A Decision Support System for the Multicriteria Analysis of Existing Stock. (2017)

• Record Type: Journal Article
• Authors: Corneli, A.; Meschini, S.; Villa, V.; Di Giuda, G.M.; Carbonari, A.
• Is Part Of: Procedia engineering. Volume 196(2017)
• Page Start: 682
• Page End: 689
• Publication Date: 2017
• Subjects: Multi-criteria analysis -- Bayesian networks -- decision networks -- building stock -- BIM; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.08.058
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Owners of any large building stock, such as public administrations, usually have to manage a huge variety of buildings with a limited budget. For this reason, targeted refurbishing actions are needed to ensure that those buildings comply with the latest standards, to preserve the stock and also keep it in good condition. As a result, most public administrators have to make important decisions regarding what part of their stock should be refurbished first. In this paper a new methodology regarding an objective assessment of the quality of large building stock is suggested, as it could help prioritize refurbishment actions. The methodology is based on a decision support system, that is capable of semi-automatically evaluating the compliance of existing buildings with a set of rules by means of the application of Bayesian Networks. The main findings of this research led to the identification of relevant parameters to be used for that assessment; the re-use of those parameters to build a multi-criteria analysis tool; the identification of criteria and requirements to interface this decision tool with BIM models of the stock under consideration. A rough estimation of costs needed to refurbish those buildings that are not compliant, in order to include budget concerns, will be dealt with in the next research step. Finally, a preliminary application of the decision support system to evaluate two Italian school buildings – selected as case studies - will be reported.