Informative As-Built Modeling as a Foundation for Digital Twins Based on Fine-Grained Object Recognition and Object-Aware Scan-vs-BIM for MEP Scenes

Boyu Wang, Fangzhou Lin, Mingkai Li, Zhenyu Liang, Zhengyi Chen, Mingzhu Wang, Jack C.P. Cheng

Abstract: Mechanical, electrical, and plumbing (MEP) systems are critical for delivering essential services and ensuring comfortable environments. To improve the management efficiency of these complex systems, digital twins (DTs) that reflect the as-is conditions of facilities are increasingly being adopted. To generate DT models, laser scanners are widely used to capture as-built environments in the form of high-resolution images and dense 3D measurements. However, existing scan-to-BIM methods primarily produce basic geometric models, lacking detailed descriptive attributes of the components. To address this limitation, this paper proposes an informative DT model generation method for MEP systems based on fine-grained object recognition and object-aware scan-vs-BIM. The proposed method adopts a few-shot learning strategy to detect target objects in complex 3D environments and identify their family types based on vision foundation models. Following this, the association between as-designed components and as-built installations is formulated as a bipartite graph matching problem, which is solved using the Hungarian algorithm. This enables the automated updating of as-designed models into as-built DT models. Notably, the proposed association method is robust and applicable to components with significant installation deviations, a common challenge in MEP systems. The feasibility of the proposed approach was validated through experiments conducted on two construction sites in Hong Kong. Results demonstrated that the proposed approach significantly enhanced the accuracy of the scan-vs-BIM of MEP systems, thereby enabling informative DT model generation. © 2025 Elsevier Ltd.

External IDs:doi:10.1016/j.aei.2025.103382