Scaling Construction Autonomous Technologies and Robotics Within the Construction Industry

Mohamed Elzomor; Piyush Pradhananga

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Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: Construction Engineering Division Technical Session 1
Tagged Division: Construction Engineering
Tagged Topic: Diversity
Page Count: 13
DOI: 10.18260/1-2--37698
Permanent URL: https://216.185.13.187/37698
Download Count: 443

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Paper Authors

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Mohamed Elzomor Florida International University

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Dr. Mohamed ElZomor is an Assistant Professor at Florida International University (FIU), College of Engineering and Computing and teaches at the Moss School of Construction, Infrastructure and Sustainability. Dr. ElZomor completed his doctorate at Arizona State University (ASU), Ira A. Fulton Schools of Engineering. Prior to attending ASU, Dr. ElZomor received a master’s of science degree in Architecture from University of Arizona, a master’s degree in Engineering and a bachelor of science in Construction Engineering from American University in Cairo. Dr. ElZomor moved to FIU from State University of New York, where he was an Assistant Professor at the college of Environmental Science and Forestry. Mohamed’s work focuses on Sustainability of the Built Environment, Engineering Education, Construction Engineering, Energy Efficiency Measures and Modeling, Project Management, and Infrastructure Resilience. Dr. ElZomor has extensive professional project management experience as well as a diverse cross-disciplinary academic knowledge. Mohamed, distinct expertise supports fostering interdisciplinary research in addition to embracing innovative pedagogical approaches in STEM education. Dr. ElZomor has been integrating innovative and novel educational paradigms in STEM education to support student engagement, retention, and diversity.

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biography

Piyush Pradhananga Florida International University

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Piyush grew up in Kathmandu, Nepal. Following college graduation in 2016 from Tribhuwan University (TU) in Kathmandu, he worked for a leading real estate corporation of Nepal on a project worth over ten million USD. He then joined a Research firm based in London where he worked as Engineering Graduate Researcher. Piyush now is a Ph.D. Candidate at Department of Civil and Environmental Engineering and Teaching/Research Assistant at Moss School of Construction, Sustainability and Infrastructure, Florida International University. His research interest includes Sustainable construction, Construction Safety, Engineering Education, AI and Robotics-based construction, and Sustainable infrastructure and resilience for disaster and extreme weather.

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Abstract

The fourth industrial revolution in the Architecture, Engineering, and Construction (AEC) Industry is transforming and enhancing conventional practices through the adoption of smart and autonomous systems fueled by advanced data processing and machine learning. Although construction management (CM) students are exposed to current fundamentals of construction technologies including BIM, students may potentially lack the fundamental knowledge and technological skills required for efficiently integrating, programming, and controlling robotics on construction sites. As such, it is critical to investigate CM students’ skill gaps in order to prepare the graduating future workforces with the required advanced automation-based technologies. This study aims to investigate: (1) the preparedness of CM students in terms of their ability to understand machine learning techniques and work with smart technologies such as Robotics and Internet of Things (IoT); (2) the level of interest of CM students to understand and work with data mining techniques as well as autonomous technologies; and (3) factors that impacts inclination of construction management students towards understanding and developing skills in advanced construction technology. To achieve this, the authors conducted a questionnaire survey as well as interviews with undergraduate and graduate students in a Minority-Serving Institution. The obtained data is analyzed through ordered probit regression to determine variables influencing the students’ interest in understanding and developing skills in advanced construction technology. The results of the study demonstrate the need to bridge the technological skill gaps of graduating STEM workforce to meet the transforming industry based on anticipated AEC workforce required qualifications. The findings of the study contribute to the construction workforce education/development and construction automation bodies of knowledge to ensure job security among STEM graduates in an era of frequently advancing and altering skill profiles, which in turn will support economic growth by producing new work skills without having to replace jobs.

Citation
Format

Elzomor, M., & Pradhananga, P. (2021, July), Scaling Construction Autonomous Technologies and Robotics Within the Construction Industry Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37698

TY  - CPAPER
AB  - The fourth industrial revolution in the Architecture, Engineering, and Construction (AEC) Industry is transforming and enhancing conventional practices through the adoption of smart and autonomous systems fueled by advanced data processing and machine learning. Although construction management (CM) students are exposed to current fundamentals of construction technologies including BIM, students may potentially lack the fundamental knowledge and technological skills required for efficiently integrating, programming, and controlling robotics on construction sites. As such, it is critical to investigate CM students’ skill gaps in order to prepare the graduating future workforces with the required advanced automation-based technologies. This study aims to investigate: (1) the preparedness of CM students in terms of their ability to understand machine learning techniques and work with smart technologies such as Robotics and Internet of Things (IoT); (2) the level of interest of CM students to understand and work with data mining techniques as well as autonomous technologies; and (3) factors that impacts inclination of construction management students towards understanding and developing skills in advanced construction technology. To achieve this, the authors conducted a questionnaire survey as well as interviews with undergraduate and graduate students in a Minority-Serving Institution. The obtained data is analyzed through ordered probit regression to determine variables influencing the students’ interest in understanding and developing skills in advanced construction technology. The results of the study demonstrate the need to bridge the technological skill gaps of graduating STEM workforce to meet the transforming industry based on anticipated AEC workforce required qualifications. The findings of the study contribute to the construction workforce education/development and construction automation bodies of knowledge to ensure job security among STEM graduates in an era of frequently advancing and altering skill profiles, which in turn will support economic growth by producing new work skills without having to replace jobs.
AU  - Mohamed Elzomor
AU  - Piyush Pradhananga
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Scaling Construction Autonomous Technologies and Robotics Within the Construction Industry
UR  - https://216.185.13.187/37698
DO  - 10.18260/1-2--37698
ER  -