SELECTION OF MOST ECONOMICAL GREEN BUILDING OUT OF N-ALTERNATIVES: APPROACH OF VAGUE FUZZY LOGIC

Sunita Bansal, Srijit Biswas, S. K. Singh

Abstract: The concept of green building are now very effective tool to an engineer for construction of a new building and plays a vital role to influence his decision towards saving of water & electricity, providing healthier spaces, and generate less quantity of wastes during constructional period[3]. The quality and quantity of materials are directly gives the output efficiency in respect of the economy as well as positive environmental condition of a green building. But it is often found that total cost of building and total environmental impact values (TEIV) (inside and outside) are not same for all buildings constructed in various places due to fluctuation of market rate from place to place[4]. Thus to define a most economical green building out of n-alternatives, total cost of the building and it’s TEIV are very essential factors for assessment and making rank among them. But it is not a easy job because most of the data are not always crisp or numeric rather linguistic and hedges like ‘high reflective roof coating’, ‘bad orientation’, ‘poor sanitation’, ‘very good environmental quality’, ‘cheap materials’, ‘good drainage system’, ‘heavy rainfall’, ‘high energy consumption’, etc. to list a few only out of infinity. All these data are fuzzy in nature thus evaluation of many objects here is not possible with numerical valued descriptions[1]. All experts’ perception towards giving his decision depends wholly on his neural network functions which fluctuate according to the nature of function of dendrite and axon. Thus every decision-maker hesitates more or less on every evaluation activity which needs to be eliminated. The fuzzy logic has now proved worldwide as a tremendous tool to tackle this situation. This paper presents a fuzzy modelling for selection of most economical green building (GB) out of n-alternatives more precisely

Keywords: attributes, fuzzy decision, TEIV, vague fuzzy EIA, etc.

DOI: https://doi.org/10.15623/ijret.2015.0402020