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研究生: 林宗禧
TSUNG-HSI LIN
論文名稱: Cloud BIM: 應用雲端運算與WebGL技術之網路式BIM系統
Cloud BIM: A Web-Based BIM System with Application of Cloud Computing and WebGL
指導教授: 陳鴻銘
Hung-Ming Chen
口試委員: 郭榮欽
Rong-Chin Guo
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: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 100
中文關鍵詞: 建築資訊模型WebGL雲端運算HadoopMapReduceHBase
外文關鍵詞: BIM, WebGL, Cloud Computing, Hadoop, MapReduce, HBase
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  •   建築資訊模型(Building Information Model, BIM)係將營建專案之三維視覺化模型與各領域屬性之數位化資料整合於特定格式之檔案或資料庫中,其包含建築、結構、機電、空調、消防等各領域之數位化資料。BIM三維視覺化技術幫助設計階段之開發者可藉由3D視覺化方式設計建物,並幫助施工階段專案管理者與施工單位更容易核圖、校正施工現場狀況,亦可幫助業主、客戶與專案管理者三方之溝通,讓非專業人士也能藉由3D模型對該專案有更清楚的了解。而一個理想化的BIM模型,即有建物完整累積的生命週期資料。現今市面上已有許多商用BIM軟體,但隨著軟體所提供的系統功能越來越多時,其所耗費的計算資源與儲存空間也越來越大,當BIM軟體所需之儲存空間與運算資源之需求不斷地增加,使得單機個人電腦所需負荷之需求資源越來越高,即為現今BIM系統之發展瓶頸。
      本研究為了解決BIM之大量運算與大量儲存之需求,因此應用雲端運算技術以解決現存之BIM問題。結合BIM與雲端運算之網路式服務,成為可同時服務多人之系統。透過雲端之巨量運算技術,讓專案設計者僅須經由幾個簡單的步驟,將BIM模型上傳到本研究之雲端伺服器,其大量的BIM資料即由MapReduce解析、擷取3D BIM模型物件與物件屬性,並存於雲端資料庫HBase中。此服務讓多位使用者可以隨時隨地使用桌機、筆電或平板,經由瀏覽器即可透過WebGL之瀏覽器3D顯示技術,能夠檢視3D BIM模型。而此雲端化之網路式BIM系統,可幫助不論在設計階段、施工階段,或是應用於公共工程招標與投標,只要是有分享檢視BIM模型之需求者,均可使用本系統。雲端化之網路式BIM系統,其解決大量的運算與儲存需求、使多人共同享有與使用之特性,亦提升更多在BIM應用層面上的可能性。


    Building Information Model (BIM) is a composition of using three-dimensional visualization models and digital data to describe a construction project. The three-dimensional visualization models are used to display the appearance of the building, digital data of are used to display the specifics of the construction project. Data are usually stored in files or databases, these data often contain large quantities of information from various fields, such as architectural, structural, mechanical and electrical etc. The developers design construction projects by establishing three-dimensional visualization models, the models serves as a communication platform for owners, clients and the project managers. If developers aim to build a perfect BIM model, the BIM model should include information of the buildings entire life cycle. There are many different kinds BIM commercial software available today, all of them provide a large number of functions and applications, but these applications require large quantities of computing resources and storage space. Constantly upgrading the hardware requirements for the use of BIM software, has become a bottleneck for the development of BIM
    In this study, we incorporate the techniques of Cloud Computing for encountering the development bottleneck. A web-based multi-user service is constructed, by combining BIM with Cloud Computing .The Cloud BIM Computing system provides mass computing capabilities of MapReduce for analyzing BIM projects. The HBase provides mass storage spaces for storing BIM information. Users can access the services of Cloud BIM by using PCs, laptops and tablets anywhere anytime and they can view the 3D model via the browsers with support for WebGL regardless of the needed hardware requirements. Cloud BIM can also be used in the design phase and construction phase of a project, by sharing the BIM models on the internet

    論文摘要 I ABSTRACT III 誌謝 V 目錄 VII 圖目錄 IX 第一章 緒論 1 1.1研究動機 1 1.2研究目的 4 1.3研究範圍 8 1.4研究方法 8 1.5論文架構 10 第二章 研究背景 11 2.1文獻回顧 11 2.1.1 BIM 11 2.1.2 WebGL 11 2.1.3 雲端運算 13 2.2系統開發技術 15 2.2.1 雲端運算 15 2.2.2 雲端技術 17 2.2.3 WebGL 22 2.2.4 IFC 23 2.3系統開發工具 24 2.3.1 PHP 24 2.3.2 Apache Thrift 25 2.3.3 GLGE 25 2.3.4 AJAX 26 第三章 系統功能需求分析與規劃 28 3.1 客戶端介面模組 29 3.1.1 BIM模型上傳模組 29 3.1.2 BIM模型WebGL檢視模組 30 3.1.3 BIM專案管理模組 32 3.2 伺服端運算模組 33 3.2.1 前言 33 3.2.2 IFC前處理轉譯xIFC模組 34 3.2.3 xIFC MapReduce解析模組 35 第四章 系統運作機制 37 4.1 IFC資料前處理轉譯xIFC 37 4.1.1 IFC資料之擷取運算 37 4.1.2 IFC資料前處理轉譯xIFC 43 4.2 xIFC之MapReduce解析運作機制 47 4.3 Cloud BIM之HBase 資料綱要設計 50 4.4 Apache Thrift參數設定 52 4.5 WebGL與Ajax: 瀏覽器3D呈現BIM模型之機制 54 第五章 系統架構與功能探討 57 5.1 資料庫資料綱要 58 5.1.1 關聯式資料庫結合Project群組之綱要設計 58 5.1.2 HBase巨量資料庫結合Display群組之綱要設計 59 5.2 主系統類別架構 61 5.2.1 Project群組相關之類別 61 5.2.2 Display群組相關之類別 63 5.2.3 Account群組相關之類別 65 第六章 系統使用範例 67 6.1 前言 67 6.2 系統登入與登出 68 6.3 BIM模型管理 69 6.4 BIM模型匯出IFC檔 70 6.5 BIM模型上傳 71 6.6 BIM模型WebGL 3D檢視 73 6.7 多人使用與多人服務 76 第七章 結論與未來展望 78 7.1 總結 78 7.2 未來展望 80 參考文獻 81 附錄 88

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