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
MENG Yanran1,2,3,WANG Xinger1,2,3,4,YANG Jian1,2,3(),XU Han1,2,3,YUE Feng1,2
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:
CLC Number:
MENG Yanran, WANG Xinger, YANG Jian, XU Han, YUE Feng. Research on Machine Learning Based Model for Predicting the Impact Status of Laminated Glass[J]. Journal of Inorganic Materials, 2021, 36(1): 61-68.
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 1 Configuration of laminated glass specimens
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 |
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 2 Test database material parameters
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 | - | - |
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 3 AUC value of the failure status prediction model
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 |
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 4 Simulation environment
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 |
Model | Computing time/ms | Training accuracy/% | Testing accuracy/% |
---|---|---|---|
WOA-KELM | 10.62 | 93.80 | 88.45 |
SVM | 367.87 | 92.80 | 87.00 |
LSSVM | 65.28 | 89.20 | 85.56 |
Table 5 Prediction results of glass failure status
Model | Computing time/ms | Training accuracy/% | Testing accuracy/% |
---|---|---|---|
WOA-KELM | 10.62 | 93.80 | 88.45 |
SVM | 367.87 | 92.80 | 87.00 |
LSSVM | 65.28 | 89.20 | 85.56 |
[1] | KAISER NATHAN D, BEHR RICHARD A, MINOR JOSEPH E , et al. Impact resistance of laminated glass using “sacrificial ply” design concept. Journal of Architectural Engineering, 2000,6(1):24-34. |
[2] | SAXE TIMOTHY J, BEHR RICHARD A, MINOR JOSEPH E , et al. Effects of missile size and glass type on impact resistance of “sacrificial ply” laminated glass. Journal of Architectural Engineering, 2002,8(1):24-39. |
[3] | WANG XING-ER, YANG JIAN, LIU QIANG , et al. Experimental investigations into SGP laminated glass under low velocity impact. International Journal of Impact Engineering, 2018,122:91-108. |
[4] | ZHANG XI-HONG, HAO HONG, MA GUO-WEI . Laboratory test and numerical simulation of laminated glass window vulnerability to debris impact. International Journal of Impact Engineering, 2013,55:49-62. |
[5] | CHEN SHUN-HUA, ZANG MENG-YAN, WANG DI , et al. Finite element modelling of impact damage in polyvinyl butyral laminated glass. Composite Structures, 2016,138:1-11. |
[6] | WANG XING-ER, YANG JIAN, LIU QING-FENG , et al. A comparative study of numerical modelling techniques for the fracture of brittle materials with specific reference to glass. Engineering Structures, 2017,152:493-505. |
[7] | MOHAGHEGHIAN IMAN, WANG Y, JIANG L , et al. Quasi-static bending and low velocity impact performance of monolithic and laminated glass windows employing chemically strengthened glass. European Journal of Mechanics-A/Solids, 2017,63:165-186. |
[8] | ALTER CHRISTIAN, KOLLING STEFAN, SCHNEIDER JENS . An enhanced non-local failure criterion for laminated glass under low velocity impact. International Journal of Impact Engineering, 2017,109:342-353. |
[9] | ZHANG YANG-MEI, WANG XING-ER, YANG JIAN . Experimental study of multiple layered SGP laminated glass under hard body impact. Journal of Inorganic Materials, 2018,33(10):1110-1118. |
[10] | WANG XING-ER, YANG JIAN, WANG FEILIANG , et al. Simulating the impact damage of laminated glass considering mixed mode delamination using FEM/DEM. Composite Structures, 2018,202:1239-1252. |
[11] | LIU XIAO-GEN, BAO YI-WANG, SONG YI-LE , et al. The calculation method The calculation method and influence factor to the natural frequency of laminated glass. Bulletin of the Chinese Ceramic Society, 2008, (27) 5:918-923. |
[12] | DAS SANTANU, SRIVASTAVA ASHOK N, CHATTOPADHYAY ADITI . Classification of Damage Signatures in Composite Plates using One-class SVMs. 2007 IEEE Aerospace Conference. MT, USA. 2007: 1-19. |
[13] | LI HONG-NAN, GAO DONG-WEI, YI TING-HUA . Advances in structural health monitoring systems in civil engineering. Advances in Mechanics, 2008(02):151-166. |
[14] | JIANG ZHEN-YU, ZHANG ZHONG, FRIEDRICH KLAUS . Prediction on wear properties of polymer composites with artificial neural networks. Composites Science and Technology, 2007,67(2):168-176. |
[15] | CHATTERJEE SANKHADEEP, SARKAR SARBARTHA, HORE SIRSHENDU , et al. Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing and Applications, 2017,28(8):2005-2016. |
[16] | XU HAN, YANG JIAN, WANG XING-ER , et al. Application of back propagation neural network on debonding prediction of glass curtain walls with concealed frames. Journal of the Chinese Ceramic Society, 2019,47(8):1073-1079. |
[17] | SHENG MING-JIAN, CHEN PU-HUI, QIAN YI-BIN . An estimating method of compressive strength of composite laminates after low-velocity impact. Journa of Shanghai Jiao Tong University, 2019,53(10):1182-1186. |
[18] | ZHANG YONG-ZHEN, TONG XIAO-YAN, YAO LEI-JIANG , et al. Acoustic emission pattern recognition on tensile damage process of C/SiC composites using an improved genetic algorithm. Journal of Inorganic Materials, 2020,35(5):593-600. |
[19] | HUANG GUANG-BIN, DING XIAO-JIAN, ZHOU HONG-MING . Optimization method based extreme learning machine for classification. Neurocomputing, 2010,74(1/2/3):155-163. |
[20] | MIRJALILI SEYEDALI, LEWIS ANDREW . The whale optimization algorithm. Advances in Engineering Software, 2016,95:51-67. |
[21] | VEER FA, LOUTER PIETER CHRISTIAAN, BOS FP . The strength of annealed, heat-strengthened and fully tempered float glass. Fatigue & Fracture of Engineering Materials & Structures, 2009,32(1):18-25. |
[22] | FAWCETT TOM . An introduction to ROC analysis. Pattern Recognition Letters, 2006,27(8):861-874. |
[23] | HUANG J, LU J, LING C X. Comparing Naive Bayes, Decision trees, and SVM with AUC and Accuracy. Third IEEE International Conference on Data Mining. Melbourne, FL, USA, USA. 2003: 553-556. |
[24] | BARAKAT N, BRADLEY A P . Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve. 18th International Conference on Pattern Recognition (ICPR'06). 2006: 812-815. |
[25] | ZHANG X, JIANG C . Improved SVM for Learning Multi-Class Domains with ROC Evaluation. 2007 International Conference on Machine Learning and Cybernetics. Hong Kong, China. 2007: 2891-2896. |
[26] | TESFAMARIAM SOLOMON, LIU ZHENG . Earthquake induced damage classification for reinforced concrete buildings. Structural Safety, 2010,32(2):154-164. |
[27] | GUI GUO-QING, PAN HONG, LIN ZHI-BIN, et al. Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection. KSCE Journal of Civil Engineering, 2017,21(2):523-534. |
[28] | HUANG GUANG-BIN, WANG DIAN HUI, LAN YUAN . Extreme learning machines: a survey. International. Journal of Machine Learning and Cybernetics, 2011,2(2):107-122. |
[29] | HO SL, YANG SHI-YOU, NI GUANG-ZHENG , et al. A particle swarm optimization-based method for multiobjective design optimizations. IEEE Transactions on Magnetics, 2005,41(5):1756-1759. |
[30] | YANG XI-YUN, GUAN WEN-YUAN, LIU YU-QI , et al. Prediction intervals forecasts of wind power based on PSO-KELM. Proceedings of the CSEE, 2015,35(S1):146-153. |
[1] | SHI Siqi, SUN Shiyu, MA Shuchang, ZOU Xinxin, QIAN Quan, LIU Yue. Detection Method on Data Accuracy Incorporating Materials Domain Knowledge [J]. Journal of Inorganic Materials, 2022, 37(12): 1311-1320. |
[2] | JIAO Zhixiang, JIA Fanhao, WANG Yongchen, CHEN Jianguo, REN Wei, CHENG Jinrong. Curie Temperature Prediction of BiFeO3-PbTiO3-BaTiO3 Solid Solution Based on Machine Learning [J]. Journal of Inorganic Materials, 2022, 37(12): 1321-1328. |
[3] | ZHANG Ruihong, WEI Xin, LU Zhanhui, AI Yuejie. Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning [J]. Journal of Inorganic Materials, 2021, 36(11): 1178-1184. |
[4] | ZHANG Yang-Mei, WANG Xing-Er, YANG Jian, LIU Qing-Feng, LIU Xin-Wei. Experimental Study of Multiple Layered SGP Laminated Glass under Hard Body Impact [J]. Journal of Inorganic Materials, 2018, 33(10): 1110-1118. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||