To probe into the mechanical behaviour of railway transition zone from the macro-meso aspects, a numerical model of transition zone is built that hybrids the Discrete Element Method (DEM) and Finite Difference Method (FDM). The DEM is utilised to simulate the ballast bed and sleeper, because it can consider the realistic ballast shapes and complex contacts between them. The FDM based on the continuum theory is utilised to simulate the track substructure according to a real structural form. Afterwards, the coupling algorithm is used to achieve the hybrid DEM-FDM simulation. The engineering practicality of this model is validated using the dynamic responses of the transition zone from a field measurement, and the macro-meso mechanical behaviour of the transition zone is analysed with or without the wedge-shape backfill. The numerical results indicate that applying the wedge-shape backfill can considerably reduce the sudden changes of track vibration and substructure surface stresses at the vicinity of the connection between the structure and embankment. Moreover, the mesoscopic results show that the acceleration vector of the soil near the rigid structure fluctuates towards the embankment and the velocity responses of track components and substructure increase gradually when the train travels from the rigid structure to the soft embankment.@en

Understanding the dynamic behavior of a software system is one of the most important and time-consuming tasks for today’s software maintainers. In practice, understanding the inner workings of software requires studying the source code and documentation and inserting logging code in order to map high-level descriptions of the program behavior with low-level implementation, i.e., the source code. Unfortunately, for large codebases and large log les, such cognitive mapping can be quite challenging. To bridge the cognitive gap between the source code and detailed models of program behavior, we propose a fully automatic approach to present a semantic abstraction with different levels of functional granularity from full execution traces. Our approach builds multi-level abstractions and identifies frequent behaviors at each level based on a number of execution traces, and then, it labels phases within individual execution traces according to the identified major functional behaviors of the system. To validate our approach, we conducted a case study on a large-scale subject program, Javac, to demonstrate the effectiveness of the mining result. Furthermore, the results of a user study demonstrate that our approach is capable of presenting users a high-level comprehensible abstraction of execution behavior. Based on a real world subject program the participants in our user study were able to achieve a mean accuracy of 70%.@en