預裝在鋼結構中扮演著重要角色，它指的是在現場施工之前，將結構元件預先在製造廠或預裝場進行組裝。然而，當前預裝過程需要耗費大量人力和時間，以確保元件在合理誤差範圍內成功組裝。本研究開發了光達模擬器作為研究工具，以此模擬實務上以光達(LiDAR，Light Detection and Ranging) 取得構件的掃描資料。

藉由掃描鋼構件所獲得的點雲資料，本研究提出了基於點雲資料的自動化螺栓孔辨識演算法，該演算法能夠高度自動化地處理螺栓孔辨識問題。透過這項自動化辨識方法，本研究在多個案例中成功辨識螺栓孔平均比例高達99.8%，顯示了演算法的有效性與準確性。

最後，本研究提出利用構件的特徵點與幾何條件搭配窮舉法尋找兩構件匹配的轉換矩陣，透過計算成功得到兩個構件的接合姿態。此方法使得在電腦中進行虛擬組裝成為可能，並可省略實務上對構件進行預裝的流程。這項虛擬組裝技術在此階段帶來更高的安全性與效率，並有望在鋼結構領域中發揮重要的應用價值。

Pre-assembly plays a significant role in steel structures, referring to the process of preassembling structural components in manufacturing plants or pre-assembly sites before on-site construction. However, pre-assembly may lead to work safety accidents due to various reasons. To address this issue, this study developed a LiDAR simulator as a research tool with highly flexible and versatile parameter settings, capable of generating highly realistic scan data according to user requirements.
Upon obtaining scan data of steel components through the LiDAR simulator, this study proposed an automated bolt hole recognition algorithm based on point cloud data. The algorithm efficiently handles complex bolt hole recognition problems and successfully restores unidentified bolt holes in the components. Through this automated recognition method, the study achieved an average success rate of 99.8% in recognizing and repairing bolt holes in multiple cases, demonstrating the effectiveness and accuracy of the algorithm.
Finally, the study presented a method using an exhaustive search to find the transformation matrix for matching two components, enabling successful calculation of their joint configuration. This method allows virtual assembly in a computer environment, eliminating the need for practical pre-assembly procedures. The virtual assembly technique contributes to higher safety and efficiency at this stage, showing promising potential for significant application in the field of steel structures.

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