基于机器学习算法的夹层玻璃冲击破坏预测模型研究

在诸如风致飞射物撞击等刚体冲击作用下, 建筑夹层玻璃因自身脆性特征极易破坏。针对这个问题提出了在刚体冲击下夹层玻璃破坏状态的预测方法, 综合考虑了玻璃构型、中间胶层、支撑条件及尺寸等多种设计参数。首先针对多类夹层玻璃进行往复刚体冲击试验, 建立567组PVB及210组SGP的两种不同中间胶层的夹层玻璃试验数据库; 随后基于鲸鱼优化下的核极限学习机(WOA-KELM)机器学习算法, 建立夹层玻璃破坏状态的预测模型, 并与支持向量机(Support Vector Machine, SVM)及最小二乘支持向量机(Least Squares Support Vector Machine, LSSVM)建立的相应预测模型进行对比分析。结果表明, WOA-KELM模型破坏状态预测精度达88.45%, 能较好地预测夹层玻璃的破坏, 满足工程应用的需求, 且预测模型精度及实时性均优于其他模型。

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 has a prediction accuracy of 88.45%in failure status of each glass layer.Such model can well predict the impact status of laminated glass and shows better performance in both accuracy and computation cost than other models.