Blockchain and deep learning technologies for construction equipment security information management

Pan Xing, Zhong Botao, Sheng Da, Yuan Xinqi, Wang Yuhang

Abstract

Construction equipment security information management (CESIM) is a critical component of equipment security management. However, equipment-related information's authenticity and integrity cannot be guaranteed in centralized systems currently, which limits the effectiveness of CESIM. In addressing this issue, a framework that combines blockchain and deep learning technologies is proposed by providing decentralized data storage, keeping data authentic, and protecting data from tampering. In accordance with the proposed framework, a prototype system is developed and further evaluated. Results show that the prototype system can improve the efficiency of equipment supervision, decision making, and accident tracking in equipment security management. In conclusion, this study aims to place emphasis on: (1) applying blockchain technology to ensure equipment related information authentic and secure; (2) developing word2vec + CNN-based deep learning to automatically identify keywords and categories of equipment inspection reports; and (3) verifying the feasibility of the proposed framework and developing a prototype system for further application

Keywords: Blockchain Deep learning Construction safety management Equipment security information Information authenticity

https://www.sciencedirect.com/science/article/pii/S0926580522000590?via=ihub