Loughborough University
LuBrilakis_ISARC2019_v4.pdf (615.52 kB)

Digital twinning of existing bridges from labelled point clusters

Download (615.52 kB)
conference contribution
posted on 2019-05-17, 10:23 authored by Ruodan Lu, Ioannis Brilakis
The automation of digital twinning for existing bridges from point clouds has yet been solved. Whilst current methods can automatically detect bridge objects in points clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to detected point clusters remains human dependent to a great extent. 95% of the total manual modelling time is spent on customizing shapes and fitting them to right locations. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are made up of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of existing methods have evaluated the resulting models in terms of spatial accuracy with quantitative measurements. We tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from labelled point clusters. The accuracy of the generated models is gauged using distance-based metrics. Experiments on ten bridge point clouds indicate that the method achieves an average modelling distance smaller than that of the manual one (7.05 cm vs. 7.69 cm) (value included all challenging cases), and an average twinning time of 37.8 seconds. Compared to the laborious manual practice, this is much faster to twin bridge concrete elements.


EPSRC, EU Infravation SeeBridge project and Trimble Research Fund.



  • Architecture, Building and Civil Engineering

Published in

36th International Symposium on Automation and Robotics in Construction


LU, R. and BRILAKIS, I., 2019. Digital twinning of existing bridges from labelled point clusters. IN: Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), Banff, AB, Canada, 21-24 May 2019, pp.616-623.


© International Association on Automation and Robotics in Construction


  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date


Publication date



This is a conference paper.


  • en


Banff, AB, Canada

Usage metrics

    Loughborough Publications


    Ref. manager