In this study, a track-bridge model was proposed by combining the direct stiffness method (DSM) and the mode superposition method (MSM) for analysis of the train-track-bridge interaction. The tracks were modeled by applying DSM, which can effectively display the nonlinear contact spring and the dynamic behavior of the tracks. The bridge was modeled by applying MSM, a method that can efficiently show dynamic behavior with a small number of modes. The analysis model was built based on a previously proposed train-track analysis model. Consequently, the train, track, and bridge were modeled as a coupled system that was interconnected by nonlinear Hertzian springs between the train and track, and springs and dampers between the track and bridge. Thus, the convergence is greatly improved as a result of the stabilization of the analytical system. Using the proposed train-bridge-track interaction analysis model, the impacts of the vertical displacements of the bridge resulting from thermal load on the dyn...

The maintenance and renewal of ballasted track can be optimized in terms of time and cost if a proper statistical model of track deterioration is derived from previous maintenance history and measurement data. In this regard, quite a few models with simplified assumptions on the parameters have been suggested for the deterioration of ballasted track. Meanwhile, data driven models such as the artificial neural network (ANN) and support vector regression (SVR), which are basic ingredients of machine learning (ML) technology, were introduced in this study to better represent the deterioration phenomena of track segments so that the results can be directly plugged into the optimization schemes. For this purpose, the influential parameters of track deterioration have been selected based on the maintenance history, and two ML models have been studied to find the best combination of input parameters. Through numerical experiments, it was found that at least 2 years of maintenance data were needed in our case to obtain a stable prediction of track deterioration.

In a fixed-end arch railway bridge restraining the displacement and rotation at the support to constrain the longitudinal deformation of the superstructure, vertical deformation occurs according to temperature change. Due to such deformation, periodic change in long-wavelength track irregularity occurs, which, by increasing the vertical train body acceleration, degrades ride comfort. In the present study, the vertical deformation of a fixed-end arch railway bridge and the accompanying track irregularity changes were measured during the summer and winter, respectively. Based on the measured data, the relationships among the ambient temperature, the temperature of the bridge members, the deformation of the bridge, and the track irregularity were investigated. Additionally, the correlation between the train body acceleration and the long-wavelength track irregularity was examined, and a method of controlling long-wavelength track irregularity considering seasonal temperature change was discussed.

Although reliability centered maintenance (RCM) has been used widely in the maintenance and renewal (M&R) of railway infrastructures, M&R methods and maintenance frequencies still require considerable study to obtain information on optimum decisions. Recently, various attempts have been made to optimize the maintenance works in railway engineering, although a decision support system on selecting the maintenance or renewal works has not been suggested distinctly. Therefore, a genetic algorithm on the basis of the nondominated sorting method (NSGA) was used in this study to optimize the biobjective functions on M&R interventions and costs. The results showed that the optimum frequency of M&R could be determined using the NSGA method with various constraints. Moreover, field application of the suggested method also was considered. Future work will be concentrated on the systematic approach of the decision support model with RCM and the enterprise resource planning (ERP) system of the infrastructure manager (IM).

Dynamic deflection of a railroad sleeper works as an indicator of ballast stiffness, reflecting the health conditions of a ballast track. However, difficulty exists in measuring dynamic deflection of a railroad sleeper by conventional deflection transducers such as a linear variable differential transformer (LVDT) or a potentiometer. This is because a fixed reference point is unattainable due to ground vibrations during train passage. In this paper, a patented signal processing technique for evaluation of pseudo-deflection is presented to recover dynamic deflection of a railroad sleeper using a coupled measurement of acceleration and strain at the concrete sleeper. The presented technique combines high-frequency deflections calculated from double integration of acceleration and low-frequency deflections determined from strains. Validity of the combined deflections was shown by the deflections measured with a camera target on a concrete sleeper, captured by a high-resolution DSLR camera with superb video capturing features and processed by computer vision techniques, such as Canny edge detection and Blob analysis.

Currently, maintenance work for ballasted track is performed based on the Track Quality Index (TQI) without a fundamental cause investigation for track irregularity. In this study, a new evaluation standard for ballasted track status is proposed to overcome the limitations of current maintenance work. The new evaluation standard is based on the stiffness index. The stiffness index is defined as the difference between the average and standard deviation of the apparent track stiffness determined by a light falling weight deflectometer (LFWD). After some field tests and analysis, it is shown that the stiffness index has a strong correlation not only with TQI but also with maintenance history. In verification results, the new evaluation standard estimated the ballasted track status quite well.