A hybrid model for predicting missile impact damages based on K-nearest neighbors and bayesian optimization

This study proposed a hybrid machine learning model which is based on k-nearest neighbors (KNN) and Bayesian optimization (BO), named as BOKNN, for predicting the local damages of reinforced concrete (RC) panels under missile impact loading. In the proposed BO-KNN, the hyperparameters of the KNN were optimized by using the BO which is a wellestablished optimization algorithm. Accordingly, the KNN was trained on an experimental dataset that consists of 254 impact tests to predict four levels (or classes) of damages including perforation, scabbing, penetration, and no damage.