研究生: |
洪智國 Jhih-Guo Hong |
---|---|
論文名稱: |
具使用情境參數的動態BIM之巨量資料分析架構與應用 Big Data Analysis Framework and applications on Dynamic BIMs with Parameters for Use Scenarios |
指導教授: |
陳鴻銘
Hung-Ming Chen |
口試委員: |
謝佑明
Yo-Ming Hsieh 周建成 Chien-Cheng Chou |
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: | 2015 |
畢業學年度: | 103 |
語文別: | 中文 |
論文頁數: | 84 |
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 |
外文關鍵詞: | BIM, Cloud Computing, Big data analysis, Data Mining |
相關次數: | 點閱:282 下載:3 |
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近年來建築資訊模型(Building Information Modeling, BIM)雖廣泛應用於營建產業中規劃及設計等生命週期之階段,但目前尚未見從使用者的角度來思考BIM於空間內環境與設施之生命週期管理應用,如果將人與建築空間之互動情形等因素考量進BIM的概念中,並整合空間內環境數據及設施使用歷史紀錄,以持續觀察人與建築的互動情形所取得之使用者經驗參數,此BIM將可用來模擬各種使用情境,使BIM變成為一個具經驗參數化的模型,而這些歷史紀錄經過長時間的累積則會成為巨量資料,如何在這些巨量資料中去尋找有用的資訊,並達到營建工程生命週期上營運維護的助益,則具備相當程度的挑戰性。
本研究透過一雲端化之資料儲存與管理系統-Cloud BIM,進行延伸性擴充結合了巨量資料分析流程與資料探勘技術,其利用分散式技術MapReduce對巨量BIM資料進行處理,並整合資料探勘工具Mahout與R語言進行資料探勘。本研究針對一棟兩層樓辦公建築實作一巨量分析應用案例,首先將模型以IFC格式上傳至系統,讓使用者利用該模型進行模擬監測以獲得動態資料,並將大量取得之資料進行上傳與儲存,接著利用MapReduce進行巨量資料之前處理,再透過本研究於系統所擴充的資料探勘功能,進行資料的深度分析與建立各種分析模型,最後藉由模型分析之結果來找出資料之間所隱含的資訊或人與建築空間互動的潛在訊息,以輔助營建工程生命週期中營運維護階段之管理決策,提供營運管理者進行決策時可有一參考依據,經由此案例之實作來證明本研究於系統所整合的巨量資料分析功能於營運維護上之運用的可行性。
In this study, a system which utilizes Cloud technology to establish a data center to store and manage multiple BIMs simultaneously has been developed. This BIM data center can not only handle the big data of massive BIMs using multiple servers in a distributed manner, but can also be accessed by using various online devices anywhere, anytime for information sharing and visualization. Traditional BIM includes only static information, such as the geometric parameters, physical properties, and spatial relations, for modeling 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. Due to such an extension, dynamic BIM became a parametric model which can be used to simulate user behaviors. Regarding the applications of dynamic BIM big data, this study proposed a big data preprocessing framework based on the MapReduce distributed computing for the data of massive BIMs stored in the CloudBIM system. Mahout and 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 or regularities in the processed data sets for the influences of the states of a two story building on indoor environment, energy consumption, and user behaviors. The results of data mining can serve as the references rules for decision support on facilities control.
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