台灣位處歐亞板塊及菲律賓海板塊交界，因板塊相互擠壓導致地震頻繁，年間平均發生214次有感地震，造成諸多生命財產損失。地震災害後首要任務為搶救受困者，若能在黃金72小時內有效地進行搜索與救援(search and rescue, SAR)便能大幅降低傷亡人數。因此本研究考慮利用建築資訊模型結合災後搜救，建立不同模型之倒塌模式資料庫，設立有效指標驗證以外部構件資訊預測內部構建的可行性並將各資料庫中倒塌模式分類，主要流程為建立6種不同構架之建築資訊模型，匯入Blender中並以其物理引擎Bullet Constraints Builder進行倒塌模擬，參考台灣地區近十年實際地震數據設計人工地震參數，分別藉由模型之外部和內部構件之歐式距離相似度、夾角餘弦相似度、曲率相似度及結合前三者之綜合相似度作為指標進行線性分析，利用演算法計算所得相關係數以及視覺化方式確認外部構件資訊預測內部構件準確性，最終設立相似度門檻值將資料庫中案例進行分類，去除相似度高倒塌案例，使資料庫中留存案例皆為相異倒塌模式，以提高資料庫效能。
本研究線性分析部分成果以散點圖呈現，證明出四種相似度指標皆能夠有效驗證模型各案例外部構件資訊和內部構件相關性，並且皆能成功判別外部結構資訊相似而內部結構相異之案例，證明四種指標皆有能力分類資料庫中相異倒塌模式。最後將各指標設置門檻值分類資料庫中案例以增加資料庫效能，各模型最終獲得資料庫數量分布於213 ~ 507筆間，由模型三、四比較後發現資料庫數量主要取決於幾何形狀排列方式，在視覺化模型案例後確認其樣式十分接近現實，尤其於高樓層模型，僅一層樓案例較有大量物件飛離之特殊案例，證明本研究建立之模型擁有足夠擬真度。

After an earthquake disaster, the first task is to rescue the trapped people. Therefore, this research will use the method of post-disaster search and rescue combined with building information model to establish the collapse mode database of different models. Use the Euclidean distance, cosine, curvature and comprehensive similarity as indicators to carry out linear analysis to confirm the accuracy of external prediction and internal construction. Then, a similarity threshold is established to classify the cases in the database, make all the remaining cases in the database become different collapse modes, so as to improve the performance of the database.
The results of this study proves that the four similarity indicators can successfully verify the linear relationship between the external and internal structures of each case of the model, and they can successfully show that the cases which external structure information is similar but the internal structure is not similar. Finally, the threshold value of each indicator is set to classify the cases in the database to increase the integrity of the database. The number of databases found that the number of databases is mainly determined by the arrangement of geometric shapes. It is very close to reality after visualizing the model case, especially in the high-rise model, which proves that the model established in this study has sufficient fidelity.

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