Data-Driven Smart Composite Reinforcement for Precast Concrete

The proposed research aims to develop a smart composite reinforcement in precast concrete for real-time health condition monitoring using embedded sensors on the composite. The monitoring system can provide the health condition and risk information of the composite reinforcement and investigate the load transfer effectiveness between layers of the reinforcement and the precast concrete. The self-sensed composite reinforcement health and environmental data such as stress, strain, and temperatures will be paired with mechanical models of composite-concrete system and data-driven machine learning algorithms to predict the risk of the composite reinforcement for a better reinforced precast concrete system. Specific research objectives include: (1) develop embedded distributed sensors for self-sensing composite reinforcement; (2) conduct multi-scale multi-physics modeling with finite element analysis for the composite reinforcement mechanical and bonding performance using the sensor data; (3) integrate the data-driven machine learning algorithms to predict the risk of different composite reinforcement.

Language

• English

Project

• Status: Active
• Sponsor Organizations:

  University of Illinois, Urbana-Champaign

  Department of Civil and Environmental Engineering
  Newmark Civil Engineering Laboratory
  Urbana, IL  United States  61801-2352

  Office of the Assistant Secretary for Research and Technology

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

  Purdue University, Lyles School of Civil Engineering

  550 Stadium Mall Drive
  West Lafayette, IN  United States  47907
• Start Date: 20240101
• Expected Completion Date: 20241231
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Composite materials; Machine learning; Precast concrete; Reinforced concrete; Sensors; Structural health monitoring
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Materials;

Filing Info

• Accession Number: 01903259
• Record Type: Research project
• Source Agency: Transportation Infrastructure Precast Innovation Center
• Files: UTC, RIP
• Created Date: Dec 24 2023 8:34AM