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Qiusheng Li

Bio: Qiusheng Li is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Wind speed & Wind tunnel. The author has an hindex of 47, co-authored 429 publications receiving 8830 citations. Previous affiliations of Qiusheng Li include Chinese Ministry of Education & Guangzhou University.


Papers
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TL;DR: The necessary conditions for the occurrence of 2D coupled galloping oscillations and a description of the set of general differential equations for 2D coupling galloping vibrations are established in this article.

24 citations

Journal ArticleDOI
TL;DR: In this article, a parallel fluid-structure interaction method based on socket parallel architecture was established and combined with the methods and models of large eddy simulation developed by authors recently, the results obtained show that the proposed method and models is capable of performing high-Reynolds number LES and high-efficiency two-way coupling between detailed fluid dynamics computing and solid structure dynamics computing so that the detailed wind induced responses for high-rise buildings can be resolved practically.
Abstract: With more and more high-rise building being constructed in recent decades, bluff body flow with high Reynolds number and large scale dimensions has become an important topic in theoretical researches and engineering applications. In view of mechanics, the key problems in such flow are high Reynolds number turbulence and fluid-solid interaction. Aiming at such problems, a parallel fluid-structure interaction method based on socket parallel architecture was established and combined with the methods and models of large eddy simulation developed by authors recently. The new method is validated by the full two-way FSI simulations of 1:375 CAARC building model with Re = 70000 and a full scale Taipei101 high-rise building with Re = 1e8, The results obtained show that the proposed method and models is potential to perform high-Reynolds number LES and high-efficiency two-way coupling between detailed fluid dynamics computing and solid structure dynamics computing so that the detailed wind induced responses for high-rise buildings can be resolved practically.

23 citations

Journal ArticleDOI
TL;DR: In this paper, an efficient and accurate algorithm is proposed to evaluate the reliability of long span steel arch bridges against wind-induced stability failure during construction, which is developed based on stochastic finite-element method.

23 citations

Journal ArticleDOI
Qiusheng Li1, Yuncheng He1, Yinghou He1, Kang Zhou1, Xuliang Han1 
TL;DR: In this article, the atmospheric boundary layer and wind effects on a 600 m high skyscraper during a landfall typhoon are presented and discussed based on records from a Doppler radar wind profiler and near-ground measurements at several meteorological stations.
Abstract: This article presents the observations of the atmospheric boundary layer (ABL) and wind effects on a 600 m high skyscraper during a landfall typhoon. Wind structure and characteristics throughout the entire ABL are presented and discussed based on records from a Doppler radar wind profiler and near-ground measurements at several meteorological stations. Wind speed profiles with the feature of low-level jets and radial-distance and exposure dependences of gradient height are stressed. Afterwards, wind-induced pressures on building surfaces of the skyscraper are investigated with highlights on non-Gaussian probability distributions of negative pressures and vortex shedding occurred alternately at two sides of the building. The structural responses measured by both accelerometers and strain gauges are analyzed subsequently. Modal parameters of the skyscraper and their dependence on response amplitude are presented and discussed. This study aims to provide useful information for the wind-resistant des...

23 citations

Journal ArticleDOI
TL;DR: It is shown through three numerical examples that the proposed method provides accurate and computationally efficient estimates of the solutions of structural optimization problems.
Abstract: This paper presents a new hybrid artificial neural network (ANN) method for structural optimization. The method involves the selection of training datasets for establishing an ANN model by uniform design method, approximation of the objective or constraint functions by the trained ANN model and yields solutions of structural optimization problems using the sequential quadratic programming method (SQP). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the objective or constraint functions and then provides an accurate estimation of the optimum solution. It is shown through three numerical examples that the proposed method provides accurate and computationally efficient estimates of the solutions of structural optimization problems.

23 citations


Cited by
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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Book ChapterDOI
11 Dec 2012

1,704 citations

Journal ArticleDOI

1,604 citations

01 Mar 1995
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

1,545 citations