A decision support system for the maintenance management of buildings

Governing agencies across North America are currently facing extreme problems of deterioration, inadequacy, and insufficiency of their city's infrastructure facilities. The shortage of adequate funds has imposed serious constraints on how candidate maintenance, rehabilitation and replacement projects are planned, and executed. Asset managers are forced to compete with other cost centers to obtain the required funding for these renewal projects. Consequently, many urban infrastructure management agencies are falling behind in their efforts to improve and enhance the performance of their facilities.

To effectively operate and manage a portfolio of diverse facilities it is essential to have a firm grasp on the asset base, to monitor the in-situ performance over its service life, and to know the operational and functional requirements as well as the financial implications. Further, asset managers must be able to execute maintenance, rehabilitation and replacement strategies based on perceived economic advantage and prudence, while reflecting management's strategic plans for the facility. Given the complex nature of the building's structure and make-up, with its intricate interconnection of systems and components, it is imperative for asset managers to be able to closely monitor the performance of each building asset, and set priorities to the large number of projects and select the ones that are most feasible given the funds that are available and the maximization of benefits to the facility.

A Building Maintenance Decision Support System (BMDSS) has been developed to assist asset managers to monitor and model the deterioration of buildings (including their many systems and components), to forecast the remaining service life of components, and to prioritize building systems and components. It utilizes the detailed inspections performed at the lowest level of the building hierarchy, and employs a roll-up procedure to determine the condition rating of the building. Further, the developed BMDSS combines available condition rating data, regression trend of the designed service life, expert knowledge, and Markov transition properties to predict the future of buildings performance. The BMDSS also provides the framework for prioritizing MR&R projects based on the financial allocation that leads to maximum benefits within the framework of a defined budget.