Design and Implementation of Digital Twin Models for Continuous Monitoring and Performance Prediction of Precast Concrete Bridges [supporting dataset]
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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.
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Design and Implementation of Digital Twin Models for Continuous Monitoring and Performance Prediction of Precast Concrete Bridges [supporting dataset]



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Details:

  • Creators:
  • Corporate Creators:
    University of Illinois at Urbana-Champaign. National Center for Supercomputing Applications (NCSA)
  • Corporate Contributors:
  • Subject/TRT Terms:
  • Publication/ Report Number:
    UI-23-RP-03
  • DOI:
  • Resource Type:
    Dataset
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center (UTC)
  • Abstract:
    This project explores the design and implementation of digital twin models for the continuous monitoring and performance prediction of precast concrete (PC) bridges. Leveraging NVIDIA Omniverse, a cutting-edge 3D modeling platform, the research establishes a streamlined methodology for rapidly prototyping digital twin models using engineering drawings, inspection data, and real-world bridge configurations. The study includes case studies of two bridges in Illinois, showcasing the digital twin's ability to integrate bridge metadata, environmental and condition monitoring data, and inspection imagery into a cohesive digital representation. Additionally, the project extends the digital twin application to precast concrete beam testing, incorporating finite element analysis and crack segmentation into the simulation for enhanced structural assessment and visualization.

    The total size of the zip is over 1.3 GB. The .cae file is a file used for computer-aided engineering (CAE) models from the Abaqus software (for more information on .cae files and software, please visit https://www.file-extensions.org/cae-file-extension). The .jpg file extension is associated with JPEG (Joint Photographic Experts Group) file format. JPEG is a lossy image compression algorithm that significantly reduces the file size of the original image at the cost of quality. The higher the compression ratio the lower the quality of the .jpg file (for more information on .jpg files and software, please visit https://www.file-extensions.org/jpg-file-extension). The Portable Document Format (PDF) file format was developed by Adobe Systems and represents two-dimensional documents in a device-independent and resolution-independent format. There are PDF readers available on many platforms, such as Xpdf, Foxit, and Adobe's own Adobe Acrobat Reader. PDF readers/viewers or online services for basic functions are generally free (for more information on .pdf files and software, please visit https://www.file-extensions.org/pdf-file-extension). The .xlsx files are Microsoft Excel files, which can be opened with Excel, and other free available spreadsheet software, such as OpenRefine. The .txt file type is a common text file, which can be opened with a basic text editor. The most common software used to open .txt files are Microsoft Windows Notepad, Sublime Text, Atom, and TextEdit (for more information on .txt files and software, please visit https://www.file-extensions.org/txt-file-extension). The .tif file extension is traditionally used for Tagged Image File Format - one of the most widely supported lossless file formats for storing bit-mapped images (both PCs and Macintosh computers). TIFF/IT is a standard for the exchange of digital adverts and complete pages (for more information on .tif files and software, please visit https://www.file-extensions.org/tif-file-extension). The .rtf file type is used for documents written in Rich Text Format, a text format with some very basic formatting preserved. The .rtf format stores data in plain ASCII text file with additional formatting commands somewhat similar to HTML files (for more information on .rtf files and software, please visit https://www.file-extensions.org/rtf-file-extension).File extension .json is associated to JavaScript Object Notation file format, a lightweight, text-based, language-independent data interchange format. JSON defines a small set of formatting rules for the portable representation of structured data. It is used by various applications as alternative option to XML file format. The data in a json file are stored in simple text file format and the content is viewable in any simple text editor (for more information on .json files and software, please visit https://www.file-extensions.org/json-file-extension).

  • Content Notes:
    National Transportation Library (NTL) Curation Note: This dataset has been curated to CoreTrustSeal's curation level "A. Active Preservation". To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2025-03-19. If, in the future, you have trouble accessing this dataset, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.

    Public Access Note: This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation:

    Kindratenko, Volodymyr and Bassem Andrawes (2025). "Design and Implementation of Digital Twin Models for Continuous Monitoring and Performance Prediction of Precast Concrete Bridges [supporting dataset]". Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center. https://doi.org/10.21949/3pa3-fe38>

  • Format:
    ZIP
  • Funding:
    69A3552348333
  • Collection(s):
  • Main Document Checksum:
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  • File Type:

Supporting Files

More +

Publications

    1. Design and Implementation of Digital Twin Models for Continuous Monitoring and Performance Prediction of Precast Concrete Bridges
      Design and Implementation of Digital Twin Models for Continuous Monitoring and Performance Prediction of Precast Concrete Bridges
      Filetype[PDF - 4.81 MB]
      :
      Kindratenko, Volodymyr ;
      Andrawes, Bassem
      2024-12-01
      Abstract:
      This project explores the design and implementation of digital twin models for the continuous monitoring and performance prediction of precast concret...
      File Type:
      Filetype[PDF - 4.81 MB]
      File Type:
      Filetype[PDF - 4.81 MB]
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