單一建築物拆除工程混合物產生量推估之研究; Model Development for Estimating the Quantity of A Single Building’s Demolition Waste

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Title:	單一建築物拆除工程混合物產生量推估之研究;Model Development for Estimating the Quantity of A Single Building’s Demolition Waste
Authors:	林政緯;Cheng-Wei Lin
Contributors:	營建管理研究所
Keywords:	建築混合物;數量推估;類神經網路;Demolition Waste;Neural Network;Quantity
Date:	2005-06-03
Issue Date:	2009-09-21 12:03:34 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	台灣地區每年產生大量之建築混合物，據內政部建研所之調查顯示，台灣地區經合法申請新建與拆除之建築混合物數量據估計每年約有1,100多萬公噸。順應世界各國永續發展的潮流趨勢下，目前政府正努力朝向著混合物減量與再利用之方向努力。倘若能追溯建築混合物之產生源頭，掌握建築混合物產生量，方能有效管制混合物與減少違規棄置之情事發生。建築混合物產生量之推估不似開挖土方可做簡易計算，較難有精確之估算標準。目前相關推估研究只考慮將建築面積與用途納入影響因子當中，並未將其他影響因子（如構造種類等）考慮至建築混合物產生量之計算，如此將造成推估數據與實際產生量有所落差。本研究透過專家訪談及問卷調查方式，彙整現行建築物拆除工程混合物產生量之主要影響因子。並蒐集各縣市政府拆除執照資料與工地現場實際調查紀錄之拆除混合物產生量加以分析篩選，利用近年來解決預測問題有較佳成效之類神經網路，建立單一建築物拆除工程混合物產生量推估之模式。經上述建立拆除混合物推估系統，測試資料於容許誤差15％之內時準確率達91.67％，與現行各界常使用之推估係數相比較，本研究所建立之模式較為接近實際產生量，顯示本研究已改善現行推估係數之準確率。本研究之結果可供政府機關更有效掌握拆除工程時所產生之混合物數量，健全現行拆除混合物總量申報管控作業，避免違規棄置情形發生。 Under the global development of sustainable construction, the government in Taiwan devotes more and more efforts for the proper treatment and recycling of construction and demolition wastes. In order for controlling those wastes to go into recycling plants or legal dumping sites, more and more local district counties demand construction sites to submit a waste treatment plan before starting the work. In the plan they have to estimate the expected quantity of CD&W wastes, and to state clearly where will they go, how will they treated. Thus the estimated quantity serves as a base for waste monitoring and controlling in the remaining process. The objective of this research is to study the major factors influencing the waste quantity generated in a building’s demolitiom construction, and to develop a model each for the estimation of waste quantity. Literature reviews and expert interviews are conducted to identify the major factors influencing waste quantity. Identified factors include size of the building floors, purpose of the building, the structural type, height of the building, and so on. Totally 47 cases for building demolition are collected for their quantities of waste generated. The Neural Network (NN) method is employed for the development of the estimating model. The developed NN model is about 100％ accurate under permit error rate 15％ in testing , and 91.67% accurate under permit error rate 15％ in testing. A comparison of the developed estimation model and the current most used estimation formula is conducted on 14 test cases. The result shows that the developed models are significantly more accurate than the current ones in estimating the waste quantity of a single building’s demolition.
Appears in Collections:	[Graduate Institute of construction engineering] Electronic Thesis & Dissertation

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