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Mar 7, 2022

Semi-Automated Generation of 3D Bridge Models from 2D PDF Bridge Drawings

Publication: Construction Research Congress 2022

ABSTRACT

Despite the recent trend of increasing adoption of building information modeling (BIM) in the infrastructure (i.e., horizontal construction) domain, there is still a long way to go before BIM-based platforms for bridges actually take full advantage of the BIM-associated benefits at a level that is analogous to that achieved in the building (i.e., vertical construction) domain. An immediate challenge is the need of processing traditional 2D bridge drawings to 3D information models for BIM-based computational tasks, as bridges in records are mostly documented in 2D PDF drawings. With the recent adoption of the industry foundation classes (IFC) standard as the national standard for roadway and bridges by the American Association of State Highway and Transportation Officials (AASHTO), most state Department of Transportations (DOTs) are faced with the challenge of converting their traditional 2D bridge drawings to 3D models that comply with IFC standard, as manually performing this task could be cost prohibitive considering the number of bridges managed by an agency could be at the level of hundreds of thousands. To help achieve this task with reduced need of manual efforts, the authors developed a semi-automated method and corresponding algorithms for automatically processing existing 2D bridge drawings in PDF and converting these processed 2D bridge drawings to 3D bridge models. The proposed method and developed algorithms were experimentally tested on two bridge drawings in 2D PDF formats. The generated 3D bridge models using the developed algorithms were compared against developed 3D models using the traditional manual method. Experimental results showed that the proposed method successfully generated 3D bridge models from 2D PDF drawings with a similar quality but significant (>90%) time saving comparing to the traditional manual method.

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Construction Research Congress 2022
Pages: 1347 - 1354

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Published online: Mar 7, 2022

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Temitope Akanbi, Ph.D., A.M.ASCE [email protected]
1Researcher, Automation and Intelligent Construction (AutoIC) Lab, School of Construction Management Technology, Purdue Univ., West Lafayette, IN. Email: [email protected]
Jiansong Zhang, Ph.D., A.M.ASCE [email protected]
2Assistant Professor, Automation and Intelligent Construction (AutoIC) Lab, School of Construction Management Technology, Purdue Univ., West Lafayette, IN. ORCID: https://orcid.org/0000-0001-5225-5943. Email: [email protected]

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