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Decision making for the sustainable management of community buildings in Australia using fuzzy logic
conference contributionposted on 03.02.2020, 00:00 by Pushpitha KalutaraPushpitha Kalutara, G Zhang, S Setunge, R Wakefield, H Mohseni
© The Hong Kong Polytechnic University. Community buildings serve a large part of built environment in Australia. Due to the fact of ageing and lack of maintenance, degradation rate of these assets has been increasing in recent years. This phenomenon and the non-existence of a reliable asset management tool in the current practice of building management have assured a necessity of developing a decision making model to address those issues to achieve sustainable management of community buildings. The majority of decisions made in community building management appeared to be inconsistent and subjective. The aim of this paper is to establish an analytical model to minimize this inconsistency and subjectivity to be precise enough through introducing logic into encapsulating and addressing this uncertain nature. Fuzzy logic has been introduced to deal with this uncertainty and finally develop the model. Based on renewal and maintenance actions decision making has been considered in four main aspects including environmental, economic, social and functional related issues. Then it will be further explored to ascertain critical parameters influencing to those main factors. Expert opinion will be used to finalise these critical parameters through questionnaire surveys. The process is running in two different fuzzy applications consisting of analytical hierarchical process (AHP) and fuzzy inference system (FIS). AHP is used to measure the aggregate impact of critical parameters. The overall impact on decision making by its four main factors is evaluated using either FIS or AHP. A criticality index has been developed to support the decision making, and future actions will be planned accordingly. Example calculations and case study data have been demonstrated throughout the paper to showcase and validate the model.