Improve Utility Investigations through AI Data Fusion and Reliable Quality Assessments
Identifying and documenting existing utility facilities within the proposed right-of-way (ROW) is crucial for successful project delivery. There is a need to leverage data collection technology’s strengths and minimize weaknesses for a more robust and reliable determination of utility locations and develop and test metrics to assess the utility investigation quality levels in ways that make sense to project design teams. Improvements will lead to a better stakeholder understanding to communicate the quality levels commonly used by the SUE industry (D, C, B, and A), the basis for assessing utility investigation deliverable quality, and would be able to tie quality levels to quantifiable performance metrics such as positional accuracy, error, and completeness, which are common in engineering and surveying when collecting field data for a project.
Language
- English
Project
- Status: Active
- Funding: $1033157
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Contract Numbers:
0-7224
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Sponsor Organizations:
Texas Department of Transportation
125 E. 11th Street
Austin, TX United States 78701-2483 -
Project Managers:
Odell, Wade
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Performing Organizations:
Texas A&M Transportation Institute
Texas A&M University System
3135 TAMU
College Station, TX United States 77843-3135 -
Principal Investigators:
Quiroga, Cesar
- Start Date: 20240901
- Expected Completion Date: 20260831
- Actual Completion Date: 0
Subject/Index Terms
- TRT Terms: Artificial intelligence; Data collection; Data fusion; Geological surveying; Metrics (Quantitative assessment); Performance measurement; Project delivery; Public utilities; Quality control; Right of way (Land); Utilities engineering
- Subject Areas: Data and Information Technology; Geotechnology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01932651
- Record Type: Research project
- Source Agency: Texas Department of Transportation
- Contract Numbers: 0-7224
- Files: RIP, STATEDOT
- Created Date: Oct 3 2024 10:31AM