Probabilistic Model Based on Bayesian Model Averaging for Predicting the Plastic Hinge Lengths of Reinforced Concrete Columns

A probabilistic model is devised for predicting the plastic hinge lengths (PHLs) of RC columns. Seven existing parametric models are evaluated first using a comprehensive database comprising PHL measurements from 133 column tests. It observed that performances these seven fair (as opposed to strong), and their predictions bear significant uncertainties. novel technique combine them into weighted-average supermodel wherein weights determined via Bayesian inference. This approach naturally produces weights’ statistical moments, thus, resulting one amenable performance-based seismic design assessment analyses. Prediction comparisons indicate proposed has higher performance than all prior models. The new easily expandable should more test data become available.