Abstract

Facilities are a major component of colleges and universities. They play a significant role in sustaining the quality of life for students, faculty, and staff. The physical inventory supports the learning areas, living quarters, research spaces, and work areas. Facilities require capital throughout their life cycles to maintain functionality and provide adequate service for their users. Presently, the rate of deterioration in United States' higher education facilities is increasing due to constraint budgets and the accumulation of deferred maintenance. Facility performance is considerably influenced by how well the project is planned, developed, designed, and constructed; considerations of maintenance requirements during the pre-construction phase may lower the operations and maintenance cost of the facility. Therefore, it is important to take a proactive approach to plan and design better facilities. This paper presents the preliminary results of a research that focuses on the knowledge discovery in maintenance databases. Applying the knowledge discovery process to higher education maintenance databases can help to discover knowledge on the parameters that increase or decrease maintenance cost, find patterns in building systems interaction and their environment, and identify the influence of planning and design on operations and maintenance. The case study illustrates one type of data-mining goals on a facility condition assessment database. It shows the clustering of building deficiencies and how they are unique to their relative building type.

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