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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.

AbstractFacility maintenance management (FMM) is essential for ensuring long-term values and to sustain project goals throughout the life cycle delivery process. However, in underdeveloped nations such as Ethiopia, facility maintenance management is an immature and underutilised process that requires a holistic intervention for practical improvement. The main aim of this study was to identify and prioritise critical factors that affect the effectiveness of FMM, with a focus on public universities in Ethiopia. Initially, a total of thirty-three (33) crucial variables were identified with a systematic literature review and desk study. To collect primary data, a survey research design approach was utilised using questionnaires and informant interviews. A total of seventy-five (75) data sets were obtained from 180 online surveys for conducting exploratory factor analysis (EFA). The outcome of the study revealed thirteen (13) critical attributes grouped into four factors that affect the effectiveness of facility maintenance management practises. The final four-factor model includes F1, internal processes and organisation; F2, community culture, learning, and growth; F3, impacts of design and construction quality; and F4, facility maintenance approach and management. This study indicated that facility maintenance management practises in public universities in Ethiopia are immature and require extensive enhancement. The identified influencing factors highlight the need for a comprehensive intervention to promote improved facility maintenance management practises and applications in Ethiopia. Further research is needed to analyse a wider range of attributes and data using confirmatory factor analysis.

The integration of BIM with other digital advancements has demonstrably led to an increase of performance in the Architecture, Engineering, Construction and Operation (AECO) industry. This integration not only is showing promising results in boosting the industry's performance, but also the productivity and promotes data-driven decision-making. Despite these benefits, there are limited studies that address the integration of BIM and digital data for managing built-assets in general and in developing countries in particular. To fill this gap, a closer assessment of current built-asset information management practice is necessary. The assessment of the practice examines how digital processes and/or technology can be seamlessly integrated into existing practices. In this regard, this study aims to provide valuable insights into increasing the maturity of built-asset information management by integrating digital data with BIM. The study uses a case-based research design using built-assets (universities in Addis Ababa) as public building representative to capture the prevailing information management practices in Ethiopian public buildings. The findings reveal that traditional paper-based practices still dominate the management of built-assets. This leads to difficulties in terms of data loss, unavailability, inaccuracy, and unreliability, all of which are detrimental to the overall performance. Based on these findings, a conceptual framework is designed to improve the performance of built-assets and help owners, end-users, and managers in defining data and information requirements for BIM-enabled asset information management. The framework delineates processes for structured information requirements definition and validation of various asset data from varying sources. The framework utilizes a BIM-enabled platform as a single source of truth and offers a comprehensive solution to the identified challenges. The findings of this study holds significant promise for improving the existing practice of built-asset information management within the study context.

AbstractThe purpose of this study is to develop a performance measurement model based on maturity dimensions or criteria and KPIs identified through an extensive literature review. Using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, a maturity model is developed by analyzing 12 criteria/dimensions and 51 KPIs. The importance of each criterion and KPI is analysed through focus group discussions with experts. A fuzzy method is used to resolve the fuzziness and uncertainty of the expert judgments. The fuzzy DEMATEL method is used to analyse the data, and the values of R+C and R-C are computed. In order to visualize the complex relationships, identify the most important criteria of building maintenance and analyse their mutual influence, a cause-effect diagram is created by mapping all sets of coordinates. Accordingly, C8 (culture and leadership), C1 (maintenance policy and strategy), C6 (design life), and C5 (maintenance information management) are assigned to the cause group. These four criteria are identified as the most critical criteria because they can influence the remaining criteria categorized in the effect group. For negative values of (R-C), C2, C3, C4, C7, C9, C10, C11, and C12 are categorized in the effect group. A five-level hierarchical model was developed to measure maturity performance based on the weighted average of the criteria and KPIs of the fuzzy DEMATE outcomes. The current model covers multiple measurement dimensions/perspectives as a measurement criterion for building maintenance maturity. The model is important for decision makers to identify weaknesses and strengths and incrementally improve the weaknesses in a continuous improvement approach. The new model can help decision makers determine the current state of maintenance maturity and plan actions to improve efficiency and effectiveness to create sustainable and productive building assets.