Digital Shadow Framework for Construction Progress Monitoring

Abstract

As Building Information Modelling (BIM) and Construction 4.0 gain traction, tracking project progress in real time has become increasingly critical. Although Digital Twins offer advanced capabilities, their reliance on complex, bidirectional data exchange often hinders practical deployment during active construction. To overcome this limitation, this study introduces a BIM-enabled Digital Shadow (DS) framework. By utilizing a streamlined, unidirectional data flow from the physical job site to its digital counterpart, the proposed framework integrates BIM, the Internet of Things (IoT), computer vision, cloud computing, and artificial intelligence (AI) for efficient progress monitoring. The architecture comprises six sequential layers: Physical Construction, Data Acquisition, Digital Integration, Analytics, Visualization, and Decision Support. To validate this model, a two-round Delphi study was conducted with 30 multidisciplinary experts specializing in BIM, construction management, AI, IoT, computer vision, cloud computing, and Construction 4.0. The evaluation revealed strong consensus across all framework components, yielding median scores of 4–5, interquartile ranges of 0–1, and agreement levels between 80% and 100%, thereby fulfilling all predefined validation criteria. These findings establish both the conceptual soundness and practical viability of the proposed framework. This research advances digital construction by offering a workable DS architecture that synthesizes heterogeneous site data to support automated progress tracking, live visualization, and informed decision-making, ultimately laying a solid foundation for future prototype development and real-world smart construction deployment.