Search

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.

The coherence properties of InAs quantum dot emissions in quasi-L2 photonic crystal nanocavities were studied using g ( 1 ) ( τ ) first-order correlation measurements at room temperature. A soft turn-on of the coherent state at ultra-low threshold power indicated that the quantum dot emissions in the cavities had exhibited a smooth transition from spontaneous to stimulated emission.