研究生: |
黃子維 Tzu-Wei Huang |
---|---|
論文名稱: |
具使用情境參數的動態BIM之巨量資料分析應用實例 Application Case of Big Data Analysis on Dynamic BIMs with Parameters for Use Scenarios |
指導教授: |
陳鴻銘
Hung-Ming Chen |
口試委員: |
呂守陞
Sou-Sen Leu 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: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 109 |
中文關鍵詞: | 建築資訊模型 、雲端運算 、巨量資料分析 、資料探勘 、MapReduce |
外文關鍵詞: | BIM, Cloud Computing, Big data analysis, Data Mining, MapReduce |
相關次數: | 點閱:330 下載:5 |
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本研究基於一雲端化資料儲存與管理系統,採用巨量儲存技術與分散式技術對巨量之BIM資料進行處理,並採用資料探勘之技術,使此雲端系統具備巨量資料分析之功能。傳統BIM中僅包括靜態資料,如幾何參數、物理性質、和空間關係,多屬於物理空間模型。而在本研究中BIM延伸至動態BIM,其中動態BIM中包括了動態資料,其中諸如設施環境與使用狀態之監測歷史記錄,以及持續觀察使用者與建築的互動情況等,透過上述收集而來的資料,進而取得使用者之經驗參數,使BIM變成為一個具經驗參數化的模型。
對於動態BIM的巨量資料分析應用,本研究基於一具備雲端儲存與管理系統中,提出採用MapReduce分散式運算的巨量資料預處理框架,並整合資料探勘工具R語言於預處理後,對巨量資料進行分析,本研究提出模擬案例,對所提出之系統基於室內環境與能源消耗的狀態下來進行預測人數,使用之模型為各種資料探勘的方法,如關聯規則、分類分群、迴歸分析、以及預測的組合,從預測之結果進行驗證,並依此案例操作流程證實動態BIM之巨量資料分析之可行性,
In this study, a Cloud-based BIM system which can store and manage massive BIMs online was utilized and integrated with data mining technology for perform-ing big data analysis online. Traditional BIM includes only static information, such as the geometric parameters, physical properties, and spatial relations, for model-ing of the physical space. In this study, BIM was extended to dynamic BIM which also includes dynamic data, such as historical records from the monitoring of the facility environment and usage, as well as the user experience parameters from continuous observation of the interaction between users and the space. Regarding the applications of dynamic BIM big data, this study proposed a big data prepro-cessing framework based on the MapReduce distributed computing for the data of massive BIMs stored in the Cloud-based BIM system. R were integrated into the system as the data mining tools to analyze the data after preprocessing. In a case study, the proposed system was applied to find hidden dependencies in the pro-cessed data sets for estimating the number of people in a facility based on the state of the indoor environment and energy consumption. The model developed is a combination of various data mining methods including association rules, classifi-cation and clustering, regression analysis, and prediction. The estimation results were verified and analyzed in this study. The case study shows the feasibility for performing big data analysis on dynamic BIMs.
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