Abstract
Effectively monitoring and analyzing on-site module installation for modular integrated construction (MiC) is essential to properly coordinating the MiC process. In this study, the authors propose an automated productivity monitoring framework for on-site module installation operations consisting of three modules: object detection, activity classification, and productivity analysis. The object detection module detects mobile cranes and modules interacting with mobile cranes, and the activity classification module classifies module installation activities into five different activities by considering the spatiotemporal relationship between the detected objects. Finally, the productivity analysis module analyzes the productivity of the module installation process by utilizing the accumulated activity classification results over image frames. The proposed model achieves an average accuracy of 89% (hooking: 85.71%, lifting: 84.44%, positioning: 94.90%, returning: 83.09%, and idling: 96.87%) in classifying the five activities. The developed framework enables practitioners to measure the productivity of the on-site module installation process automatically. In addition, productivity data stored from diverse construction sites contribute to identifying progress-impeding factors and improving the productivity of the entire MiC process.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
