A BIM-based semantic approach for fund-allocation of building components

This paper articulates the fund allocation and prioritization challenge for existing building facilities' Maintenance and Repair (M&R) interventions. Despite the existing conspicuous research in funding allocation, there is a dearth of comprehensive insight into which facilities necessitate maintenance, when to intervene, and the strategies to adopt. Integrating Building Information Modeling (BIM) and Facility Management data for fund allocation decisions remains challenging, and existing models lack a thorough portrayal of as-is information for M&R interventions and funding allocation. To this end, this study proposes an integrated BIM-based fund allocation and prioritization framework to bridge these gaps. The framework incorporates as-is BIM semantic knowledge and utilizes a genetic algorithm optimization scheme to determine interventions based on condition state, future deterioration rate, and relative weighted importance. The research objectives include constructing an as-is BIM model, formalizing as-is knowledge using BIM standards, allocating funding efficiently, and determining optimum timing and type of interventions. The developed framework is enacted in a real-case scenario for validation and verification, and findings unveiled the proposed paradigm capabilities as a decision support system for fund allocation and prioritization, enabling facility managers to constitute informed decisions based on building facilities' as-is information, deterioration rates, and relative importance weight.