Deep learning-based automated productivity monitoring for on-site module installation in off-site construction

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.