Research on Machine Learning Based Model for Predicting the Impact Status of Laminated Glass

Architectural laminated glass exhibits significant vulnerability under hard body impacts such as windborne debris impacts. In this work, a prediction model is proposed for assessing the impact status of laminated glass under hard body impact. Multiple design variables including the glass make-ups, interlayer types, support conditions and size are considered. The impact tests with consecutive impact attempts are first conducted. A comprehensive database encompassing the failure condition of each glass layer is then established. This database has 567 groups of PVB laminated glass data and 210 groups of SGP laminated glass data. A combined WOA-KELM machining learning based model is subsequently developed to predict the impact status of laminated glass. The modelling results are compared with that from SVM and LSSVM based models. The results show that the proposed model