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Prediction of Original Ingredients of Portland Glass and Research into Subclassification Methods Based on Machine Learning
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Zhihao WANG1, Xingwei ZHAO1, Zhiqun LI1, Ming GUO1, Wanyue XIAO1, Zhijan LIU2
Journal of the Chinese Ceramic Society | 2023, 51(2) : 416 - 426
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Journal of the Chinese Ceramic Society | 2023, 51(2): 416-426
• Research Articles •
Prediction of Original Ingredients of Portland Glass and Research into Subclassification Methods Based on Machine Learning
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Zhihao WANG1, Xingwei ZHAO1, Zhiqun LI1, Ming GUO1, Wanyue XIAO1, Zhijan LIU2
Affiliations
  • 1.Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China
  • 2.Marine Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China
  • WANG Zhihao (2001-), male, Undergraduate. E-mail:

About Author:

WANG Zhihao (2001-), male, Undergraduate. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220985
Outline
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Glass as a material has existed in China for a long time, but the related studies on ancient glass in China started relatively late due to the long-term confusion of name and texture, leading to a lack of research on the weathering and composition of ancient silicate glass. Some previous studies on ancient glass mainly discussed the artistic character and development laws of glass with respect to cultural exchange and chemical analysis from the perspective of dynastic succession. A few work established the related mathematical model and used the intelligent algorithm for qualitative quantification of weathering silicate glass original composition prediction and subclassification method. This paper was to use multiple groups of weathered and unweathered silicate glasses and collect/extract the data on their chemical composition content, ornamentation and color. The relations among the patterns, color, types of glass and surface weathering were analyzed by the Spearman coefficient. The decision tree for a rough classification and neural network to predict the main chemical composition of glass before its weathering was given, and the classification basis of silicate glass was summarized. Besides, the subcategorization at the optimal quantity of categories to conduct subclass classification was established, and a reasonable amount of barium glass and high potassium glass was selected. The results show that the type of glass has an influence on the surface weathering, and there are silicon dioxide, aluminum oxide, lead oxide, barium oxide and phosphorus pentoxide involved in the weathering process. Moreover, the amount of silicon dioxide decreases and lead oxide increases sharply in lead barium glass, while vice versa in high potassium glass after weathering.

Portland glass  /  decision tree  /  neural network  /  K-means clustering  /  Spearman coefficient
王祉皓, 赵芗溦, 李智群, 郭明, 肖琬玥, 刘志坚. 基于机器学习的风化硅酸盐玻璃原成分预测及亚分类方法. 硅酸盐学报, 2023 , 51 (2) : 416 -426 . DOI: 10.14062/j.issn.0454-5648.20220985
Zhihao WANG, Xingwei ZHAO, Zhiqun LI, Ming GUO, Wanyue XIAO, Zhijan LIU. Prediction of Original Ingredients of Portland Glass and Research into Subclassification Methods Based on Machine Learning[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 416 -426 . DOI: 10.14062/j.issn.0454-5648.20220985
Year 2023 volume 51 Issue 2
33
6
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Article Info
doi: 10.14062/j.issn.0454-5648.20220985
  • Receive Date:2022-11-15
  • Online Date:2026-10-20
  • Published:2023-02-20
Article Data
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History
  • Received:2022-11-15
  • Revised:2022-12-13
Funding
Affiliations
    1.Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China
    2.Marine Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China

Corresponding:

LIU Zhijian (1986-), male, Master, Associate Professor. E-mail:
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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