Citation

Kim K, Cho YK. J. Constr. Eng. Manage. 2021; 147(3): e04020184.

Copyright

(Copyright © 2021, American Society of Civil Engineers)

DOI

10.1061/(ASCE)CO.1943-7862.0002001

PMID

unavailable

Abstract

Monitoring and understanding construction workers' behavior and working conditions are essential to achieve success in construction projects. The dynamic nature of construction sites has heightened the awareness of the need for improved monitoring of individual workers on sites. Although several studies indicated promising results in automated motion and activity recognition using wearable motion sensors, their technical and practical feasibility was not properly validated at actual job sites. Motion recognition models have to be evaluated in actual conditions because the motion sensor data collected in controlled conditions, and actual conditions can have different characteristics. This study proposes Long Short-Term Memory (LSTM) networks for recognizing construction workers' motions. The LSTM networks were validated through case studies in one bridge construction site and two road pavement sites. The LSTM networks indicated classification accuracies of 97.6%, 95.93%, and 97.36% from three different field test sites, respectively. Through the case studies, the technical and practical feasibility of the LSTM networks was properly investigated. With LSTM networks, individual workers' behavior and working conditions are expected to be automatically monitored and managed without excessive manual observation.

Language: en

Keywords

Construction worker; Deep learning; Long short-term memory; Monitoring; Motion recognition