Optimal kernel extreme learning machine model for predicting the fracture state and impact response of laminated glass panels

摘要

The structural calculations of laminated glass (LG) under impact are commonly not available in sophisticated load scenarios such as consecutive impacts in typhoon or calculating the post-fracture response of LG. In this work, a flexible prediction model based on kernel extreme learning machine (KELM) was proposed for assessing the fracture state and characteristic impact response of LG under impact, which only used rough material properties and preliminary experimental data. Multiple design variables such as glass make-up, panel size, interlayer type and thickness, and support conditions were considered. Impact tests with consecutive impact attempts were first conducted. Then, a comprehensive database was established which include the fracture state of each glass layer and characteristic impact response, which encompasses peak impact force, contact duration and impact energy dissipation ratio. 567 groups of polyvinyl butyral (PVB) LG data and 210 groups of SentryGlas (R) (SG) LG data were finally recorded and collected for the database. An optimal KELM model was subsequently developed via introducing whale optimization algorithm (WOA). The modelling results were compared with those from two popular models, i.e. support vector machine (SVM) and least squares SVM (LSSVM) based models to identify both potential strengths and shortcomings. The results show that the proposed model has better performance in both prediction accuracy and computation cost than other examined models. The proposed model has the highest prediction accuracy of 88.45% in evaluating the fracture state, more than 86% in the characteristic impact response.