PROBABILISTIC PREDICTION OF RETROFIT COST FOR MASONRY STRUCTURES

A probabilistic model is developed that predicts the retrofit cost for masonry buildings. This model is key in the prioritization of vulnerable structures for seismic retrofit. A database of real data for school buildings in Iran is compiled and the Bayesian regression modeling methodology is employed to establish the model. This modeling approach emphasizes the comprehensive characterization and modeling of epistemic uncertainties. As a result, the model is updated as new data become available. The explanatory functions of the model are selected in accordance with the mechanics of the problem. An important explanatory function is the amount of increase in lateral strength of buildings due to retrofit, which is accounted for in such a model for the first time. The paper presents the data collection, model development, insights obtained from the model, and concludes with a numerical example.