|University||Engineering Institute of Technology (EIT)|
|Subject||Review of BCI and FCI|
One of the critical elements to strategically managing a property portfolio involves the regular building condition assessment (BCA) and then documenting the results in a manner that is concise and consolidated. While the origins of the BCA were developed by the US Army Engineering Research and Development Centre, Construction Engineering Research Laboratory (ERDC-CERL), there remains, until today’s date, a lack of an international or industry best practice for the implementation of a successful program.
With a lack of an industry-recognized benchmark or process, this paper aims to identify whether there is consistency in how the BCA derives the Building Condition Index (BCI) and therefore draws into question its benefits or limitations. This research is significant in that it aims to highlight the gaps that may have been exacerbated by the implementation and utilization of computerized software in the strategic asset management industry and creates a foundation for future research which may begin the exploration of setting acceptable and recognized standards for assessing the condition of a building asset.
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Over the years, multiple researchers have invested in discussing and documenting the many different methodologies for the calculation of a concise and industrially recognized BCI, and as our modern society evolves and embraces the utilization of computerized software applications to assist with efficiencies and data management, further complexities and variations being to emerge and challenge the traditional BCI formula.
The Building Condition Index (BCI) remains an engineering performance-based metric developed based on recorded (normally visually) measured values that a building asset inspector would determine during a site inspection. These inspection observations result in an individual building component condition index score on a 0-100 scale.
It indicates the deteriorated condition of the component based on wear-and-tear, environmental exposure, occupational demands, and general equipment lifecycle. The individual component index scores are aggregated to a building level and frequently use a weighted average approach to emphasize critical components.
At this point, the complexities begin, as each component within a building asset (and there could be thousands of components within a building) has a reported level of deterioration and, conversely, a balance of remaining economic life.
A strategic asset management software program, or a Computerized Maintenance Management System (CMMS), consolidates the thousands of individual components. Depending on the construction materials, user-defined fields assign a maintenance intervention point.
Figure 01 demonstrates the impact of three different maintenance strategies, each having a different cost implication when maintenance is performed, depending on the assigned maintenance intervention value. The higher the intervention number, the lower the maintenance costs and it will occur more frequently. The lower the intervention (deterioration) value is, the less maintenance is performed, but it is more costly when maintenance is required.
In the above example, the sash window is defective, worn, or deteriorated; however, because it has a balance of remaining life, and the computer systems nominated maintenance intervention point may not have been triggered, the allocation of repairs may not be accurately reflected in the calculation of the annual BCI figure, as the repairs are pushed forward into a forward/ future years capital works program, or until such time that the deterioration of the component asset falls below the nominated pre-determined maintenance intervention point.
Further compounding the process, strategic management seeks to allocate financial resources (budgets) to the individual maintenance programs associated with the intervention points. Traditionally, the Facility Condition Index (FCI) was considered a separate and alternative calculation methodology, with no reference to a BCI; however, our current software applications use the same data to generate both the FCI and BCI.
The Facility Condition Index can be considered an economic rating based on the money required to perform maintenance, repair and replacement activities.  By utilizing the same data from the BCA (to determine the BCI), it is argued that it has the same foundation database characteristics that could contribute to the development of an FCI.
The objective of the FCI is to develop a numerical value based on an amalgamated allocated maintenance/ repair cost (monetary value) to return a building asset to its original (as-built) condition – in comparison with the building’s asset replacement value (ARV).
The FCI index generates a numerical value for a building asset that is applicable at a single point in time (or reporting year) – therefore, if the allocated budget for the repair of the building component is allocated in a forward capital works program (in the future) what portion of deteriorated asset monetary value is assigned to the reporting years figure?
In a majority of cases, the question regarding the BCI and FCI formula’s relevancy, conciseness, and accuracy using computerized asset management software requires significant investigation, validation, and documentation as the as-built and facility management industries pursue reliable and accurate strategic asset management tools is currently questionable, vague and ambiguous.
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