Automated generation of FE mesh of concrete structures from 3D point cloud using computer vision technology
Paper in proceeding, 2021

To achieve real-time structural health monitoring (SHM), a concept of digital twin - a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures.


Jiangpeng Shu

Zhejiang University

Kamyab Zandi

Chalmers, Architecture and Civil Engineering, Structural Engineering

Weijian Zhao

Zhejiang University

Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenaince, Safety and Management, IABMAS 2020

9780367232788 (ISBN)

10th International Conference on Bridge Maintenaince, Safety and Management, IABMAS 2020
Sapporo, Japan,

Digital Twin as a Decision-Making Support Tool for Resilience of Urban’s Infrastructure under Extreme Climatic Events

Region Västra Götaland (2017- 00779), 2018-02-01 -- 2020-01-31.

European Commission (EC) (EC/H2020/754412), 2018-02-01 -- 2020-01-31.

Barbro Osher Endowment (CS2017-0122), 2018-02-01 -- 2020-01-31.

Subject Categories

Other Computer and Information Science

Applied Mechanics

Computer Vision and Robotics (Autonomous Systems)



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