Abstract
In railway engineering, the load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. The LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. The “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application. Besides, there is currently a lack of LSR prediction methods considering the impact of multiple factors. Therefore, this paper proposes a “moving loading method” for investigating the LSR under moving train excitation, verified to be rational by comparing with the experimental results. At the same time, a procedure for establishing the LSR multi-factor prediction model is put forward, namely, we (1) determine the LSR function form and the fitting algorithm; (2) perform parameter sensitivity analysis to determine the main influencing parameters of the LSR function; and (3) design a quadratic regression orthogonal test to obtain the prediction formula of the LSR function coefficients. Once establishing the prediction model for a type of train-track system, the LSR of similar systems can be calculated by adjusting the main parameters of the model. Shijiazhuang Metro Line 1 using the A-type vehicle and the monolithic trackbed is taken as a case study to develop a corresponding LSR multi-factor prediction model by the moving loading method and the procedure mentioned above. The results indicate that the proposed method performs well and can be adopted to enhance the accuracy of track design or tunnel and environmental vibration prediction.
Highlights
The load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. e LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. e “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application
Shijiazhuang Metro Line 1 using the A-type vehicle and the monolithic trackbed is taken as a case study to develop a corresponding LSR multi-factor prediction model by the moving loading method and the procedure mentioned above. e results indicate that the proposed method performs well and can be adopted to enhance the accuracy of track design or tunnel and environmental vibration prediction
Introduction e rail seat load (RSL) is the load transferred from the rail to the underneath slabs via fastenings, rail seat plates, and sleepers. e load sharing ratio (LSR) is the ratio of the RSL to the axle load, reflecting the axle-load transmission law among wheelsets, rail, and fastenings. e maximum RSL, acting as the main parameter in the design and construction of railway tracks [1], needs to be calculated from the maximum axle load and the LSR
Summary
The load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. e LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. e “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application. Recently increasing investigations have been devoted to reducing the model scale (for example, using 2.5D numerical methods) of the dynamic response prediction of railway tunnels [2] and the environment [3,4,5,6,7] These models still include rail and fastenings, partly repeating with train-track coupling models. Erefore, as shown, the LSR of a single fastening time history includes all the static loading method results, and the complete LSR curve is obtained only by transforming the x-axis from time to D. To investigate the effect of the moving loading method, a calculation is carried out using the static loading model test parameters of the literature [28], including the train speed v 100 km/h , the fastening stiffness k 28 · 5 MN/m , and the fastening spacing a 0.63 m. Regarding the coefficients A and B as target parameters, equation (2) is transformed into the following form: SAj
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