Digital reconstruction and geometrical model updating of large-scale bridge structures: a case study of Baijusi bridge

Maintaining and modernising bridge structures is crucial to ensuring their structural integrity and public safety. This paper presents an innovative, semi-automated approach to transforming 3D point cloud data of bridges into precise, editable computer-aided design (CAD) drawings. The proposed methodology achieves high precision in mapping essential bridge components, accurately recognising and delineating key structural features such as supports and deck structures. Testing shows that the automatically generated CAD models achieve an Intersection over Union (IoU) metric of up to 91% for critical structural outlines, with an F1-measure indicating dimensional inaccuracies of about 8% and an 83% success rate in identifying pivotal elements. In addition, the approach demonstrates exceptional efficiency, generating an editable CAD drawing in DXF format in under 1 min. The proposed framework is also used for updating finite element (FE) models for bridges, enabling more advanced structural analysis and proactive maintenance protocols. This combination of automated CAD modelling with FE model refinement provides a novel methodology for streamlining the evaluation and preservation of bridge infrastructure, making a significant contribution to the field of structural engineering.

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Publication Details

Journal
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
Published
2025-10-23
DOI
https://doi.org/10.1680/jsmic.24.00012
Citations
1
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
2.35
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article

Digital reconstruction and geometrical model updating of large-scale bridge structures: a case study of Baijusi bridge

Ashraf El‐Hamalawi, Shanqin Chen, Chunli Ying, Daguang Han
1 citations
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
3D Surveying and Cultural Heritage
2.35
article

Digital reconstruction and geometrical model updating of large-scale bridge structures: a case study of Baijusi bridge

Ashraf El‐Hamalawi, Shanqin Chen, Chunli Ying, Daguang Han
article en
1 citations

Abstract

Maintaining and modernising bridge structures is crucial to ensuring their structural integrity and public safety. This paper presents an innovative, semi-automated approach to transforming 3D point cloud data of bridges into precise, editable computer-aided design (CAD) drawings. The proposed methodology achieves high precision in mapping essential bridge components, accurately recognising and delineating key structural features such as supports and deck structures. Testing shows that the automatically generated CAD models achieve an Intersection over Union (IoU) metric of up to 91% for critical structural outlines, with an F1-measure indicating dimensional inaccuracies of about 8% and an 83% success rate in identifying pivotal elements. In addition, the approach demonstrates exceptional efficiency, generating an editable CAD drawing in DXF format in under 1 min. The proposed framework is also used for updating finite element (FE) models for bridges, enabling more advanced structural analysis and proactive maintenance protocols. This combination of automated CAD modelling with FE model refinement provides a novel methodology for streamlining the evaluation and preservation of bridge infrastructure, making a significant contribution to the field of structural engineering.

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and ConstructionVol. 178(3)
Loughborough University (GB), Southeast University (BD), Institute of Navigation (US), Southeast University (CN)
Openalex Percentile: Top 9%
3D Surveying and Cultural Heritage
2.35
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Digital reconstruction and geometrical model updating of large-scale bridge structures: a case study of Baijusi bridge — Ashraf El‐Hamalawi, Shanqin Chen, et al. · Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction (2025) | TGRS Research Map | TGRS