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.

Reliability based analysis of torsional divergence of long span suspension bridges.

Abstract: A systematic reliability evaluation approach for torsional divergence analysis of long span suspension bridges is proposed, consisting of the first order reliability method and a simplified torsional divergence analysis method. The proposed method was implemented in the deterministic torsional divergence analysis program SIMTDB through a new strategy involving interfacing the proposed method with SIMTDB via a freely available MATLAB software tool (FERUM). A numerical example involving a detailed computational model of a long span suspension bridge with a main span of 888 m is presented to demonstrate the applicability and merits of the proposed method and the associated software strategy. Finally, the most influential random variables on the reliability of long span suspension bridges against torsional divergence failure are identified by a sensitivity analysis.

Closure to “Equivalent Static Wind Loads on Long-Span Roof Structures” by J. Fu, Z. Xie, and Q. S. Li

Abstract: The authors addressed the determination of equivalent static wind loads ESWLs on long-span roof structures based on wind pressure data. The authors have asserted that a new description of the ESWLs was presented that differed from the existing methods. The paper focuses on the presentation of their methodology and demonstrates its effectiveness using an example of a roof structure. The discussers have noted many similarities between this paper and their papers on the subject Chen and Kareem 2001, 2004 , which have been cited in the paper. These issues are detailed in the following: • Regarding the methodology, the discussers are of the opinion that the development and the modeling of ESWLs described in this paper are the same as presented in Chen and Kareem 2001 . The discussers would like the authors to identify specific parts of the methodology that are unique or different from the procedure described in Chen and Kareem 2001 . • The authors mentioned that the method presented by Chen and Kareem 2001, 2004 has a limitation: “However, such a method requires separating the response and associate wind load into background and resonant components” p. 1116 . In fact, Chen and Kareem 2001 had emphasized in their paper:

I and i

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 …

Nonlinear Time Series Analysis.

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)