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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105631

    Title: 以類神經網路建立半圓頂型屋蓋結構子午線上風壓頻譜之估算模式
    Other Titles: The establishment of wind spectrum estimation models for meridian on dome-like structures using artificial neural networks
    Authors: 劉博溢;Liu, Bo-Yi
    Contributors: 淡江大學土木工程學系碩士班
    王人牧;Wang, Jenmu
    Keywords: 類神經網路;輻狀基底函數;結構工程;風工程;風壓頻譜;大跨徑結構;半圓頂結構;ANN;RBFNN;Wind Engineering;Structural Engineering;Wind pressure Spectrum;Dome-Like Structure;Large Span Structure
    Date: 2015
    Issue Date: 2016-01-22 15:01:03 (UTC+8)
    Abstract:   結構物的耐風設計通常需要經由風洞實驗,取得風壓頻譜的實驗數據,其過程相當耗時且費用昂貴。使用回歸公式來整理分析實驗數據,常無法得到準確的風壓頻譜值,因此,如何更有效的利用風洞實驗氣動力資料庫是一個重要的課題。
    Wind resistant design of buildings often needs to acquire wind spectra from wind tunnel tests. Using regression formulas to process and analyze experimental data of wind spectra usually is not very accurate. Therefore, one of the most important issue is how to use experimental wind load aerodynamic database more effectively.
    The development of wind load estimation models for high-rise buildings using artificial neural networks (ANNs) has already been studied by the Wind Engineering Research Center of Tamkang University (WERC-TKU) for a long time. However, no complete research about dome structures using ANNS has been conducted. Only a large-span research project in 2011 conducted by research assistant Hsin-Chieh Chung trained ANNs for the predictions of wind spectra of fixed shape dome, which examined the axis and circle relation to coherence wind spectra. Never the less, there are a lot of rooms for further development of the estimation models for different shapes of domes.
    In this study, the wind pressure database of dome models that established by Dr. Yuan-Lung, Lo in University of Tokyo was used. Comparing with the data that used by Hsin-Chieh Chung, the focus is more on the differences of wind pressure spectra on the meridian with the change of curvature and height. Random center selection method was used to write RBFNN program to train, validate and test the ANNs. The estimation models found not only accurate but also theoretically consistent. Models were also compared with previous regression formula. At the end, the ANN models were applied to a network platform and a simple web browser user interface was built. Wind pressure spectra calculated by the server can be easily obtained with simple parameter inputs, which can be used as preliminary estimations before wind tunnel tests.
    Appears in Collections:[土木工程學系暨研究所] 學位論文

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