Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers' activities. HAR has many applications in human-robot interaction research to enable robots' understanding of human counterparts' activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner which is crucial for the widespread deployment of collaborative robots in construction.
Since the pandemic, most work environments have changed to home-based settings, which mainly serve for living purposes other than working purposes. There is no lack of studies in the impact of indoor environmental quality (IEQ) factors on occupant productivity in regular work environments;however, limited studies are conducted in home-based work environments, not to mention in the context of the COVID-19 pandemic. Therefore, an online survey was developed to explore the impact of IEQ factors on productivity between office-and home-based work environments among occupants with different genders and ages. A comprehensive list of key indicators was first developed. Then a survey was developed, distributed, and received 204 complete responses. The descriptive analysis and t-test are performed to evaluate the impact difference of all the IEQ factors on productivity of different occupants. The findings indicate that the visual factors' impact on productivity decreases for both genders, the impact of all factors on productivity for younger occupants increases when work from home (WFH), and the impact of acoustic quality is the highest among all five IEQ factors. Because of WFH as the future of work, this study can provide insights for future built environment design. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.
In the last couple of decades, various industries have taken advantage of emerging advanced technologies, such as the digital twin, to find more effective solutions in their respective areas. In the transportation infrastructure sector, the implementation of digital twin technology is slowly gaining traction but lagging behind other major industries. To better understand the limitations, opportunities, and challenges for the adoption of digital twin technology in this sector, a survey questionnaire was distributed to collect information from industry professionals involved in transportation infrastructure projects. The purpose of this study is to understand how digital twin technology is being perceived by the industry. Based on the results of this survey, the current state of digital twins in the infrastructure sector, as well as the expected benefits, and potential challenges for the deployment of this technology are discussed here. This study aims to initiate discussion for future research in digital twin technology for transportation infrastructure applications.
