Abstract

Construction, one of the largest industries in the world, consistently underperforms and faces barriers in leveraging the full potential of applying analytics to sensor data due to a lack of a skilled workforce. The prospects for data-driven solutions to address emerging construction challenges and enhance performance across project life cycles are therefore constrained. Through mixed-method research utilizing a survey and focus group, this study investigates the knowledge and skills required for graduating construction engineering and management students to implement sensor data analytics in the construction sector. The findings revealed that sensor data analytics knowledge and skills are required to systemically process and analyze data from sensing technologies and present them in formats for effective decision-making. The presented key knowledge areas, specific skills, and their significance can aid the construction industry and academics to streamline professional development efforts to match the actual demands, allowing for more efficacy in workforce training. The future construction workforce is expected to gain a competitive edge with sensor data analytics knowledge and skills as the ubiquitous integration of sensing technologies continues to drive the tremendous growth of sensor data.

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Data Availability Statement

All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant Nos. 2111003 and 2111045.

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Go to Journal of Civil Engineering Education
Journal of Civil Engineering Education
Volume 150Issue 1January 2024

History

Received: Aug 30, 2022
Accepted: Aug 7, 2023
Published online: Sep 29, 2023
Published in print: Jan 1, 2024
Discussion open until: Feb 29, 2024

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Ph.D. Student, Construction Engineering and Management, Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24060 (corresponding author). ORCID: https://orcid.org/0000-0001-8668-3022. Email: [email protected]
Abiola Akanmu, Ph.D., M.ASCE https://orcid.org/0000-0001-9145-4865
Associate Professor, Construction Engineering and Management, Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24060. ORCID: https://orcid.org/0000-0001-9145-4865
Assistant Professor, Dept. of Engineering Education, Virginia Tech, Blacksburg, VA 24060. ORCID: https://orcid.org/0000-0003-3849-2947
Sang Won Lee, Ph.D.
Assistant Professor, Dept. of Computer Science, Virginia Tech, Blacksburg, VA 24060.
Ibukun Awolusi, Ph.D., A.M.ASCE https://orcid.org/0000-0001-8723-8609
Assistant Professor, School of Civil and Environmental Engineering, and Construction Management, Univ. of Texas at San Antonio, San Antonio, TX 78249. ORCID: https://orcid.org/0000-0001-8723-8609
Ph.D. Student, Dept. of Computer Science, Virginia Tech, Blacksburg, VA 24060. ORCID: https://orcid.org/0000-0001-8177-0733
Chinedu Okonkwo, S.M.ASCE https://orcid.org/0000-0002-8809-1378
Ph.D. Student, School of Civil and Environmental Engineering, and Construction Management, Univ. of Texas at San Antonio, San Antonio, TX 78249. ORCID: https://orcid.org/0000-0002-8809-1378

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