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Related Experiment Video

Updated: Aug 16, 2025

Precision Measurements and Parametric Models of Vertebral Endplates

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

Automatic generation of structural geometric digital twins from point clouds.

Kaveh Mirzaei¹, Mehrdad Arashpour¹, Ehsan Asadi²

¹Department of Civil Engineering, Monash University, Melbourne, Australia.

Scientific Reports

|December 24, 2022

Summary

This study introduces a geometric digital twin (gDT) framework to automatically create and update building models from 3D scans. This enhances structural health monitoring by integrating laser scan data with Building Information Models (BIMs).

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Area of Science:

Digital Engineering
Structural Health Monitoring
Computer Vision

Background:

Digitizing structural health monitoring (SHM) requires advanced digital twin (gDT) models that utilize 3D geometric data.
Existing methods for SHM often lack efficient ways to integrate real-time structural data into digital models.

Purpose of the Study:

To present a novel framework for generating and updating geometric digital twins (gDTs) of existing buildings.
To infer semantic information from as-is point clouds for creating accurate virtual models.
To enable automated structural health monitoring through integrated gDT and Building Information Models (BIMs).

Main Methods:

Acquisition of 3D geometric data using regular laser scanning.
Extraction of geometric information (position, section shape) from point clouds via supervised classification and domain knowledge.
Inference of structural member function and section type.
Automatic generation or updating of Building Information Models (BIMs) as the virtual model within the gDT.

Main Results:

Successful generation of as-is geometric digital twins (gDTs) for building structural members.
Demonstrated efficiency and precision in creating virtual models from real-world construction data.
Extracted dimensional outputs from the gDT for effective structural health monitoring.

Conclusions:

The proposed framework effectively leverages 3D geometric data to create and update digital twins for SHM.
Integration of laser scan data with BIMs within a gDT enhances the digitization of structural health monitoring processes.
The model shows significant potential for improving the accuracy and efficiency of monitoring existing building structures.