Preparing the Future Workforce of Architecture, Engineering, and Construction for Robotic Automation Processes

摘要

Abstract The evolution of robotics automation and artificial intelligence technologies is transforming the landscape of our future jobs, especially in the fields of Architecture, Engineering, and Construction (AEC). Despite the myth that these technologies may lead to job displacements, research data based on AEC workforce qualifications demonstrate the need to advance and alter skill profiles, thus, in turn, promise to support economic growth by producing new work skills without having to replace jobs. For example, tasks that previously could not be done because of several limitations, complexities, and/or required extended durations can now be pursued in a more efficient practice through automation and robotics. This on-going NSF funded research captures the opportunities offered by automation through planning, designing and developing a pilot Robotic Academy, a cloud-based set of training resources to cultivate a more talented workforce. To date, integrating robotics in AEC disciplines is perceived as a challenging and time-consuming task, yet training our future workforces through a Robotic Academy that deploys available technologies will be the first step to hedge against those challenges. In this planning phase of the study, the primary goal is to: (1) understand the reasons behind the lack of adopting robotics technologies and Artificial Intelligence (AI) techniques in the construction industry within South Florida; (2) identify the need of robotic-operation training modules; (3) design and develop educational courses for a Robotic Academy and; (4) assess and evaluate the effectiveness of the implemented pilot study while training the first cohort of students. To achieve this, the authors conducted interviews, and survey questionnaires distributed to leading construction firms in South Florida. Additionally, the study conducted surveys to evaluate the pilot training courses at the Robotic Academy to record students’ perspectives and learnings. The authors developed ordered probit regression model to determine the variables influencing the expected student enrollment in the designed courses. Results indicate that students with experience and knowledge in the field are more likely to enroll in the training, thus, in order to broaden the reach, the academy could incorporate entry-level courses tailored to prospective minority students, based on their needs and knowledge levels. Overall, the findings of the study show that a new pedagogy is urgent to meet the workforce demand of AI robotic driven construction industry. This on-going research initiative develops cutting-edge immersive cloud-based training modules for AEC stakeholders to improve their preparedness towards a more automated workplace.