AI-Enhanced LiDAR Tools for Smart Bridge Clearance Assessment and Obstacle Detection
Over-height vehicle collisions with bridges continue to pose serious safety risks and impose significant infrastructure repair costs, despite the presence of clearance signage and record-based systems. Many current clearance datasets rely on legacy documentation or infrequent manual surveys, failing to reflect changes caused by resurfacing, structural modifications, or evolving roadway geometry. As a result, clearance information is often outdated, incomplete, or unreliable for operational decision-making. This project develops a smart, artificial intelligence (AI)-enhanced system that integrates mobile LiDAR scanning with machine learning models to automatically extract accurate bridge clearance and obstacle measurements. Neural network architectures will analyze 3D point cloud data to identify bridge geometry, detect vertical obstructions, and calculate clearances with sub-inch precision. The resulting data will be delivered through a user-friendly visualization and archiving platform designed for direct use by state Departments of Transportation. Pilot deployment in Massachusetts, in partnership with MassDOT, will demonstrate the system’s potential to improve clearance monitoring, reduce bridge strike risk, and modernize infrastructure safety workflows.
Language
- English
Project
- Status: Active
- Funding: $160,000.00
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Contract Numbers:
69A3552348301
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Performing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Principal Investigators:
Simos, Gerasimidis
- Start Date: 20260101
- Expected Completion Date: 20261231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
- Subprogram: University Transportation Centers
Subject/Index Terms
- TRT Terms: Artificial intelligence; Bridges; Laser radar; Monitoring; Object detection
- Identifier Terms: Massachusetts Department of Transportation
- Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01975680
- Record Type: Research project
- Source Agency: New England University Transportation Center
- Contract Numbers: 69A3552348301
- Files: UTC, RIP
- Created Date: Jan 5 2026 10:14PM