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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
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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.

論文摘要 I ABSTRACT II 目錄 V 圖目錄 VIII 表目錄 X 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 4 1.4 研究範圍 5 1.5 研究方法 5 第二章 文獻回顧 8 2.1 自動化施工項目進度監控發展與應用 8 2.2 影像辨識研究發展與文獻 9 2.2.1 人工智慧 9 2.2.2 深度學習相關應用 11 2.2.3 4D時程管理與資料收集 12 2.3 系統開發工具 13 2.3.1 Unity 13 2.3.2 Microsoft SQL Server 14 2.3.3 專案排程軟體 15 第三章 系統架構與運作機制 17 3.1 系統架構 17 3.1.1 系統前置作業 17 3.1.2 系統運作流程 18 3.2 系統運作機制 20 3.2.1 BIM模型與元件資訊 20 3.2.2 進度排程與匯出匯入 21 3.2.3 攝影鏡頭架設與定位 23 3.2.3.1 攝影鏡頭架設與定位方法一 23 3.2.3.2 攝影鏡頭架設與定位方法二 24 3.2.4 隨施工進度更新BIM模型機制 25 3.2.5 影像偵測結果輸入BIM模型機制 27 3.2.5.1 施工影像偵測 27 3.2.5.2 系統座標轉換 27 3.2.5.3 元件選取 29 3.2.5.4 資料庫進度更新機制 30 3.2.5.5 進度視覺化回饋 31 3.2.5.6 進度偵測視覺化 32 3.2.5.7 整體工項視覺化 33 第四章 系統介面與使用情境 34 4.1 系統介面 34 4.2 使用情境 35 第五章 案例模擬 45 5.1 實際施工工地系統測試 45 5.2 模擬施工工地系統測試 47 第六章 結論與未來展望 52 6.1 結論 52 6.2 未來展望 53 參考文獻 54

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全文公開日期 2028/08/22 (校外網路)
全文公開日期 2028/08/22 (國家圖書館:臺灣博碩士論文系統)
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