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SCC Performance Prediction Technology Based on Deep Learning
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Shi-qin HE1, Peng-fei GAO1, Chun-yue WANG2, Hui WANG1
Water Resources and Power | 2023, 41(4) : 155 - 158
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Water Resources and Power | 2023, 41(4): 155-158
• WATER CONSERVANCY AND HYDROPOWER ENGINEERING •
SCC Performance Prediction Technology Based on Deep Learning
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Shi-qin HE1, Peng-fei GAO1, Chun-yue WANG2, Hui WANG1
Affiliations
  • 1.School of Civil Engineering, North China University of Technology, Beijing 100144, China
  • 2.R & D Department, Beijing Zhongtuo Xinyuan Technology Co., Ltd., Beijing 102206, China
Published: 2023-04-25 doi: 10.20040/j.cnki.1000-7709.2023.20221152
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The deep learning technology was used to study a method of predicting the performance of SCC based on the mixture image information during the mixing process. Twenty-five sets of videos of the SCC mixing process with different performances were recorded. According to the slump flow and T500 measured values and combined with the visual inspection, the SCC mixes were classified into three performances: qualified, insufficient fluidity and segregation. By processing the videos into image sets, the deep learning models were built using image classification and target detection respectively. The models learn and train the image features of the mixes to realize the prediction of SCC performance. The results show that both image classification and target detection methods can achieve more than 98% accuracy on the validation set, which provides a reference for adjusting the mix proportion in real-time and realizing the smart production of SCC.

self-compacting concrete  /  performance  /  deep learning  /  target detection  /  image classification
Shi-qin HE, Peng-fei GAO, Chun-yue WANG, Hui WANG. SCC Performance Prediction Technology Based on Deep Learning[J]. Water Resources and Power, 2023 , 41 (4) : 155 -158 . DOI: 10.20040/j.cnki.1000-7709.2023.20221152
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Year 2023 volume 41 Issue 4
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221152
  • Receive Date：2022-05-30
  • Online Date：2026-01-27
  • Published：2023-04-25
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  • Received：2022-05-30
  • Revised：2022-06-28
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Affiliations
    1.School of Civil Engineering, North China University of Technology, Beijing 100144, China
    2.R & D Department, Beijing Zhongtuo Xinyuan Technology Co., Ltd., Beijing 102206, China
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Citations
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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