Two condition indicators for building components based on reactive‐maintenance data

Purpose Sustaining the safety and operability of the civil infrastructure assets, including buildings, is a complex undertaking that requires a perpetual cycle involving inspection, and further decisions for renewal fund allocation. Inspection, which is the basis for all subsequent decisions, however, is subjective, costly, and time‐consuming. To circumvent inspection problems, this paper aims to develop indicators of the condition of building components, without inspection, using reactive‐maintenance data. Design/methodology/approach For that purpose, sample reactive‐maintenance data of 88 schools are obtained from the Toronto District School Board in Canada. The data are then analysed to identify two condition indicators for building components: the number of reactive‐maintenance work orders per year; and the cost of reactive‐maintenance work orders per year. The analysis then identifies threshold values that differentiate the good, fair, poor, and critical conditions of components. Accordingly, a condition prediction system has been developed and discussed in this paper. Findings The system has great potential benefits in saving the time and cost associated with indiscriminate inspections, and in providing accurate and timely data for asset renewal decisions. Originality/value The paper introduces an essential component of a comprehensive framework for building asset management: condition prediction and inspection planning.