Journal of Information Processing Systems

  • 제18권4호
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  • Pages.457-469
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  • 2022
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Life Prediction of Hydraulic Concrete Based on Grey Residual Markov Model

  • Gong, Li (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Gong, Xuelei (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Liang, Ying (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Zhang, Bingzong (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Yang, Yiqun (School of Civil Engineering, Lanzhou Jiaotong University)
  • 투고 : 2022.01.24
  • 심사 : 2022.06.16
  • 발행 : 2022.08.31
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초록

Hydraulic concrete buildings in the northwest of China are often subject to the combined effects of low-temperature frost damage, during drying and wetting cycles, and salt erosion, so the study of concrete deterioration prediction is of major importance. The prediction model of the relative dynamic elastic modulus (RDEM) of four different kinds of modified concrete under the special environment in the northwest of China was established using Grey residual Markov theory. Based on the available test data, modified values of the dynamic elastic modulus were obtained based on the Grey GM(1,1) model and the residual GM(1,1) model, combined with the Markov sign correction, and the dynamic elastic modulus of concrete was predicted. The computational analysis showed that the maximum relative error of the corrected dynamic elastic modulus was significantly reduced, from 1.599% to 0.270% for the BS2 group. The analysis error showed that the model was more adjusted to the concrete mixed with fly ash and mineral powder, and its calculation error was significantly lower than that of the rest of the groups. The analysis of the data for each group proved that the model could predict the loss of dynamic elastic modulus of the deterioration of the concrete effectively, as well as the number of cycles when the concrete reached the damaged state.

키워드

과제정보

This study was the financially supported by the National Science Foundation (No. 51969011) of China, Gansu Provincial Science and Technology Program (No. 20JR10RA274, 21JR7RA301), and the Department of Education of Gansu Province (No. 2022CXZX-595 and 2022CXZX-603; "Innovation Star" Program of Excellent Graduate Student).

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