TY - JOUR
T1 - Automated Methods for Activity Recognition of Construction Workers and Equipment
T2 - State-of-the-Art Review
AU - Sherafat, Behnam
AU - Ahn, Changbum R.
AU - Akhavian, Reza
AU - Behzadan, Amir H.
AU - Golparvar-Fard, Mani
AU - Kim, Hyunsoo
AU - Lee, Yong Cheol
AU - Rashidi, Abbas
AU - Azar, Ehsan Rezazadeh
N1 - Publisher Copyright:
© 2020 American Society of Civil Engineers.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Equipment 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.
AB - Equipment 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.
KW - Activity recognition
KW - Activity tracking
KW - Audio-based method
KW - Construction equipment
KW - Convolutional neural network
KW - Kinematic-based method
KW - Location tracking
KW - Machine learning
KW - Performance monitoring
KW - Vision-based method
KW - Worker
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U2 - 10.1061/(ASCE)CO.1943-7862.0001843
DO - 10.1061/(ASCE)CO.1943-7862.0001843
M3 - Review article
AN - SCOPUS:85083201862
SN - 0733-9364
VL - 146
JO - Journal of Construction Engineering and Management
JF - Journal of Construction Engineering and Management
IS - 6
M1 - 0001843
ER -