Journal of Inorganic Materials ›› 2021, Vol. 36 ›› Issue (1): 61-68.DOI: 10.15541/jim20200187

Special Issue: 【虚拟专辑】气凝胶,玻璃(2020~2021) 【虚拟专辑】计算材料

Previous Articles     Next Articles

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

MENG Yanran1,2,3,WANG Xinger1,2,3,4,YANG Jian1,2,3(),XU Han1,2,3,YUE Feng1,2   

  1. 1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    3. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    4. Key Laboratory of Impact and Safety Engineering, Ministry of Education, Ningbo University, Ningbo 315211, China
  • Received:2020-04-09 Revised:2020-05-22 Published:2021-01-20 Online:2020-06-15
  • About author:MENG Yanran(1996-), female, Master candidate. E-mail: yrmeng@sjtu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFC0806100);National Natural Science Foundation of China(51908352);Project of Key Laboratory of Impact and Safety Engineering(Ningbo University, Ministry of Education)(CJ201906)

Abstract:

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.

Key words: laminated glass, impact failure, machine learning, kernel extreme learning machine

CLC Number: