Advanced Sensing and AI for Next-Generation Transportation Asset Management

The proposed study aims to create an artificial intelligence (AI)-based framework that harness data from advanced sensing technologies to advance the next-generation of transportation asset management systems. The development of AI-driven diagnostic tools for infrastructure is fundamental to optimizing maintenance plans, thereby enhancing public safety and minimizing the inefficient use of economic resources. To address the limitations associated with the availability of training data for deep learning algorithms, the proposed framework integrates heterogeneous data collected from multiple sources. Beyond the deployment of conventional fixed sensors, cutting-edge mobile sensing technologies will be incorporated to achieve unprecedented temporal and spatial resolution, thereby ensuring the scalability and adaptability of the AI-based strategy. The dynamic characteristics extracted from acceleration data acquired through smartphones will be utilized to: (i) calibrate the digital twin of the structure, (ii) identify and characterize potential structural damage, and (iii) provide essential physics-based knowledge to support the development of a physics-informed neural network for life-cycle assessment.

• Record URL:
  • https://abc-utc.fiu.edu/advanced-sensing-and-ai-for-next-generation-transportation-asset-management/

Language

• English

Project

• Status: Active
• Funding: $70,000.00
• Contract Numbers:

  69A3552348322

• Sponsor Organizations:

  Innovative Bridge Technologies/Accelerated Bridge Construction University Transportation Center (IBT/ABC-UTC)

  Florida International University
  Miami, FL  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Performing Organizations:

  Florida International University

  Civil and Environmental Engineering
  10555 W. Flagler Street, EC 3680
  Miami, FL  United States  33174
• Principal Investigators:

  Marasco, Giulia

• Start Date: 20260101
• Expected Completion Date: 20270630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Asset management; Digital twins; Neural networks; Sensors
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01998528
• Record Type: Research project
• Source Agency: Innovative Bridge Technologies/Accelerated Bridge Construction University Transportation Center (IBT/ABC-UTC)
• Contract Numbers: 69A3552348322
• Files: UTC, RIP
• Created Date: Aug 7 2026 8:35AM