995
Author(s):  
G Buchheit ◽  
O Malassé ◽  
N Brinzei ◽  
N Ammad
Keyword(s):  
Railway Systems

Case study: Prediction and field tests of railway noise and effects of a low-height noise barrier

2020 ◽  
Vol 68 (4) ◽  
pp. 303-314
Author(s):  
Yuna Park ◽  
Hyo-In Koh ◽  
University of Science and Technology, Transpo ◽  
University of Science and Technology, Transpo ◽  
University of Science and Technology, Transpo ◽  
...  
Keyword(s):  
High Speed ◽  
Field Tests ◽  
Residential Areas ◽  
Railway Systems ◽  
Railway Noise ◽  
Sound Levels ◽  
Noise Barrier ◽  
The Difference ◽  
Reduction Effect ◽  
The Impact

Railway noise is calculated to predict the impact of new or reconstructed railway tracks on nearby residential areas. The results are used to prepare adequate counter- measures, and the calculation results are directly related to the cost of the action plans. The calculated values were used to produce noise maps for each area of inter- est. The Schall 03 2012 is one of the most frequently used methods for the production of noise maps. The latest version was released in 2012 and uses various input para- meters associated with the latest rail vehicles and track systems in Germany. This version has not been sufficiently used in South Korea, and there is a lack of standard guidelines and a precise manual for Korean railway systems. Thus, it is not clear what input parameters will match specific local cases. This study investigates the modeling procedure for Korean railway systems and the differences between calcu- lated railway sound levels and measured values obtained using the Schall 03 2012 model. Depending on the location of sound receivers, the difference between the cal- culated and measured values was within approximately 4 dB for various train types. In the case of high-speed trains, the value was approximately 7 dB. A noise-reducing measure was also modeled. The noise reduction effect of a low-height noise barrier system was predicted and evaluated for operating railway sites within the frame- work of a national research project in Korea. The comparison of calculated and measured values showed differences within 2.5 dB.

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

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