To accurately predict and control environmental vibrations induced by the subway during station entry and exit, in this paper, an efficient method suitable for the acceleration and deceleration processes of subway station entry and exit for calculating wheel-rail interaction forces throughout the entire process was developed. Based on the moving time-domain Green's function method, a calculation model for wheel-rail interaction forces under the working condition of subway running at a constant speed was established. The calculation results were compared and verified with those from the frequency-domain wheel-rail force calculation method. By considering the influence of track parameter excitation, equivalent roughness samples of parameter excitation were established. Through the solution methods of moving roughness and Green's function, the time history of wheel-rail forces under variable speed operation conditions was obtained, and the spectral characteristics were analyzed. Research results show that within the frequency range of 3 - 600 Hz, the wheel-rail force spectra obtained by the two methods are basically consistent; the fluctuation range of dynamic wheel-rail forces is approximately 50 - 90 kN; the fluctuation amplitude reaches 20 kN compared with the axle load. Parameter excitation generates significant peaks at sleeper-passing frequencies (27.8, 55.6 Hz, etc.), and the equivalent roughness curve exhibits a mid-span cosine distribution. The proposed method can quickly and efficiently solve the non-stationary medium-high frequency wheel-rail interaction forces throughout the entire process of subway station entry and exit within the frequency range of primary interest for environmental vibration. In terms of computational performance, the traditional challenges of constructing excessively long track models and solving wheel-rail forces with large-scale degree-of-freedom matrices are efficiently avoided. Regarding model accuracy, the method incorporates the parameter excitation characteristics induced by the track and the nonlinear wheel-rail contact, making the model more aligned with actual engineering conditions. This approach provides reliable input for accurately predicting the environmental vibrations caused by subways entering and exiting the stations, offering references for vibration reduction design and environmental assessment in integrated construction projects.

To obtain the in-situ structural parameters of ballastless track in service, a structural parameter identification method was developed by combining the finite element model of ballastless track with the data-driven sparrow search algorithm (SSA)-convolutional neural network (CNN). The finite element model of the ballastless track was established, and frequency response functions (FRFs) under different parameters were calculated within the given parameter space to form a dataset. 70% of the data was taken as the training set, and the remaining data was used as the test set. By using the FRFs of the training set as inputs and track's structural parameters as outputs, the SSA-CNN parameter identification model was trained and verified by the test set. A hammer test on the ballastless track was carried out, and the measured FRFs were input into the parameter identification model to obtain fastener stiffness, damping, and the elastic modulus of CA mortar. The identified parameter values were substituted into the track's finite element model to calculate the vibration response under the same excitation. The calculated results were in good agreement with the test results. Research results show that when the parameter identification model is trained with a dataset containing 10% Gaussian noise, the average absolute percentage errors for identifying fastener stiffness, damping, and the elastic modulus of CA mortar are 7.90%, 1.00%, and 3.03%, respectively, verifying the reliability of the parameter identification model. The track's structural parameters can be captured accurately using the parameter identification method in this paper and the hammer test data. The vibration response of the rail is beneficial to identifying parameters or damage of fastener, while the vibration data of the slab is suitable for identifying the elastic modulus or delamination of the CA mortar layer. The developed parameter identification method for ballastless track is an effective analytical tool for detecting and assessing the service performance of interlayer connection components in ballastless tracks.

To address the issues of high energy consumption, low thermal efficiency, and incomplete snow melting in electric heating elements of turnout, No. 18 turnout of a 60 kg·m^-1 steel rail was selected as the research subject. Based on finite element analysis and physical field analysis methods of solid-fluid heat transfer, a physical model of "turnout-heating element-snow accumulation-air" was constructed. The model surface was defined as an open boundary, with the bottom defined as thermally insulated. Under identical initial conditions, the simulation results of heating methods of rail web, rail slope, and combined rail web and slope heating were compared. An optimized method involving the installation of heat-conducting plates on the sides of the slide bed was proposed. Simulation analyses were conducted under varying temperatures, wind speeds, and directions. Analysis results show that, under constant total power, a more pronounced rise in the slide bed temperature is observed when the rail slope heating method is employed, as compared with the other two heating methods. After installing heat-conducting plates, heat generated by the heating element transfers faster to the sliding bed due to their higher thermal response speed. Under the temperatures of -5 ℃ and -15 ℃ and in the absence of snow accumulation, the time required for the heating element to reach the corresponding temperature is shortened by 40% when a heat-conducting plate is used, compared with the general condition without such a plate. With an additional 20 mm of snow accumulation, a lead time of over 20% is achieved compared with the general condition. Snow-melting rate at the near end of the stock rail is initially lower than under general conditions. Later, the rate increases. As a result, the snow at the far end melts faster and more completely. Different wind directions act on different positions of the turnout and produce different effects in suppressing temperature rise. Higher wind speed makes the temperature rise more slowly. It also causes the system to reach a balance between heat absorption and heat loss more quickly. The established heat transfer model of the heat-conducting plate in the switch rail provides a theoretical basis for power optimization in different regions. It can also guide the selection of heating element power and the precise control of heating time.