Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review

AbstractEquipment and workers are two important resources in the construction industry. Performance monitoring of these resources would help project managers improve the productivity rates of construction jobsites and discover potential performance issues. A typical construction workface monitoring...

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Bibliographic Details
Published in:Journal of construction engineering and management 2020-06, Vol.146 (6)
Main Authors: Sherafat, Behnam, Ahn, Changbum R, Akhavian, Reza, Behzadan, Amir H, Golparvar-Fard, Mani, Kim, Hyunsoo, Lee, Yong-Cheol, Rashidi, Abbas, Azar, Ehsan Rezazadeh
Format: Article
Language:English
Subjects:
Activity recognition
Automation
Construction equipment
Construction industry
Monitoring
Monitoring systems
State-of-the-Art Review
State-of-the-Art Reviews
Tracking devices
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Summary:AbstractEquipment and workers are two important resources in the construction industry. Performance monitoring of these resources would help project managers improve the productivity rates of construction jobsites and discover potential performance issues. A typical construction workface monitoring system consists of four major levels: location tracking, activity recognition, activity tracking, and performance monitoring. These levels are employed to evaluate work sequences over time and also assess the workers’ and equipment’s well-being and abnormal edge cases. Results of an automated performance monitoring system could be used to employ preventive measures to minimize operating/repair costs and downtimes. The authors of this paper have studied the feasibility of implementing a wide range of technologies and computational techniques for automated activity recognition and tracking of construction equipment and workers. This paper provides a comprehensive review of these methods and techniques as well as describes their advantages, practical value, and limitations. Additionally, a multifaceted comparison between these methods is presented, and potential knowledge gaps and future research directions are discussed.
ISSN:0733-9364
1943-7862
DOI:10.1061/(ASCE)CO.1943-7862.0001843