Condition Detection and Maintenance of Steel-Fiber-Reinforced Concrete Based on Markov Random Matrix

Abstract

The condition detection and maintenance of steel-fiber-reinforced concrete (SFRC) involves a combination of qualitative and quantitative assessment methods, while the ineffectiveness of the Markov chain can analyze the fluctuation law of gray fitting accuracy indexes and use this to correct the detection results of the residual gray model, having outstanding advantages. On the basis of summarizing and analyzing previous works of literature, this study expounded the research status and significance of the condition detection and maintenance of SFRC, elaborated the development background, current status, and future challenges of the Markov random matrix, introduced the methods and principles of feature extraction network and loss function, proposed the detection process analysis of SFRC based on Markov random matrix, constructed a detection model of SFRC, analyzed the maintenance monitoring method of SFRC based on Markov random matrix, discussed the maintenance effect evaluation of SFRC, and finally carried out a simulation experiment and its result analysis. The results show that the most important feature of Markov random matrices is the absence of aftereffects, which means that the condition evolution of SFRC can be regarded as a multiple Markov chain. When carrying out the dynamic condition maintenance of SFRC, the maintenance object should be first determined, then the condition detection of the object should be carried out to obtain information characteristics and to assess its condition, and then the condition should be compared with the condition set to determine its position in the set to make fault prediction based on the Markov chain constructed from the set of conditions. Under other excepted standards and maintenance conditions with the increase of steel fiber content, the flexural strength of concrete decreased first and then increased, but the difference of maintenance conditions had obvious influence on the flexural strength of concrete. The results of this paper provide a reference for further research on the condition detection and maintenance of SFRC based on the Markov random matrix.