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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/73069


    題名: 建築預鑄結構體生產難易度分級與分析之研究;Integrating precast big data and computational intelligence to classify the levels of construction difficulty
    作者: 戴興偉;Tai, Hsing-Wei
    貢獻者: 營建管理研究所
    關鍵詞: 建築預鑄;分群;難易度等級;群啟發;人力配置;construction precast;clustering;difficulty level;Swarm-Inspired Projection;manpower allocation
    日期: 2017-01-19
    上傳時間: 2017-05-05 17:43:04 (UTC+8)
    出版者: 國立中央大學
    摘要: 建築預鑄工廠通常皆會面臨到缺少具經驗的技術人員、超時工作與人力配置等問題。本研究主要目的是將建築預鑄構件的生產區分出難易度等級。經於完整探討有關預鑄構件之生產、分群及分級方式、成本管理、人力調派與最佳化的文獻回顧後,本研究於各種管理觀點衍生出的相關技術,最後選用群啟發演算法(Swarm-Inspired Projection)之分群方式,其可將所蒐集的數據資料區分出數個群組。本研究彙集整合台灣過去10年之建築預鑄構件資料總數據為1,015,840筆資料,經相關統計分析篩選後為772,212筆資料作為研究樣本,經輸入於SIP演算法中,所得結果顯示預鑄構件生產呈現顯著差異之四個等級;各級差異範圍在39至112分鐘間。本研究成果可供預鑄廠未來於生產成本與人力資源配置管理之重要參考依據。另外本研究亦驗證整合計算演算法對實際巨型資料的效果。;Companies in the construction precast industry usually face lack of skilled manpower, overtime working, and difficulty of manpower allocation. The objectives of this research are to identify the difficulty level of precast elements. After conducting a comprehensive literature review regarding precast production, clustering, classification, cost management, manpower allocation, and optimization, expertise from field/headquarter supervision leads the way to SIP clustering algorithm that drives collected data converted to certain clusters. Data collection was carried out to gather over 90% precast construction data in Taiwan for the recent decade. A total of 1,015,840 datasets were collected and then 772,212 datasets were taken into computation using Swarm-Inspired Projection (SIP) algorithm after trimming data. The results yielded from the algorithm show that there are 4 levels associated with difficulty for precast production. The gaps between these 4 levels are significant and are in the ranges around 39 to 112 minutes. The findings should be not only directly used for construction precast companies to improve cost control and management for manpower, but verify the feasibility for integrating computational algorithm with practical big data.
    顯示於類別:[營建管理研究所 ] 博碩士論文

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