研究生: |
吳庭瑜 Ting-Yu Wu |
---|---|
論文名稱: |
整合AI影像辨識與BIM技術之自動化施工進度管控系統 An automated construction progress control system based on the integration of AI image recognition and BIM technologies |
指導教授: |
陳鴻銘
Hung-Ming Chen |
口試委員: |
林主潔
Jay Lin 謝佑明 Yo-Ming Hsieh |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 營建工程系 Department of Civil and Construction Engineering |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 55 |
中文關鍵詞: | 人工智慧 、影像辨識 、建築資訊模型 、自動化建築專案進度監控 |
外文關鍵詞: | Artificial Intelligence, Image Recognition, Building Information Model, Automated Construction Project Progress Monitoring |
相關次數: | 點閱:515 下載:2 |
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隨著科技的進步,人工智慧的影像辨識已發展相當成熟,也廣泛的應用於各項產業領域,在營建產業中更是有多方研究致力於將其結合與應用,加上營建管理的專案進度排程已成為工程中不可獲缺的重要任務,故本研究嘗試將本研究室既有的研究所訓練的偵測模型做為影像辨識之輔助工具,此偵測模型主要針對施工現場之不同工項進度影像進行分類以及偵測,並加入建築資訊模型(Building Information Model, BIM)做為建築資訊的傳遞媒介,提出一套整合AI影像辨識與BIM技術的自動化施工進度管控系統。然而,上述的偵測模型僅能針對單一的影像進行辨識,而單一影像可能無法涵蓋完整的施工區域,因此,本研究將著重於結合多視角的影像偵測結果,並將基於AI偵測結果的資訊透過BIM模型做為資訊傳遞之媒介,最後,將現場進度資訊與進度排程系統進行比對,並將比對結果依元件進度以不同的顏色在BIM模型上呈現,透過視覺化的方式,使管理人員能夠直觀地即時掌握施工進度。
With the advancement of technology, the image recognition capabilities of artificial intelligence have matured significantly and have been widely applied across various industries. In the construction industry, there have been numerous studies dedicated to integrating and utilizing these capabilities. Coupled with the fact that project scheduling within construction management has become an indispensable task, this research endeavors to leverage the detection models previously developed by our research team as auxiliary tools for image recognition. These detection models primarily focus on classifying and detecting different construction progress images from various tasks on construction sites. Additionally, the research incorporates Building Information Model (BIM) as a means of conveying architectural information. The goal is to propose an integrated system that combines AI image recognition and BIM technology for automated construction progress monitoring and control. However, the aforementioned detection models can only address individual images, and a single image might not encompass the entire construction area. Thus, this study places emphasis on amalgamating results from image detection using multiple perspectives. Furthermore, the information derived from AI detection outcomes is transmitted through the BIM model as a medium of information transfer. Ultimately, the research involves comparing on-site progress information with the scheduling system, and presenting the comparison results on the BIM model using distinct colors corresponding to different component progress levels. This visual approach allows management personnel to intuitively and promptly comprehend the construction progress.
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