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Review on Machine Learning for Predicting Concrete Properties
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Xiang-qing KONG1, 2, Ming-liang ZHANG1, *, Ran KANG1, Yuan-hao ZHAO1
Science Technology and Engineering | 2025, 25(5) : 1764 - 1773
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Science Technology and Engineering | 2025, 25(5): 1764-1773
• Surveies·Architectural Science •
Review on Machine Learning for Predicting Concrete Properties
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Xiang-qing KONG1, 2, Ming-liang ZHANG1, *, Ran KANG1, Yuan-hao ZHAO1
Affiliations
  • 1 School of Civil Engineering, Liaoning University of Technology, Jinzhou 121001, China
  • 2 College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao 266580, China
Published: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2402999
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Under the guidance of the “14th Five-Year Plan” and the “Dual Carbon” goals, construction materials face significant challenges, particularly as the adaptability and accuracy of traditional concrete performance prediction models are questioned. Recently, machine learning (ML) has demonstrated high accuracy and efficiency in predicting concrete performance. The research progress of ML in this field was systematically reviewed, focusing on its applications in mechanical properties, mix design, and durability, while identifying its limitations and proposing improvement strategies. CiteSpace software was used to analyze the current state of ML research in construction engineering, examining publication volume, research hotspots, and trends. This analysis offers valuable reference for future researchers, aiding in the effective application of ML technology to drive innovation in construction materials and support environmental sustainability goals.

concrete  /  machine learning  /  performance prediction  /  visual analytics  /  CiteSpace
Xiang-qing KONG, Ming-liang ZHANG, Ran KANG, Yuan-hao ZHAO. Review on Machine Learning for Predicting Concrete Properties[J]. Science Technology and Engineering, 2025 , 25 (5) : 1764 -1773 . DOI: 10.12404/j.issn.1671-1815.2402999
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Year 2025 volume 25 Issue 5
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Article Info
doi: 10.12404/j.issn.1671-1815.2402999
  • Receive Date：2024-04-23
  • Online Date：2025-07-29
  • Published：2025-02-18
Article Data
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History
  • Received：2024-04-23
  • Revised：2024-11-18
Funding
Affiliations
    1 School of Civil Engineering, Liaoning University of Technology, Jinzhou 121001, China
    2 College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao 266580, 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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