現代社會的都會區由於可利用的土地與空間有限且日漸減少的情況下，都市往上發展，興建高層建築成為不可避免的趨勢。由於高層建築相對於低矮建築來說勁度較低，且當建築物愈接近地面因地表粗糙度的關係所受到風力較小，因此愈高的建築物所受到的風力會愈大，所以在設計時就必須考慮到風力對高層建築物的影響。而在高樓林立之現今大都市中，除了單棟建築的風力之外，相鄰建築物間之風力交互作用也成為一個重要的問題，影響高層建築物間干擾效應的參數很多，如上游流場、建築物間的距離及主建物及干擾建物的尺寸等。 本文的風洞實驗數據顯示，在權重干擾指標部分，干擾指數會隨著建物高度而增大，因此可知當主建物與干擾建物相同時，其高寬比愈大所產生的干擾效應也會隨著增大。 本文主要是利用類神經網路對目前已有的干擾效應資料庫來進行干擾係數的預測，最後再利用所預測的干擾係數來計算出設計風載重。研究使用的類神經網路為輻狀基底函數類神經網路，其主要架構分為輸入層、一層隱藏層及輸出層，在使用遞增式的方法選取出中心點後透過隱藏層中的輻狀基底函數來尋求出網路模式。 由本文的結果得知，在訓練資料的預測方面絕大多數的誤差都低於3%，僅有少數幾點偏高一些，但仍然都在5%以下，在預測資料上，A(α=0.32)、B(α=0.25)、C(α=0.15)三個地況中，除了C地況在順風向動態背景方面平均誤差較高外，其餘在靜態及動態背景部份平均誤差皆低於7%，動態共振部份A、B地況介於7~12%之間，但在最複雜的C地況則大多介於15~22%之間，因此在共振部份的預測仍需加強。 Tall building plays an important role in the city development due to the limited land, and the interference effects from the adjacent buildings cannot be ignored. Due to the complexity of the interference phenomenon, the building interference phenomenon is too complicated a problem for traditional engineering approaches. A more sensible way to deal with this problem would be using good quality aerodynamic database with IT techniques such as neural network. The aim of this thesis is to construct an aerodynamic database on building interference that can be used together with the wind code or/and single building’s aerodynamic database for wind resistant design of tall buildings. Through the comparison of the weighted interference index, the results of wind tunnel experiments indicate, that the interference effects increase with buildings aspect ratio. In this study the Artificial Neural Network technique was applied on an existing limited aerodynamic database to predict tall buildings’ interference factor (IF), and use the result to calculate design wind load. The ANN model is radial basis function (RBF) neural network, and the framework includes input layer, a single hidden layer and output layer. By centers and radial basis function in the hidden layer to create a neural network. The results indicate that, in the training phase, the error almost can smaller than 3%, in the predicting phase, except acrosswind background part in terrain C ,the static part and background part in all terrain are less than 7%. The resonant part in terrain A&B are within 7% to 12%. However, in terrain C the error of resonant part can be as large as 15% to 22%. In other words the resonant part need to be further improved.
