Hard hat wearing detection based on head keypoint localization

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems, especially regarding personal protective equipment. However, despite all this attention, there is still no reliable way to establish the relationship between workers and their hard hats. To answer this problem a combination of deep learning, object detection and head keypoint localization, with simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. The results show that the conjunction of novel deep learning methods with humanly-interpretable rule-based systems can result in a solution that is both reliable and can successfully mimic manual, on-site supervision. This work is the next step in the development of fully autonomous construction site safety systems and shows that there is still room for improvement in this area.Comment: 17 pages, 9 figures and 9 table

Analysis of Private Investors Conduct Strategies by Governments Supervising Public-Private Partnership Projects in the New Media Era

Private investors and governments need to cooperate in public–private partnership (PPP) projects but the private investors may be in pursuit of their own profit by conducting defaulting behaviors which causes various environmental problems and economic risks. However, the information asymmetry between them makes it difficult to supervise the behaviors of private investors. The development of internet and social media creates new environment for the information spread and people are using new media increasingly in the current society, providing an inexpensive and viable way for the public to participate in PPP projects. We constructed a dynamic evolutionary model to analyze the behaviors strategies of governments and private investors in the new media era and then analyzed how important factors influence the behavior trends of governments and private investors. These findings could provide meaningful insight to improve supervision status by using the new media environment and predict the behaviors of governments and private investors in PPP projects, which would be conductive for the governments to supervise the private investors in PPP projects more efficiently

Artificial intelligence in infrastructure construction: A critical review

Artificial intelligence (AI) has emerged as a promising technological solution for addressing critical infrastructure construction challenges, such as elevated accident rates, suboptimal productivity, and persistent labor shortages. This review aims to thoroughly analyze the contemporary landscape of AI applications in the infrastructure construction sector. We conducted both quantitative and qualitative analyses based on 594 and 91 selected papers, respectively. The results reveal that the primary focus of current AI research in this field centers on safety monitoring and control, as well as process management. Key technologies such as machine learning, computer vision, and natural language processing are prominent, with significant attention given to the development of smart construction sites. Our review also highlights several areas for future research, including broadening the scope of AI applications, exploring the potential of diverse AI technologies, and improving AI applications through standardized data sets and generative AI models. These directions are promising for further advancements in infrastructure construction, offering potential solutions to its significant challenges