The sustainable smart security construction site management using fuzzy evaluation and artificial intelligence

Abstract

The objective is to manage Smart Construction Sites (SCSs) and reduce risks from the perspective of Artificial Intelligence (AI). Consequently, a sustainable SCS management system is designed. First, the development of SCS is introduced, along with the key technical concepts in SCS management, on-site risk factors, and AI-based identification technology. Next, an SCS risk-oriented Safety Helmet System (SHS) is established using the Fuzzy Comprehensive Evaluation (FCE) method and biometric identification technology. The SHS factors in site personnel, materials, environment, and management. Afterward, Building Information Modeling (BIM) is introduced to optimize construction personnel’s safety training process, visually monitor the SCS, and reduce on-site risks. Finally, the sustainable SCS management system is explored. The experimental analysis shows that the sustainable SCS management system reduces the annual risks from 36,700 to 3500. Meanwhile, the proposed SHS can significantly improve material utilization by 20%, reduce material wastage, and save construction costs. Overall, the construction site management has improved. The findings offer valuable insights for both governmental bodies and businesses to develop efficient strategies and responses to enhance the management of Sustainable Construction Supply Chains (SCSs). Consequently, these insights enable companies to make informed decisions and mitigate risks associated with construction projects. As a result, this contributes to the sustainable and robust growth of China’s construction industry. The recommendations presented hold substantial theoretical and practical importance for the sustainability of SCS.

Keywords

Artificial Intelligence fuzzy evaluation Smart Construction Site construction management deep learning

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