台灣地區屬於亞熱帶島嶼，冬天有東北季風，夏秋時節又常受到颱風侵襲，由於高層建築物屬於柔性建物，對風的影響較為敏感，因此在設計時必須考慮風力對高層建築物的影響，其中鄰近的高層建築物間的相互影響也是在設計時必須考慮的重要因素。高層建築物間的干擾效應其影響的參數很多，如：兩建築物間的距離、建築物的幾何形狀及上游流場地況等。本研究中，利用類神經網路對已存在有限的高層建築物之干擾係數資料庫進行預測。研究所使用的為倒傳遞類神經網路屬於類神經網路中的一種網路模式，主要架構為輸入層、輸出層及單層隱藏層，並透過改變輸入層及隱藏層神經元個數進而找尋較佳之網路模式。研究結果指出，在A地況中(α=0.32)，倒傳遞類神經網路具有較佳之預測結果，其誤差較合理，尚可符合高層建築物進行初步耐風設計的需求；C地況中(α=0.15)，其預測誤差較大，特別是在橫風向背景部分的類神經網路預測值與風洞試驗值之間存在很大的差異，此部分差異極可能源自實驗數據，有待進一步釐清。

High-rise building is sensitive to wind. In Taiwan, typhoon is a problem, it can bring large wind load on buildings. Wind force is an important lateral design load for high-rise buildings, especially in interference effects of tall buildings. The interference phenomenon between two adjacent tall buildings involves many parameters, such as distance of the two adjacent buildings, buildings’ geometry shape and the upstream terrain conditions. In this study the Artificial Neural Network technique was applied on an existing limited aerodynamic database to predict tall buildings’ interference effects. The ANN framework includes input layer, output layer and a single hidden layer. The optimal number of neuron was selected through detailed parameter studies. The results indicate that the back-propagation ANN model can predict interference effects of tall buildings up to a reasonable margin of error in terrain A (α=0.32), at least for the initial building design stage. In terrain C (α=0.15), however, there exist significant deviations between ANN predictions and wind tunnel measurements especially in the acrosswind background part. This deviation is likely due to some unidentified errors during wind tunnel measurements.

目錄
第一章	緒論  …………………………………………………  1
1-1	研究動機 …………………………………………………  1
1-2	研究方法 …………………………………………………  2
1-3	研究內容 …………………………………………………  3
1-4	論文架構 …………………………………………………  5
第二章	文獻回顧  ……………………………………………  6
2-1	大氣邊界層與流場之模擬 ………………………………  6
2-2	力平衡儀之風力量測 ……………………………………  7
2-3	雷諾數效應 ………………………………………………  7
2-4	風洞實驗之阻塞效應 ……………………………………  8
2-5	干擾效應對順風向之影響 ………………………………  8
2-5-1	干擾效應對順風向風力頻譜之影響 ………………  9
2-5-2	干擾效應對順風向風力係數之影響 ………………  9
2-6	干擾效應對橫風向之影響 ……………………………… 10
2-6-1	干擾效應對橫風向風力頻譜之影響 ……………… 10
2-6-2	干擾效應對橫風向風力係數之影響 ……………… 10
2-7	干擾係數之現象 ………………………………………… 11
2-7-1	順風向之靜態 ……………………………………… 11
2-7-2	順風向動態 ………………………………………… 11
2-7-3	橫風向動態 ………………………………………… 12
2-8	高樓風載重各分量之相關性 …………………………… 12
2-9	干擾係數之預測 ………………………………………… 13
2-9-1	類神經網路 ………………………………………… 13
2-9-2	利用倒傳遞類神經網路預測干擾係數 …………… 13
第三章	理論背景  …………………………………………… 15
3-1	大氣邊界層流場特性概述 ……………………………… 15
3-2	力平衡儀 ………………………………………………… 16
3-2-1	力平衡儀量測架構 ………………………………… 16
3-2-2	力平衡儀量測基本原理 …………………………… 18
3-2-3	實驗中使用之公式推導 …………………………… 20
3-3	倒傳遞類神經網路 ……………………………………… 24
第四章	實驗設置  …………………………………………… 27
4-1	干擾效應資料庫說明 …………………………………… 27
4-1-1	改變干擾建築物高度比及寬度比系列 …………… 27
4-1-2	改變主要量測建築物斷面系列 …………………… 28
4-1-3	實驗座標系統 ……………………………………… 29
4-1-4	干擾係數 …………………………………………… 30
4-2	類神經網路主題說明 …………………………………… 31
4-2-1	倒傳遞類神經網路架構 …………………………… 31
4-2-2	使用參數與資料庫之訂定 ………………………… 32
第五章	結果與討論  ………………………………………… 34
5-1	實驗資料說明 …………………………………………… 34
5-2	實驗數據比較 …………………………………………… 38
5-3	類神經網路預測 ………………………………………… 40
5-3-1	高層建築物之干擾係數類神經網路模式建立 …… 42
5-3-2	高層建築物之干擾係數資料預測 ………………… 50
5-3-3	設計風載重 ………………………………………… 55
第六章	結論與建議  ………………………………………… 63
第七章	參考文獻  …………………………………………… 65 
圖目錄
圖(3-1) 模型與力平衡儀之動力反應頻譜轉換圖	17
圖(3-2) 類神經網路架構圖	24
圖(3-3) 類神經網路神經元示意圖	25
圖(4-1) 改變干擾建物高寬比系列	28
圖(4-2) 改變主要建物斷面系列	29
圖(4-3) 干擾效應實驗座標系統圖	30
圖(5-1) 實驗資料與座標位置參照圖	36
圖(5-2) 類神經網路四個、五個及六個輸入變數之架構圖	37
圖(5-3) 兩棟建築物並排示意圖	39
圖(5-4) 兩建築物並排之改變寬度比干擾係數與Xie and Gu[22]比較	39
圖(5-5) 隱藏層神經元個數與誤差	42
圖(5-6) A地況順風向背景之干擾係數分布圖	44
圖(5-7) C地況順風向背景之干擾係數分布圖	45
圖(5-8) A地況順風向靜態	48
圖(5-9) A地況順風向背景	48
圖(5-10) A地況橫風向背景	48
圖(5-11) C地況順風向靜態	49
圖(5-12) C地況順風向背景	49
圖(5-13) C地況橫風向背景	49
圖(5-14) A地況類神經網路4變數模式干擾係數預測之絕對誤差MAE	51
圖(5-15) C地況類神經網路4變數模式干擾係數預測之絕對誤差MAE	52
圖(5-16) 類神經網路5變數模式干擾係數預測之絕對誤差MAE	53
圖(5-17) 類神經網路5變數模式干擾係數預測忽略(2, 0)之絕對誤差MAE	53
圖(5-18) 類神經網路6變數模式干擾係數預測之絕對誤差MAE	54
圖(5-19) 類神經網路6變數模式干擾係數預測忽略(2, 0)之絕對誤差MAE	55
圖(5-20) A地況4變數設計風載重之誤差	57
圖(5-21) A地況5變數設計風載重之誤差	58
圖(5-22) A地況6變數設計風載重之誤差	58
圖(5-23) A地況共振干擾係數之絕對誤差MAE	60
圖(5-24) A地況順風向設計風載重之誤差(含共振部份)	61

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