Enhancing facility maintenance using knowledge-based BIM systems

Abstract

The operation phase of a building's lifecycle refers to the period during which the building is in active use. So, to keep all these facilities working with acceptable performance, maintenance is necessary to keep the buildings working throughout their life cycle. Building maintenance and facility management are very complicated by traditional methods like personal estimation or manual inspections. On the other hand, the growth of technology, mainly in construction, helps in different fields of construction management. A big example of this technology is Building Information Modeling (BIM), a digital representation tool of buildings at various phases of the construction life cycle. To get more simplification, many commercial plugins for construction maintenance have been issued, which are integrated with the BIM model. Unfortunately, these plugins require specialized knowledge, are complicated, and need expertise, training, and more accuracy. In addition, the needed improvement in the maintenance process to overcome the challenges and obstacles that the organization is currently facing. This paper proposes a decision support system framework plugin to help facility management teams effectively plan, track, and allocate their maintenance budgets with a very simple, flexible, and easy-to-use system. The proposed framework creates maintenance schedules based on the facility's condition, type of equipment, a frequency of use, and allocates the necessary resources, parts, and labor required for the maintenance phase. It can also generate reports that show resource utilization, expenditure, and other cost-related indicators, making it easier for facility managers to plan their budgets and make informed decisions. The main idea of the proposed framework plugin that was created using Python is to use real-time data and predictive models to optimize maintenance plans, schedules, and resource allocation, facility management teams. The results show reduced downtime, minimized costs, and extended the lifespan of building assets within an actual case study. This framework plugin is an exemplary approach for facilities seeking to implement an innovative yet scalable Facility management (FM) optimum solution.