BIM-aided variable fuzzy multi-criteria decision making of low-carbon building measures selection. (November 2016) - Chen, L.; Pan, W.
Highlights: A decision framework is developed for selecting low carbon building (LCB) measures. A new model is proposed to integrate the use of BIM and MCDM. Variable fuzzy sets theory is useful for dynamically allocating decision weights. Variable fuzzy PROMETHEE can select the best from other alternatives more easily. CFL/T5 lamps were observed to be the best LCB measures in the case studied. Abstract: Low-carbon building (LCB) has been regarded as an innovative and practical approach to reducing building carbon emissions. The design decision making for LCBs should consider various criteria which however are often associated with uncertain information. Little research has examined multi-criteria decision making (MCDM) in selecting LCB measures, particularly in high-density subtropical urban environments. That selection process is inhibited by the lack of consensus on assessing the performance of LCB options and of an efficient decision support system. The aim of this paper is to develop a BIM-aided variable fuzzy MCDM model for selecting LCB measures. The paper identifies the key criteria and alternatives to systematically assess LCB measures. Five criteria and nine alternatives were identified within the context of high-rise commercial buildings in Hong Kong, which are centralized on technical, economic and environmental aspects of building performance. With the use of BIM and eQUEST, the paper develops a MCDM model based on variable fuzzy Preference ranking organization method for enrichment evaluation (PROMETHEE) to improve the robustness and practicality of decision results over traditional methods. The developed model is validated utilizing a real-life project case in Hong Kong. The results suggest that the developed model will provide design decision makers with a consolidated tool for selecting LCB measures.