台灣位處環太平洋地震帶，因板塊相互擠壓導致地震頻繁，而極端規模的地震常造成嚴重災情。地震災害後首要任務是搶救受困者，若能在黃金72小時內進行有效地搜索與救援(search and rescue, SAR)便能大幅降低傷亡。因此吾人結合建築資訊模型(building information modeling, BIM)與動態模擬(dynamic simulation)系統，以及為驗證相似度演算法的可行性，利用候選解的概念針對構架結構進行倒塌模擬欲建立其倒塌模式資料庫。主要內容為建立14種構架結構模型，匯入物理引擎(Bullet Constraints Builder, BCB)並對BCB倒塌模擬進行假設以建立倒塌模擬資料庫，再藉由歐式距離相似度、餘弦相似度及Bloch相似度三種相似度演算法對倒塌資料庫進行資料庫完整性分析，建立完整的構架結構倒塌模式資料庫。最終假設一倒塌個案作為實際案例進行案例比對，透過三種相似度演算法同時篩選，從倒塌模式資料庫中搜索出對應的倒塌模式。
本研究成果為相似度演算在判定簡易構架模型時表現優異，且在複雜之高層樓構架結構模型亦能找出對應整體的倒塌模式，是因為資料庫完整性分析能提高資料庫整體性能。且案例比對因同時使用三種相似度指標進行搜索，故能有效地找出最佳解，但所得倒塌模式並非一定是距離最接近解，優先考量整體結構的餘弦相似度能先篩選出相同傾倒方向的倒塌模式，再以距離法去判定其相似程度，能更正確地搜索到最佳解。最後藉由比對成功案例，對三種相似度指標與柱構件及梁構件分開討論可以驗證本研究對倒塌模擬進行的假設，將三種相似度指標互相迴歸分析可知歐式距離法主導著Bloch相似度的表現，因此吾人認為相似度演算法應以歐式距離為主餘弦為輔去進行分析。

Taiwan is located on the Ring of Fire. Frequent earthquakes are due to the movement of plates, and extreme earthquakes often cause severe disasters. The primary task after an earthquake disaster is rescuing the trapped. If search and rescue (SAR) can be executed effectively, casualties can be significantly reduced. Therefore, we create a procedure for analyzing structural collapse that combine building information modeling (BIM) and dynamic simulation system. Moreover, to verify the feasibility of similarity algorithm, only frame structures are modeled. The candidate solution concept is used to establish collapse databases. This study build 14 frame structural models, then import them to the physics engine and make hypothesis on collapse simulation. Hence, use Euclidean distance similarity, cosine similarity and Bloch similarity to analyze the integrity of databases, then establish collapse mode databases. Finally, generate a collapse case as an actual case, then filter simultaneously through three similarity indicators to figure out the corresponding collapse mode from database.
The results show that the similarity algorithm is outstanding in simple frame structures, and it can also perform well in complex high-rise frame structures, because database integrity can improve the ability of databases. Furthermore, prioritizing the cosine similarity can obtain the same direction case first, then use distance similarity method, which can accurately search for the best solution. Finally, discussing three similarity indicators from column and beam can verify the collapse simulation hypothesis of this study. The regression analysis of three similarity indicators show that Euclidean similarity dominates the performance of Bloch similarity.

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