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Research on Strength Prediction Model and Ratio Optimization of Concrete Damaged by Early Freezing
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Cun-dong XU1, 2, 3, Hui XU1, Jia-hao CHEN1, 2, Zhun LI1, 2, Zhi-hong ZHAO1, 2, Hai-ruo WANG1, Zi-hao REN1
Water Resources and Power | 2023, 41(3) : 144 - 148
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Water Resources and Power | 2023, 41(3): 144-148
• WATER CONSERVANCY AND HYDROPOWER ENGINEERING •
Research on Strength Prediction Model and Ratio Optimization of Concrete Damaged by Early Freezing
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Cun-dong XU1, 2, 3, Hui XU1, Jia-hao CHEN1, 2, Zhun LI1, 2, Zhi-hong ZHAO1, 2, Hai-ruo WANG1, Zi-hao REN1
Affiliations
  • 1.School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
  • 2.Key Laboratory for Technology in Rural Water Management of Zhejiang Province, Hangzhou 310018, China
  • 3.Henan Provincial Hydraulic Structure Safety Engineering Research Center, Zhengzhou 450046, China
Published: 2023-03-25 doi: 10.20040/j.cnki.1000-7709.2023.20220711
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In view of the problem that hydraulic concrete buildings in cold regions are vulnerable to early freezing injury during pouring construction and may affect the later healthy service, in order to study the strength damage law of early frozen concrete and optimize its ratio, Box-Behnken (RSM-BBD) response surface method was used to optimize the experimental design. The RSM response surface model was established by taking water binder ratio, fly ash content and air entraining agent content as variables. A GA-BPNN strength prediction model was constructed to predict the strength of early-frozen concrete accurately. Compared with the RSM model, the results show that the GA-BPNN has more accurate prediction performance and can optimize proportion design more efficiently. The goodness of fit R2 and average relative error eMRE by the GA-BPNN strength prediction model are 0.998 5 and 2.13%, respectively. The relative error between the predicted value of the optimal strength ratio and the experimental value is about 1%. The application of GA-BPNN strength prediction model can realize the efficient optimization of concrete freezing strength and its ratio.

response surface method  /  genetic optimization  /  BP neural network  /  ratio optimization  /  early frozen concrete
Cun-dong XU, Hui XU, Jia-hao CHEN, Zhun LI, Zhi-hong ZHAO, Hai-ruo WANG, Zi-hao REN. Research on Strength Prediction Model and Ratio Optimization of Concrete Damaged by Early Freezing[J]. Water Resources and Power, 2023 , 41 (3) : 144 -148 . DOI: 10.20040/j.cnki.1000-7709.2023.20220711
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Year 2023 volume 41 Issue 3
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20220711
  • Receive Date：2022-03-11
  • Online Date：2026-01-28
  • Published：2023-03-25
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History
  • Received：2022-03-11
  • Revised：2022-04-21
Funding
Affiliations
    1.School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    2.Key Laboratory for Technology in Rural Water Management of Zhejiang Province, Hangzhou 310018, China
    3.Henan Provincial Hydraulic Structure Safety Engineering Research Center, Zhengzhou 450046, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20220711
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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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