Enhancing strategic decision-making in built asset management through BIM-Enabled asset information modelling (AIM) for public buildings in Ethiopia: A fuzzy-AHP analysis

BIM-Enabled Asset Information Modelling (AIM) entails incorporating extensive data, known as big data, into digital platforms for informed decision-making. However, the lack of accurate and reliable data and the immaturity of BIM integration in existing buildings lead to operational phase performance inefficiencies due to inadequate data access. A strategic approach using BIM-enabled AIM is proposed to address these challenges, with the goal of enhancing data accessibility and adequacy for the operational team's performance. This study aims to develop a framework of information requirements that supports operational strategic decisions in asset portfolio management. To develop the framework, we employed a methodology that combines the Analytic Hierarchy Process (AHP) a structured technique for organizing and analysing complex decisions with fuzzy logic, which helps handle uncertainty in experts' judgments. A comprehensive questionnaire based on the Analytic Hierarchy Process (AHP) methodology was developed to gather expert insights on prioritizing information requirements, and it was administered to 11 experts selected for their diverse expertise in problem area. Cost, risk, and business value as selection criteria, while technical, managerial, financial, legal, and commercial categories of information are considered as alternatives in the AHP hierarchy. Utilizing the described methodology, fuzzy-AHP analysis revealed distinct variations in information requirements across the strategic decisions of maintain/keep, improve/adapt, and deconstruct/disassemble. For decisions on whether to maintain/keep buildings, the primary information requirement is managerial (39.6 %), followed by legal (20.7 %) and commercial (20 %), guiding strategic decisions. In contrast, improve/adapt decisions prioritize technical information (39 %), with financial (15.5 %) and legal (13.5 %) considerations also being significant. For the deconstruct/disassemble decisions, technical information requirements are most critical (55.5 %), followed by legal (16.6 %) and commercial (12.8 %) information. The findings highlight the need for tailored data generation strategies in existing buildings to address specific decision requirements, aiding in planning and resource allocation towards efficient AIM. The primary limitation of this study is its reliance on a small pool of 11 experts, which may limit the generalizability of the findings. Future research should aim to broaden the expert base to enhance the applicability and robustness of the results.