BIM-integrated TOPSIS-Fuzzy framework to optimize selection of sustainable building components

Existence of diverse sustainable materials with distinctive features causes more difficult decision makings for project teams especially when it is intended to use ideal materials from diverse types of sustainable products. Few research studies have been conducted so far on applying Building Information Modeling (BIM) to act as Decision Support System (DSS) using of math works functions and tools in combination with Multiple Criteria Decision-Making (MCDM) techniques. The main purpose of this study is to propose a methodology that integrates BIM with decision-making and problem-solving approaches including Fuzzy and TOPSIS in order to efficiently optimize the selection of sustainable building components at the conceptual design stage of building projects. To select the optimum building components, each item is assessed against three major attributes of decision criteria as Design, Economic and Quality factors, which are applied in Multiple Attribute Decision Support System (MADSS) methodology to indicate the various performance of buildings sustainability. This BIM-integrated process is linked to the engine of Matlab software to apply Fuzzy functions on the users' priority in order to automatically suggest the ideal solutions. The design alternatives suggested by Matlab is validated by Life Cycle Cost (LCC) method to analyze the operational cost of an actual building project. Using this innovative method will make the decision-making procedure more convenient as well as proposing more realistic and reliable final and sustainable optimized choice.

Automated summary

This study proposes a methodology that integrates BIM with decision-making and problem-solving approaches to optimize the selection of sustainable building components in the conceptual design stage. By assessing the building components against criteria such as design, economic, and quality factors and using a multiple attribute decision support system, the study provides more realistic and reliable choices for sustainable design.