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Development on Machine Learning for Durability Prediction of Concrete Materials
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Xiao LIU1, 2, Simai WANG1, 2, Lei LU1, 2, Meizhu CHEN3, Yue ZHAI1, 2, Suping CUI1, 2
Journal of the Chinese Ceramic Society | 2023, 51(8) : 2062 - 2073
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Journal of the Chinese Ceramic Society | 2023, 51(8): 2062-2073
• Review •
Development on Machine Learning for Durability Prediction of Concrete Materials
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Xiao LIU1, 2, Simai WANG1, 2, Lei LU1, 2, Meizhu CHEN3, Yue ZHAI1, 2, Suping CUI1, 2
Affiliations
  • 1.National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
  • 2.Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
  • 3.State Key Laboratory of Silicate Materials for Architectures, Wuhan University of Technology, Wuhan 430070, China

    • LIU Xiao (1983-), male, Ph.D., Professor. E-mail:

About Author:

LIU Xiao (1983-), male, Ph.D., Professor. E-mail:

Published: 2023-05-05 doi: 10.14062/j.issn.0454-5648.20220973
Outline
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The durability evaluation of concrete materials based on the related experiments has a low economic efficiency ratio. The prediction accuracy of the conventional empirical formula for durability is restricted and the proportions of concrete mix cannot be calculated according to the performance. It is thus necessary to develop a novel and efficient material quality control and performance prediction tool. In this review, the process of building machine learning models was stated. The basic working flow and advantages of common algorithms, as well as the durability index prediction algorithms based on machine learning were summarized. Its application effects and development direction were discussed. This review can provide a basis for the in-depth development and application of machine learning technology in the field of concrete.

concrete  /  durability  /  machine learning  /  algorithm  /  prediction
刘晓, 王思迈, 卢磊, 陈美祝, 翟跃, 崔素萍. 机器学习预测混凝土材料耐久性的研究进展. 硅酸盐学报, 2023 , 51 (8) : 2062 -2073 . DOI: 10.14062/j.issn.0454-5648.20220973
Xiao LIU, Simai WANG, Lei LU, Meizhu CHEN, Yue ZHAI, Suping CUI. Development on Machine Learning for Durability Prediction of Concrete Materials[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (8) : 2062 -2073 . DOI: 10.14062/j.issn.0454-5648.20220973
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Year 2023 volume 51 Issue 8
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Article Info
doi: 10.14062/j.issn.0454-5648.20220973
  • Receive Date：2022-11-11
  • Online Date：2026-10-19
  • Published：2023-05-05
Article Data
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History
  • Received：2022-11-11
  • Revised：2022-12-10
Funding
Affiliations
    1.National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
    2.Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
    3.State Key Laboratory of Silicate Materials for Architectures, Wuhan University of Technology, Wuhan 430070, China
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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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