Calixto, Casey, School of Engineering and Applied Science, University of Virginia
Baritaud, Catherine, Engineering and Society
Heydarian, Arsalan, Civil Engineering

Construction sites are underperforming in productivity trends and are one of the least digitized industries. It is well known for being a significant source of air and noise pollution, impacting both individuals who work on those sites and surrounding communities. The construction industry needs increased efforts to improve processes and environmental impacts. This project explores the use of IoT and monitoring techniques at a specific construction site through a technical scope. Additionally, this project is accompanied by an STS lens that dissects the social implications of monitoring techniques for stakeholders. The blend of both projects is important in addressing the gap in limited research regarding understanding the implications of the technology in the context of the industry and workers.
Optimizing a minimally digitized industry through IoT is vital in potentially maximizing multiple values such as worker safety and improving efficiency. Given that there is a positive correlation between the technology and industry values, issues and areas of improvement can be addressed at a faster pace. On a smaller scale of IoT technologies, this project uses commercially available environmental sensors – Awair Omni – to understand the efficacy of the method at a specific construction site. The use of the sensors focuses specifically on understanding environmental impacts with the goal of improving worker well-being. The project used a system approach to further analyze areas of improvement through access of contractor and site schedules to find a link to trends from the sensor results. After analyzing the trends from the data, the sensors proved to be successful in identifying specific times, activities, and locations on-site that produced the most alarming spikes in noise (dB) levels and air (PM 2.5) quality. It was found that the first-floor sensors had the most PM2.5 exposure specifically on the roadside of the site. Indoor quality was the most concerning after analyzing the site-trailer data compared to the outside data which is indicative of better ventilation methods on-site. The results prove that overtime specific trends can be identified and used to increase efforts in combating the impacts. Although the technology proved useful, this project showed that the system architecture needs to include a user-friendly dashboard that performs the analysis and communicates real-time alerts to site supervisors. There was limited access to the site schedule, however, after performing an analysis it was found that the masonry subcontractor had the highest direct impact, and the specific location on-site was found. The results prove that the construction industry can benefit from a system architecture but the issue that needs to be addressed is the slow implementation and low adoption rates seen now. The issue of data privacy in the workplace has become increasingly relevant in today's digital age, as the use of technology and data-driven decision-making continues to shape organizational practices and policies. As such, understanding the social implications of data privacy is crucial for all stakeholders involved, including employers and employees. This project seeks to explore how these stakeholders draw the line between personal privacy and data security at work, and how this boundary-setting process impacts the social dynamics of the workplace. Specifically, answer the research question: How do employers and employees navigate data privacy concerns in the workplace, and what are the direct and indirect social implications for all stakeholders involved? By addressing this research question, the goal is to shed light on the complex relationship between data privacy and social dynamics in the workplace and contribute to a more nuanced understanding of this important issue. An Actor Network model – the Actor Network model is in accordance with Actor Network Theory (ANT) - and Pacey’s Triangle in accordance to the framework introduced by Michael Pacey was developed to examine the social implications of technological change. The ANT framework was used since it views technology as an active participant in social networks and emphasizes the role of non-human actors, such as machines and algorithms, in shaping social outcomes. Pacey’s triangle takes a sociological approach to identify the various social factors that influence data privacy practices in the workplace, such as organizational culture, power dynamics, and legal and regulatory frameworks. Using these frameworks clarified the restricted meaning of the technology in the industry while understanding the mistrust from
the construction workers regarding the technology. After workers’ values were found through the framework, it was found that union representation groups need to solidify their roles and understanding of workers’ rights. There is a need for clear communication between the employer and employee, specifically detailing how the information gathered at the workplace will be used. The most valuable finding is that there is a need for a transparent and detailed policy that protects workers’ data protection in the workplace. Overall, the technical project aims to unravel the benefits of using sensors on construction sites to mitigate environmental impacts. On the other hand, the STS project focuses on understanding how employers and employees draw the line when it comes to privacy. As IoT continues to make its way into the construction industry, current procedures in place are very lose ended and future work needs to focus on protecting workers’ values.

BS (Bachelor of Science)
Active Network Theory, Pacey's Triangle, Environmental Monitoring, IoT in Workplace

School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: Arsalan Heydarian
STS Advisor: Catherine Baritaud
Technical Team Members: Juan Chavez, Abid Hussain, Kathryn Owens, Alex Repak

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