An improved spatial vehicle–track–bridge interaction system considering the unsupported track is proposed using the virtual work principle. When the track fails to be supported due to the defects under the track, the corresponding bridge–rail connection coupled matrix should be removed. Using the proposed dynamic model, a novel numerical analysis of the unilateral and bilateral unsupported sleepers in the bridge and the subgrade is carried out. The results indicate that the wheel–rail contact force changes dramatically when the vehicles pass through the unsupported track. The unsupported track has a little effect on the displacement of the bridge, but it increases the acceleration of the bridge. The displacement and acceleration of the track increase significantly with the expansion of the supported defects under the track and the increase of driving speed. For unilateral unsupported sleepers, the dynamic response on the unsupported side is slightly smaller than that of the bilateral unsupported sleeper, while the unsupported side has a limited effect on the other side of the normal support.

This study proposed a separation method to identify the temperature-induced response from the long-term monitoring data with noise and other action-induced effects. In the proposed method, the original measured data are transformed using the local outlier factor (LOF), and the threshold of the LOF is determined by minimizing the variance of the modified data. The Savitzky–Golay convolution smoothing is also utilized to filter the noise of the modified data. Furthermore, this study proposes an optimization algorithm, namely the AOHHO, which hybridizes the Aquila Optimizer (AO) and the Harris Hawks Optimization (HHO) to identify the optimal value of the threshold of the LOF. The AOHHO employs the exploration ability of the AO and the exploitation ability of the HHO. Four benchmark functions illustrate that the proposed AOHHO owns a stronger search ability than the other four metaheuristic algorithms. A numerical example and in situ measured data are utilized to evaluate the performances of the proposed separation method. The results show that the separation accuracy of the proposed method is better than the wavelet-based method and is based on machine learning methods in different time windows. The maximum separation errors of the two methods are about 2.2 times and 5.1 times that of the proposed method, respectively.

A new damage identification method is proposed to solve the problem of no correspondence between the element division form of the finite element model and the actual damage location. The three basic operators in the traditional genetic algorithm are improved, and the catastrophe and neighborhood search processes are introduced to enhance the local optimization ability of the algorithm. The train–rail–bridge coupling time-varying equation is established. Based on the dynamic response of the bridge under trainload, the damage index is constructed, and the corresponding objective function is given. Through a numerical example, the stability and convergence rate of the algorithm are statistically analyzed. The effects of noise, the number of measuring points, and train speed on the recognition results are discussed. The research results indicate that, even if the damage location is different from the element division form of the finite element model, this method can accurately locate the damage location, but it will affect the quantitative results to a certain extent. In addition, the convergence speed of this method is fast, and the computing efficiency is about 6.7 times that of the conventional one-time recognition method.