Real-Time Warning Model of Highway Engineering Construction Safety Based on Internet of Things
Auteur(s): |
Haoran Song
Hao Yu Dianliang Xiao Yuexiang Li |
---|---|
Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Advances in Civil Engineering, janvier 2021, v. 2021 |
Page(s): | 1-10 |
DOI: | 10.1155/2021/6696014 |
Abstrait: |
Real-time and effective early warning of highway engineering construction sites is the key to ensuring the safety of highway engineering construction. At present, highway engineering construction safety early warning is limited by the experience of relevant personnel at the site and the dynamic changes of the project site environment. Therefore, the creation of a more active, smarter, and more effective real-time early warning model for construction safety is a strong complement to current research and has important theoretical and practical implications. The Internet of Things is the third wave of the information industry after computers, the Internet, and mobile communication networks. It is of great significance to promote the development of science and technology, economic growth, and social progress. Aiming at the shortcomings of the inadequate safety management methods for highway engineering construction in China, the inefficient efficiency of safety production supervision and management, and the emphasis on single and sporty supervision methods, a real-time early warning model for highway engineering construction safety based on the Internet of Things technology was constructed. By quantifying, scoring, and statistics of the safety situation during the construction process, the model achieves the goals of real-time monitoring, early warning, and handling hidden safety hazards. It overcomes problems such as untimely and unscientific safety issues in the past and effectively improves China’s highway engineering construction. The experimental comparison between the real-time early warning model and the traditional early warning model in this paper shows that the accuracy of the early warning model proposed in this paper is improved by nearly 5%, and the false alarm rate is reduced by nearly 4%. |
Copyright: | © Haoran Song et al. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
1.19 MB
- Informations
sur cette fiche - Reference-ID
10560637 - Publié(e) le:
03.02.2021 - Modifié(e) le:
02.06.2021