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|Other Titles: ||Wind coefficient and spectrum estimation models for aero-data based wind resistant building design|
|Authors: ||林昶志;Lin, Chang-Chih|
|Keywords: ||類神經網路;幅狀基底函數;風力係數;風力頻譜;氣動力資料庫;風工程;ANN;RBFNN;Wind Coefficient;Wind Force Spectrum;Aero-Data Based;Wind Engineering|
|Issue Date: ||2015-05-04 09:56:42 (UTC+8)|
Wind resistant design of buildings often needs to acquire wind coefficents and spectra from wind tunnel tests. Recently, the development of building design wind load standards of other countries has gradually progressed toward database-assisted design methods. Using regression formulas to process and analyze experimental data of wind coefficients usually are not very accurate. Therefore, one of the most important issue is how to use experimental wind load aerodynamic database more effectively.
For wind coefficients, Back Propagation (BP), Radial Basis Function (RBF) and Generalized Regression (GR) neural networks have been used previously at the Wind Engineering Research Center of Tamkang University (WERC-TKU) for wind coefficient simulations, and the previous finding was that the RBFNNs yielded the best results. In order to further validate the reliability of the prediction models, measurement data of wind tunnel experiments of models with aspect ratio from 1 to 2.5 were added to the scope of this research. The alongwind results showed that the predictions of windward drag coefficients (Cdw) were superior to the leeward drag coefficients (Cdl). For the acrosswind fluctuating lift coefficients, the predictions were better for buildings with side ratio greater than 1. For the torsional fluctuating moment coefficients, similar to the acrosswind results, the predictions of terrain B and terrain C were better than terrain A.
For wind spectra, not only previous WERC-TKU methods were followed but also several improvements were employed. Data was grouped into two adjacent side ratios for classification and random center selection was used for the RBFNN as before. On the other hand, the RBFNN program was recoded and different interpolation schema for adding data points to the low-frequency region was used. Using the new wind tunnel test data to train neural networks, adjusting the training and validation data sets and fine tuning the final model were conducted to produce improved and accurate results. For the acrosswind and torsional wind force spectra, the maximum errors of terrain B and terrain C were smaller than terrain A within the specific frequency. The worse individual RMSEs of the spectra appeared at aspect ratio 5.5 and 6.5 for all three terrains. The maximum errors of acrosswind spectra usually happened in the low frequency range (dimensionless frequency of 0.01) and the maximum errors of torsional spectra mostly happened in the vicinity of the peak value (dimensionless frequency of 0.1).
Finally, the neural network architectures and trained networks were applied to perform wind load analysis of several cases, and the case study results were discussed in the thesis.
|Appears in Collections:||[土木工程學系暨研究所] 學位論文|
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