<trans-title>Research on Machine Learning Based Model for Predicting the Impact Status of Laminated Glass</trans-title>

Yanran MENG; Xinger WANG; Jian YANG; Han XU; Feng YUE

doi:10.15541/jim20200187

Journal of Inorganic Materials, Volume. 36, Issue 1, 61(2021)

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

Yanran MENG^1,2,3, Xinger WANG^1,2,3,4, Jian YANG^1,2,3、*, Han XU^1,2,3, and Feng YUE^1,2

Author Affiliations

¹School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

²State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

³Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

⁴Key Laboratory of Impact and Safety Engineering, Ministry of Education, Ningbo University, Ningbo 315211, China

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Figures & Tables(15)

1. Diagram of impact tests equipment

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2. ROC curves of single input parameter (a) Outer layer; (b) Inner layer

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3. ROC curves of integrated input parameters (a) Outer layer; (b) Inner layer

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4. The network structure of KELM

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5. WOA optimization process

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6. WOA-KELM flow chart

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7. WOA optimization process

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8. WOA-KELM glass failure status prediction results

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9. SVM glass failure status prediction results

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10. LSSVM glass failure status prediction results

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Table 1.
夹层玻璃试件配置情况

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Table 1.
夹层玻璃试件配置情况

ID	Material	Make-up (o/m/i)	Dimensional of glass/mm	Support condition	Quantity
P01	FTG/PVB/FTG	8/1.52/8	1000 × 1000	Edge clamped	12
P02	FTG/PVB/FTG	8/1.52/8	1000 × 1000	Bolted connection	6
P03	FTG/PVB/FTG	8/1.52/8	1500 × 1500	Edge clamped	3
P04	FTG/PVB/FTG	8/1.52/8	1500 × 1500	Bolted connection	3
P05	HSG/PVB/HSG	8/1.52/8	1000 × 1000	Bolted connection	3
P06	FTG/PVB/FTG	8/0.76/8	1000 × 1000	Bolted connection	3
P07	FTG/PVB/FTG	8/3.04/8	1000 × 1000	Bolted connection	3
P08	FTG/PVB/FTG	8/1.52/10	1000 × 1000	Bolted connection	3
P09	FTG/PVB/FTG	6/1.52/10	1000 × 1000	Bolted connection	3
P10	FTG/PVB/HSG	8/1.52/8	1000 × 1000	Bolted connection	3
P11	HSG/PVB/FTG	8/1.52/8	1000 × 1000	Bolted connection	3
S01	ANG/SGP/FTG	8/3/8	1000 × 1000	Edge clamped	3
S02	FTG/SGP/ANG	8/3/8	1000 × 1000	Edge clamped	3
S03	FTG/SGP/FTG	8/3/8	1000 × 1000	Bolted connection	6
S04	FTG/SGP/FTG	8/3/8	1500 × 1500	Bolted connection	3
S05	HSG/SGP/FTG	8/3/8	1000 × 1000	Bolted connection	3
S06	FTG/SGP/FTG	8/5/8	1000 × 1000	Bolted connection	3

Table 2.
试验数据库材料参数
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Table 2.
试验数据库材料参数
Material parameter FTG HSG ANG PVB SGP
Density/(kg·m^-3) - 2500.0 - 1100.00 950.00
Elasticity modulus/GPa - 70.0 - 0.15 0.30
Poission ratio - 0.2 - 0.45 0.45
Mean failure strength/MPa 157.4 104.0 42.0 - -

Table 3.
破坏状态预测模型AUC值

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Table 3.
破坏状态预测模型AUC值

n	Input parameter	Outer layer state AUC (Ao n)	Inner layer state AUC (Ai n)
1	Thickness of interlayer	0.605	0.571
2	Thickness of outer layer	0.516	0.537
3	Thickness of inner layer	0.511	0.546
4	Type of interlayer	0.573	0.576
5	Type of outer layer	0.573	0.515
6	Type of inner layer	0.563	0.559
7	Side length	0.516	0.507
8	Boundary condition	0.541	0.573
9	Peak kinetic energy	0.654	0.675
10	State of outer layer	0.873	0.513
11	State of inner layer	0.472	0.714
12	Multiple input	0.916	0.842

Table 4.
仿真环境
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Table 4.
仿真环境
Item Detailed settings
Hardware
CPU Quad-core intel core i7-4850HQ
Frequency 2.3 GHz
RAM 16GB 1600 MHz DDR3
Hard drive 500 GB
Operating system MacOS

Table 5.
玻璃破坏状态预测结果
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Table 5.
玻璃破坏状态预测结果
Model Computing time/ms Trainingaccuracy/% Testingaccuracy/%
WOA-KELM 10.62 93.80 88.45
SVM 367.87 92.80 87.00
LSSVM 65.28 89.20 85.56

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Yanran MENG, Xinger WANG, Jian YANG, Han XU, Feng YUE. Research on Machine Learning Based Model for Predicting the Impact Status of Laminated Glass[J]. Journal of Inorganic Materials, 2021, 36(1): 61

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