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Safety Management of Civil Engineering Construction Based on Artificial Intelligence and Machine Vision Technology.

Authors :
Zhang, Yuting
Source :
Advances in Civil Engineering; 12/13/2021, p1-14, 14p
Publication Year :
2021

Abstract

With the development of social economy and the progress of science and technology, the process of urbanization continues to deepen and the progress of the construction industry changes with each passing day. People are paying more and more attention to the safety of civil engineering. Accidents frequently occurred during the construction process, which exposed the lack of attention to safety issues during the construction process, the lack of effective supervision of safe construction, and the lack of safety awareness of construction personnel, which had an impact on the construction of civil engineering. In this article, a solution to the safety management system for construction personnel based on artificial intelligence machine vision technology is proposed for the study of the safety management of civil engineering structures. First of all, through the research and analysis of the problems in the safety management of construction workers, the types of safety accidents of construction workers are summarized and the effect of machine understanding of the construction scene is realized through target detection and description of the spatial interaction between the two. A real-time detection and early warning platform is built, and early warnings are automatically sent out when a predefined dangerous scene occurs, so as to achieve the purpose of preventing construction accidents. The experimental results in this paper show that, in the use of artificial intelligence machine vision technology to manage the safety of civil engineering construction, the safety management of workers in the construction is realized, and the results show that the level of construction safety management has increased to 97.4%, ensuring the quality of civil engineering construction and safety. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16878086
Database :
Complementary Index
Journal :
Advances in Civil Engineering
Publication Type :
Academic Journal
Accession number :
154101948
Full Text :
https://doi.org/10.1155/2021/3769634