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- 저자 : Zhiyao Liang (검색결과 4 건)
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Rational analysis model and seismic behaviour of tall bridge piers
Jianzhong Li,Zhongguo Guan,Zhiyao Liang 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.51 No.1
This study focuses on seismic behaviour of tall piers characterized by high slender ratio. Two analysis models were developed based on elastic-plastic hinged beam element and elastic-plastic fiber beam element, respectively. The effect of the division density of elastic-plastic hinged beam element on seismicdemand was discussed firstly to seek a rational analysis model for tall piers. Then structural seismicbehaviour such as the formation of plastic hinges, the development of plastic zone, and the displacement at the top of the tall piers were investigated through incremental dynamic analysis. It showed that the seismic behaviour of a tall pier was quite different from that of a lower pier due to higher modes contributions. In a tall pier, an additional plastic zone may occur at the middle height of the pier with the increase of seismicexcitation. Moreover, the maximum curvature reaction at the bottom section and maximum lateral displacement at the top turned out to be seriously out of phase for a tall pier due to the higher modes effect, and thus pushover analysis can not appropriately predict the local displacement capacity.
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Rational analysis model and seismic behaviour of tall bridge piers
Li, Jianzhong,Guan, Zhongguo,Liang, Zhiyao Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.51 No.1
This study focuses on seismic behaviour of tall piers characterized by high slender ratio. Two analysis models were developed based on elastic-plastic hinged beam element and elastic-plastic fiber beam element, respectively. The effect of the division density of elastic-plastic hinged beam element on seismic demand was discussed firstly to seek a rational analysis model for tall piers. Then structural seismic behaviour such as the formation of plastic hinges, the development of plastic zone, and the displacement at the top of the tall piers were investigated through incremental dynamic analysis. It showed that the seismic behaviour of a tall pier was quite different from that of a lower pier due to higher modes contributions. In a tall pier, an additional plastic zone may occur at the middle height of the pier with the increase of seismic excitation. Moreover, the maximum curvature reaction at the bottom section and maximum lateral displacement at the top turned out to be seriously out of phase for a tall pier due to the higher modes effect, and thus pushover analysis can not appropriately predict the local displacement capacity.
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Luo, Long,Zhang, Liang,Duan, Zhiyao,Lapp, Aliya S.,Henkelman, Graeme,Crooks, Richard M. American Chemical Society 2016 ACS NANO Vol.10 No.9
<P>In this paper, we show that the onset potential for CO oxidation electrocatalyzed by ∼2 nm dendrimer-encapsulated Pt nanoparticles (Pt DENs) is shifted negative by ∼300 mV in the presence of a small percentage (<2%) of Cu surface atoms. Theory and experiments suggest that the catalytic enhancement arises from a cocatalytic Langmuir–Hinshelwood mechanism in which the small number of Cu atoms selectively adsorb OH, thereby facilitating reaction with CO adsorbed to the dominant Pt surface. Theory suggests that these Cu atoms are present primarily on the (100) facets of the Pt DENs.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancac3/2016/ancac3.2016.10.issue-9/acsnano.6b04448/production/images/medium/nn-2016-04448k_0008.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/nn6b04448'>ACS Electronic Supporting Info</A></P>
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LIU QIAN,Zhang Zhiyao,GUO PENG,WANG YIFAN,Liang Junxin 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1
Predicting the remaining useful life (RUL) of the aircraft engine based on historical data plays a pivotal role in formulating maintenance strategies and mitigating the risk of critical failures. None the less, attaining precise RUL predictions often encounters challenges due to the scarcity of historical condition monitoring data. This paper introduces a multiscale deep transfer learning framework via integrating domain adaptation principles. The framework encompasses three integral components: a feature extraction module, an encoding module, and an RUL prediction module. During pre-training phase, the framework leverages a multiscale convolutional neural network to extract distinctive features from data across varying scales. The ensuing parameter transfer adopts a domain adaptation strategy centered around maximum mean discrepancy. This method efficiently facilitates the acquisition of domain-invariant features from the source and target domains. The refined domain adaptation Transformer-based multiscale convolutional neural network model exhibits enhanced suitability for predicting RUL in the target domain under the condition of limited samples. Experiments on the C-MAPSS dataset have shown that the proposed method significantly outperforms state-of-the-art methods.
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