Development of Digital Twins for Texas Bridges

Bridges are a critical component of transportation infrastructure, providing safe and efficient travel for millions of people every day. However, bridge maintenance can be complex and expensive, and it is challenging to detect problems early before they become more significant and costly to repair. Digital twins offer a solution to this challenge, providing a comprehensive and efficient means of obtaining, integrating, processing, and storing high-fidelity information about the current geometry and condition of a bridge. Developing digital twins of Texas bridges is a significant undertaking, as it requires collecting and integrating data from multiple sources, including sensors and unmanned aerial systems (UASs). Moreover, the resulting data must be complete and usable without overtaxing existing computer systems at Texas Department of Transportation (TxDOT), which presents challenges related to data compression and redundancy. To address these challenges, this research proposes an outcome-based framework for the development of digital twins of TxDOT bridges. This framework will be extensively validated across real-world conditions through data collection and digital twin construction efforts from 30 (or as many as requested and agreed to) TxDOT bridges. The resulting guidelines and procedures will provide a means for comprehensively and efficiently collecting, integrating, processing, and storing geo-referenced, multi-sensor, and high-fidelity information about the current geometry and condition of a bridge. To enable early identification of bridge maintenance needs, the researchers will investigate and assess feasibility and requirements for aligning multi-temporal models and detecting and quantifying changes over time both with manual observation and automatically. The prototype routines for alignment and change detection developed will be integrated into the digital twinning framework for direct use by TxDOT. Additionally, the research team will prepare and test training materials to teach project managers, maintenance supervisors, and other personnel on field collection and planning of 3D data, digital twin development, and data processing and modeling. These training materials will include operational recommendations and guidance on data collection plans, reviews and approval, and safety. The proposed research will provide a clear path for the digital transformation and integration of TxDOT bridge inspections, design, and maintenance activities, enabling more scientific decision-making and bridge management practices with broad impacts to TxDOT operations.


  • English


  • Status: Active
  • Funding: $505286
  • Contract Numbers:


  • Sponsor Organizations:

    Texas Department of Transportation

    125 E. 11th Street
    Austin, TX  United States  78701-2483
  • Project Managers:

    Odell, Wade

  • Performing Organizations:

    University of Houston, Texas

    Department of Mechanical Engineering
    Houston, TX  United States  77004
  • Principal Investigators:

    Hoskere, Vedhus

  • Start Date: 20230901
  • Expected Completion Date: 20260831
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01894813
  • Record Type: Research project
  • Source Agency: Texas Department of Transportation
  • Contract Numbers: 0-7181
  • Files: RIP, STATEDOT
  • Created Date: Sep 27 2023 2:44PM