Automated vision-based construction progress monitoring in built environment through digital twin

Abstract

Effective progress monitoring is ineviTable for completing the construction of building and infrastructure projects successfully. In this digital transformation era, with the data-centric management and control approach, the effectiveness of monitoring methods is expected to improve dramatically. ”Digital Twin,” which creates a bidirectional communication flow between a physical entity and its digital counterpart, is found to be a crucial enabling technology for information-aware decision-making systems in manufacturing and other automotive industries. Recognizing the benefits of this technology in production management in construction, researchers have proposed Digital Twin Construction (DTC). DTC leverages building information modeling technology and processes, lean construction practices, on-site digital data collection mechanisms, and Artificial Intelligence (AI) based data analytics for improving construction production planning and control processes. Progress monitoring, a key component in construction production planning and control, can significantly benefit from DTC. However, some knowledge gaps still need to be filled for the practical implementation of DTC for progress monitoring in the built environment domain. This research reviews the existing vision-based progress monitoring methods, studies the evolution of automated vision-based construction progress monitoring research, and highlights the methodological and technological knowledge gaps that must be addressed for DTC-based predictive progress monitoring. Subsequently, it proposes a framework for closed-loop construction control through DTC. Finally, the way forward for fully automated, real-time construction progress monitoring built upon the DTC concept is proposed.