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.

Modelling of structural response and optimization of structural control system using neural network and genetic algorithm

Abstract: This paper proposes an integrated approach to the modelling and optimization of structural control systems in tall buildings. In this approach, an artificial neural network is applied to model the structural dynamic responses of tall buildings subjected to strong earthquakes, and a genetic algorithm is used to optimize the design problem of structural control systems, which constitutes a mixed-discrete, nonlinear and multi-modal optimization problem. The neural network model of the structural dynamic response analysis is included in the genetic algorithm and is used as a module of the structural analysis to estimate the dynamic responses of tall buildings. A numerical example is presented in which the general regression neural network is used to model the structural response analysis. The modelling method, procedure and the numerical results are discussed. Two Los Angeles earthquake records are adopted as earthquake excitations. Copyright © 2000 John Wiley & Sons, Ltd.

Experimental investigation of characteristics of torsional wind loads on rectangular tall buildings

Abstract: In order to investigate the characteristics of torsional wind loads on rectangular tall buildings, five models with different rectangular cross-sections were tested in a boundary wind tunnel. Based on the test results, the RMS force coefficients, power spectrum densities as well as vertical correlation functions of torsional wind loads were analyzed. Formulas that took the side ratio as parameters were proposed to fit the test results above. Comparisons between the results calculated by the formulas and the wind tunnel measurements were made to verify the reliability of the proposed formulas. An simplified expression to evaluate the dynamic torsional wind loads on rectangular tall buildings in urban terrain is presented on basis of the above formulas and has been proved by a practical project. The simplified expressions as well as the proposed formulas can be applied to estimate wind-induce torsional response on rectangular tall buildings in the frequency domain.

Evaluation of the lever-type multiple tuned mass dampers for mitigating harmonically forced vibration

Abstract: The lever-type multiple tuned mass dampers (LT-MTMD), consisting of several lever-type tuned mass dampers (LT-TMDs) with a uniform distribution of natural frequencies, are proposed for the vibration control of long-span bridges. Using the analytical expressions for the dynamic magnification factors (DMF) of the LT-MTMD structure system, an evaluation, with inclusion of the LT-MTMD stroke, is conducted on the performance of the LT-MTMD with identical stiffness and damping coefficients but unequal masses for mitigating harmonically forced vibrations. The LT-MTMD is found to possess the near-zero optimum average damping ratio regimen when the total number of dampers exceeds a certain value. In comparison, the LT-MTMD without the near-zero optimum average damping ratio and the traditional hanging-type multiple tuned mass dampers (HT-MTMD) without the near-zero optimum average damping ratio can achieve approximately the same optimum frequency spacing (an indicator for robustness), effectiveness, and stroke. Compared with the HT-MTMD, the LT-MTMD needs lesser optimum average damping ratio but significantly higher optimum tuning frequency ratio. Its main advantage is that the static stretching of the spring may be adjusted to meet the practical requirements through the support movement, while maintaining the same robustness, effectiveness, and stroke. Consequently, the LT-MTMD is a better choice for suppressing the vibration of long-span bridges as the static stretching of the spring required is not large.

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)