Automatic generation of structural geometric digital twins from point clouds

Abstract

A geometric digital twin (gDT) model capable of leveraging acquired 3D geometric data plays a vital role in digitizing the process of structural health monitoring. This study presents a framework for generating and updating digital twins of existing buildings by inferring semantic information from as-is point clouds (gDT’s data) acquired regularly from laser scanners (gDT’s connection). The information is stored in updatable Building Information Models (BIMs) as gDT’s virtual model, and dimensional outputs are extracted for structural health monitoring (gDT’s service) of different structural members and shapes (gDT’s physical part). First, geometric information, including position and section shape, is obtained from the acquired point cloud using domain-specific contextual knowledge and supervised classification. Then, structural members’ function and section family type is inferred from geometric information. Finally, a BIM is automatically generated or updated as the virtual model of an existing facility and incorporated within the gDT for structural health monitoring. Experiments on real-world construction data are performed to illustrate the efficiency and precision of the proposed model for creating as-is gDT of building structural members.

Enhancing point cloud semantic segmentation in the data‐scarce domain of industrial plants through synthetic data

“…Xie et al (2019) and Zhang et al (2019) present recent overviews of the topic; online resources such as Papers With Code (2021) can help to provide an upto-date roundup in this fast-changing environment. The work of Mirzaei et al (2022) contains a comprehensive overview of methods used by state-of-the-art point cloud deep learning network architectures. Among others, a notable performance increase for semantic segmentation on point clouds was achieved by applying kernel point convolutions (KPConv; Thomas et al, 2019) and Point Cloud Transformers (Guo et al, 2021).…”

“…The work of Mirzaei et al. (2022) contains a comprehensive overview of methods used by state‐of‐the‐art point cloud deep learning network architectures. Among others, a notable performance increase for semantic segmentation on point clouds was achieved by applying kernel point convolutions (KPConv; Thomas et al., 2019) and Point Cloud Transformers (Guo et al., 2021).…”

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

“…Thus, the use of SHM technology becomes essential in the architecture, engineering, and construction (AEC) industry. SHM involves various activities such as capturing real-time data, analysing structural performance, utilising predictive modelling, generating actionable insights, and implementing proactive maintenance strategies [88][89][90]. Conventional monitoring techniques depend on visual examination and manual measurements, which require significant labour and are susceptible to inaccuracies [89,90].…”

“…SHM involves various activities such as capturing real-time data, analysing structural performance, utilising predictive modelling, generating actionable insights, and implementing proactive maintenance strategies [88][89][90]. Conventional monitoring techniques depend on visual examination and manual measurements, which require significant labour and are susceptible to inaccuracies [89,90]. Moreover, their effectiveness is also highly dependent on the expertise and discipline of personnel [89,90].…”

Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)

“…The so-called "Digital Shadows" [45], which envision an automatic data connection only between the physical and virtual spaces, correspond to a level 4 in the "connection" dimension; • "Digital Models" [45], "Pre-Digital Twins" [58], or "Geometric Digital Twins" [92], which may not have connections to the physical space or have only manual or semiautomatic ones, correspond to levels equal to or lower than level 3 in the "connection" dimension;…”