Condition and Reliability Prediction Models Using the Weibull Probability Distribution

Key to a successful building asset management plan is the ability to measure current condition and predict future condition and degradation trends over a specified planning horizon for each and every individual component-section present in a building. However, this ability poses a difficult challenge because of the vast array of different components, their material make-up, and type found in a building and because each has a different expected life and degradation curve. In addition, condition trends and service lives depend on the amount of preventative and corrective maintenance, including repair, invested during a component-section's lifecycle. Because of these variables, it is difficult to accurately project a condition-lifecycle trend for each individual component-section without a periodic inspection and a meaningful condition metric for measuring condition. This condition metric provides data for the lifecycle prediction process. However, condition data are most often very limited for any given component-section. This paper addresses the use of the Weibull probability distribution function with the data collected during component-section inspections to predict lifecycle condition and reliability over time. The prediction model is self-correcting using attribute information collected during both current and historical inspections to accurately project the unique lifecycle degradation trend for an individual component-section in a building.