The rated suspension gap of a low-to-medium-speed maglev train with electromagnetic suspension is normally 8-10 mm. However, while either passing over a bridge or being stationary on one, the maglev train deforms the bridge and therefore alters the suspension gap. Hence, a problem arises due to coupled vibrations between the maglev train and its supporting bridge. In the study reported here, field experiments were conducted on the Chinese Changsha maglev line, which was the first commercial low-to-medium-speed maglev line in China. The focus is on two types of prestressed double-track concrete bridges on the maglev line. One is a simply supported girder with a span of 25 m, while the other is a continuous girder designed as 25 þ 35 þ 25 m. The accelerations and vertical dynamic deflections of the two bridges at midspan were measured while a five-module low-to-medium-speed maglev train with electromagnetic suspension either passed over or was stationary on either bridge, as were the accelerations of the car body and the suspension frame. The basic dynamic characteristics of the two bridges are analysed and compared with those of bridges in various typical maglev lines. The vibration characteristics of the two bridges, the car body and the suspension frame are studied in the time and frequency domains for the maglev train running at normal speeds, low speeds and when stationary. The influences of the speed on the dynamic characteristics are discussed. Some comparisons with other studies are also carried out, including the effects of bridge parameters on the coupled vibrations and the running stability of a low-to-medium-speed maglev and a CRH2C wheel/rail train. Significant conclusions are drawn from the analysis: increasing the rigidity and mass of the bridge can significantly reduce its vibration; increasing the span and deflection of the bridge increases the vibrations of the car body and the suspension frame; the dynamics of the maglev vehicle and bridges are different when the maglev train runs at normal speeds (more than 30 km/h), low speeds (less than 30 km/h) and when being stationary. The running stability of a low-to-medium-speed maglev train is better than that of a CRH2C high-speed train. The present study provides a test basis for further research on the mechanisms for coupled vibrations of maglev train-bridge systems.

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Over the last decades, the resonance-related dynamics for bridge systems subjected to a moving train has been researched and discussed from mechanics, physics and mathematics. In the current work, new perspectives of train-induced resonance analysis are investigated through introducing random propagation process into the train–bridge dynamic interactions. Besides, the Nataf-transformation-based point estimation method is applied to generate pseudorandom variables following arbitrarily correlated probability distributions. A three-dimensional (3D) nonlinear train-ballasted track–bridge interaction model founded on fundamental physical and mechanical principles is employed to convey and depict train–bridge interactions with random properties considered. After that, extensive applications are illustrated in detail for revealing the statistical characteristics of the so-called “random resonance”. Numerical results show that the critical train speeds associated with resonance and cancelation are random in essence owing to the variability of system parameters; the correlation between parameters exerts obvious influences on system dynamic behaviors; the last vehicle of a train will be in more violent vibrations compared to the front vehicles; the influences of track irregularities on the wheel–rail interactions are significantly greater than those of resonance.

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Session-based recommendations have been widely adopted for various online video and E-commerce Websites. Most existing approaches are intuitively proposed to discover underlying interests or preferences out of the anonymous session data. This apparently ignores the fact these sequential behaviors usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session is challenging. To address aforementioned issue, this paper proposes a demand-aware graph neural networks (DAGNN). Particularly, a demand modeling component is designed to first extract session demand and the underlying multiple demands of each session is estimated using the global demand matrix. Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demandaware item embedddings for the later recommendations. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments are evaluated on several real-world datasets and the proposed model achieves the SOTA model performance.

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