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With the extensive use of structural health monitoring technologies, vibration-based structural damage detection becomes a crucial task in both academic and industrial communities. Following the noteworthy trends of data-driven paradigms in recent years, some solutions have been released to identify, localize, and classify damages via deep neural networks. However, some deficiencies still exist for effective damage-intensive feature extraction and representation. To overcome such a problem, this paper proposes a novel end-to-end structural damage detection neural model by taking the advantages of the Convolutional Neural Network and Bidirectional Gated Recurrent Unit in parallel. The well-known IASC-ASCE benchmark and TCRF dataset are used for evaluation. The experimental results show that the proposed approach can achieve a better detecting effect than other existing manners.

As an extremely important part of traffic arteries, bridge structure plays an essential role in national economic construction, social development and smart city. Thus the monitoring of the bridge structure health are increasingly concerned by the bridge industry scholars and engineering people at home and aboard. In this paper, we propose a deep learning framework to evaluate the safety of the bridge structural state. More specifically, the proposed system generates a learnable transformation which attempts to map most of the data network representations into a hypersphere characterized of minimum volume. During inference, mappings of normal examples fall within the learned hypersphere, whereas mappings of anomalies fall outside the hypersphere. The whole system is end-to-end trainable and outperforms other advanced methods in real-world dataset.

A 2D depth-averaged model for hydrodynamic sediment transport and river morphological adjustment was established. The sediment transport submodel takes into account the influence of non-uniform sediment with bed surface armoring and considers the impact of secondary flow in the direction of bed-load transport and transverse slope of the river bed. The bank erosion submodel incorporates a simple simulation method for updating bank geometry during either degradational or aggradational bed evolution. Comparison of the results obtained by the extended model with experimental and field data, and numerical predictions validate that the proposed model can simulate grain sorting in river bends and duplicate the characteristics of meandering river and its development. The results illustrate that by using its control factors, the improved numerical model can be applied to simulate channel evolution under different scenarios and improve understanding of patterning processes.

Slowing down or even stopping light is the first task to realising optical information transmission and storage. Theoretical studies have revealed that metamaterials can slow down or even stop light; however, the difficulty of preparing metamaterials that operate in visible light hinders progress in the research of slowing or stopping light. Metasurfaces provide a new opportunity to make progress in such research. In this paper, we propose a dendritic cell cluster metasurface consisting of dendritic structures. The simulation results show that dendritic structure can realise abnormal reflection and refraction effects. Single- and double-layer dendritic metasurfaces that respond in visible light were prepared by electrochemical deposition. Abnormal Goos-Hänchen (GH) shifts were experimentally obtained. The rainbow trapping effect was observed in a waveguide constructed using the dendritic metasurface sample. The incident white light was separated into seven colours ranging from blue to red light. The measured transmission energy in the waveguide showed that the energy escaping from the waveguide was zero at the resonant frequency of the sample under a certain amount of incident light. The proposed metasurface has a simple preparation process, functions in visible light, and can be readily extended to the infrared band and communication wavelengths.