Abstract
Building envelope management includes all activities, e.g., periodic inspections, condition assessment and maintenance, needed to preserve the integrity of the building exterior. In building envelope management, appropriate decision-making focuses on finding an optimum inspection interval and repair strategy. A Bayesian updating with rating method is presented for conducting planning and decision-making for building envelop inspections using data from previous inspections. The model applies to a zone (strip) of the envelope and uses the previous inspection records to predict the future condition. An artificial neural network (ANN) is used to improve the prediction for the envelope condition. The ANN uses historical data of an envelope condition to determine a correction factor, for the predicted condition, to account for the effect of factors that promote damage. The Bayesian updating will improve the capabilities of the model in predicting the future conditions as more data becomes available. Used along with a cost optimization and reserve analysis, the model will result in a more sustainable and cost-effective building envelope management process.
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