Linear guided wave based methods have been proposed to measure the axial load of continuously welded rail (CWR) in service. The underlying principle is that the propagation velocities of excited guided waves are sensitive to the axial load. However, the in-service CWR inevitably faces changes in rail wear and temperature, which also affects the propagating guided waves and results in severe degradation of existing methods. In this paper, we proposed the IGA-IWLS algorithm to estimate the axial load of in-service CWR using multiple guided wave modes. This novel load estimation method takes rail profile and phase velocities of a small set of wave modes as input, then uses an improved genetic algorithm to roughly search the candidate solutions of axial load and Young's modulus, and finally employs weighted least squares algorithm to iteratively converge to the estimated value of axial load. The paper presents the estimation theory in detail, including selection of the optimal set of guided wave modes and the IGA-IWLS algorithm. Numerical experiments show that the proposed method is able to estimate the axial load of CWR with an accuracy less than 2 MPa and is robust to measurement error and model error.INDEX TERMS Axial load estimation, rail wear, semi-analytical finite element, ultrasonic guided wave, continuously welded rail.
Continuous Welded Rail (CWR) is widely used in modern railways. With the absence of the expansion joints, CWR cannot expansion freely when the temperature changes, which could cause buckling in hot weather or breakage in cold weather. Therefore, rail thermal stress measuring system plays an important role in the safe operation of railways. This paper designed a thermal stress measurement system based on the acoustoelastic effect of the ultrasonic guided wave. A large-scale rail testbed was built to simulate the thermal stress in the rail track, and to establish the relationship of time-delay of guided wave and thermal stress. After laboratory testing, the system was installed in several railway lines in China for field tests. The results showed that the system was stable and accurate in stress measurement. The performance and potentials of the system were discussed.
Non-destructive rail testing and evaluation based on guided waves need accurate information about the mode propagation characteristics, which can be obtained numerically with the exact material properties of the rails. However, for rails in service, it is difficult to accurately obtain their material properties due to temperature fluctuation, material degradation and rail profile changes caused by wear and grinding. In this study, an inverse method is proposed to identify the material elastic constants of in-service rails by minimizing the discrepancy between the phase velocities predicted by a semi-analytical finite element model and those measured using array transducers attached to the rail. By selecting guided wave modes that are sensitive to moduli but not to rail profile changes, the proposed method can make stable estimations for worn rails. Numerical experiments using a three-dimensional finite element model in ABAQUS/Explicit demonstrate that reconstruction accuracies of 0.36% for Young’s modulus and 0.87% for shear modulus can be achieved.
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