مدلسازی ریاضی پارامترهای انرژی تعبیه شده، گازهای گلخانه ای، ضایعات، زمان - هزینه در پروژه های ساختمانی: مرور
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|69613||2013||15 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Building and Environment, Volume 59, January 2013, Pages 23–37
The construction industry including its support industries is one of the highest consumers of natural resources. In the act of consumption of natural resources during construction processes, embodied energy and greenhouse gases are emitted which have adverse effects on the natural environment. Thus, recent studies have revealed a significant interest in the quantification of embodied energy and greenhouse gases in construction processes. Unfortunately, current interpretations and quantification procedures of embodied energy and greenhouse gases are quite unclear. More also, while greenhouse gas and embodied energy quantification models are so disaggregated, studies reveal their existence in isolation without any links to other important environmental/construction management variables such as waste, time and cost. The objectives of this study are to identify the gaps in the current computation models, to reveal the relationships between the identified models and to propose a framework towards developing an integrated model for measuring embodied energy, greenhouse gases, construction waste, time and cost. The contributions of this study are three-fold. Firstly, the identification of the different models and variables, such that they can be used in computations, that can lead to consistent and comparable results. Secondly, investigate the relationships amongst embodied energy, greenhouse gases, construction waste, cost and time variables, that can facilitate the quantification process and hence potentially facilitate the engagement into low carbon building design by construction professionals. Lastly, lay the foundation for further research especially with regards to the integration of the different models and variables so that they can be measured simultaneously.