An Integrated AI-Powered Approach for Road Infrastructure Health Monitoring Using UAV and ERI Technologies

The stability of transportation infrastructure, particularly slopes adjacent to roadways, is critical for ensuring safety and longevity. However, traditional monitoring methods (e.g., inclinometers, extensometers, piezometers, sensors, etc.) often fail to provide comprehensive subsurface insights for early slope failure detection. While significant advancements have been achieved by unmanned aerial vehicle (UAV)-based imaging techniques, such as red, green, blue and light detection and ranging, for surface monitoring, several challenges and research gaps remain, such as deeper subsurface data acquisition and automation in surface and subsurface data fusion. This research proposes a data fusion framework integrating UAV-based sensing of slope surface and Electrical Resistivity Imaging (ERI)-based sub-surface characterization to enhance slope stability assessments. Integrating advanced data fusion methodologies using Machine Learning (ML) and Artificial Intelligence (AI), this research aims to develop an infrastructure health monitoring (IHM) framework. This research directly contributes to transportation infrastructure's durability and extended service life by providing a holistic view, advancing early‐warning capabilities, and optimizing maintenance strategies. The integrated monitoring system developed from this study will enable preventative interventions and deliver the precise information required for maintenance. In addition, advanced data fusion using ML and AI enhances infrastructure management technology. By integrating UAV and Electrical Resistivity Imaging (ERI) technologies, this study addresses the limitations of conventional IHM and offers a novel, scalable solution for transportation agencies. The developed framework will serve as a model for data-driven, technology-enhanced geohazard assessments, ensuring safer and sustainable transportation networks.

Language

• English

Project

• Status: Active
• Funding: $544,690.00
• Contract Numbers:

  69A3552344813

  69A3552348318

• 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:

  National Center for Infrastructure Transformation

  Prairie View A&M University
  Prairie View, TX  United States  77446
• Performing Organizations:

  Prairie View A&M University

  Department of Civil & Environmental Engineering
  P.O. Box 519
  Prairie View, TX  United States  77446
• Principal Investigators:

  Alam, Jobair

  Aktar, Jennifer

• Start Date: 20260201
• Expected Completion Date: 20280131
• Actual Completion Date: 20280131
• USDOT Program: University Transportation Centers
• Source Data: 03-45-PVAMU

Subject/Index Terms

• TRT Terms: Geotechnical engineering; Infrastructure; Research; Safety
• Subject Areas: Data and Information Technology; Maintenance and Preservation; Research; Safety and Human Factors;

Filing Info

• Accession Number: 02001907
• Record Type: Research project
• Source Agency: National Center for Infrastructure Transformation
• Contract Numbers: 69A3552344813, 69A3552348318
• Files: UTC, RIP
• Created Date: Sep 10 2026 8:32AM