The Study of Wind Coefficient and Spectrum Prediction for High-Rise Buildings

• Main Authors: Hsin-Chieh Chung, 鍾欣潔
• Other Authors: Jenmu Wang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/19217788774440746570

碩士 === 淡江大學 === 土木工程學系碩士班 === 98 === In wind-resistant design of structures, the calculation of the required wind loads, coefficients and spectrums are usually based on wind codes and standards or wind tunnel tests. The process is very time-consuming and expensive. Therefore, regression analysis is often used to study experimental data in practice to produce regression formulas. These formulas can then be used to forecast results without performing experiments. Three MATLAB build-in regression methods, namely regression analysis, polynomial regression and linear regression, were used to examine wind coefficients and to compare the results of the different methods. Also, MATLAB’s neural network functions were used as well to train, simulate and forecast wind coefficients using terrain, side ratio (D/B) and aspect ratio (H/B) as inputs The neural networks used includes BPNN(Back Propagation Neural Network), RBFNN (Radial Basis Function Neural Network) and GRNN(General Regression Neural Networks). To extend the previous research that uses RBFNNs for wind spectrum simulation, the preprocessing of data and internal structure of the networks were compared and analyzed again in this research. The goals were to reduce the number of the networks and to increase the accuracy of predictions. According to the results presented in this thesis, RBFNN is the best way to predict wind coefficients. The final application used three networks to predict wind coefficients. For alongwind coefficients, the predictions of Cd and Cdm are better than Cdd and Cdmd, and terrain B and C are better than terrain A. For acrosswind coefficients, the maximal prediction error is more than 10%, but the error is below 4% when aspect ratio is greater than 1. For torsional wind coefficients, the maximal error is over 24% impacted by the small values of the coefficients, and terrain A is better than terrain B and C. The suggestion of the thesis is to add terrain condition to the inputs of the RBFNN, which reduces the total number of networks needed to forecast wind spectrums from 72 to 24. This only slightly increases the error of some validation cases. The most obvious changes occurred to H/B=6 cases. A case-based expert system operating on the Internet was built using the above findings to estimate target buildings’ wind coefficients and spectrums for preliminary designs of building wind loads.