Design and Performance of Shotcrete Using Ultra-High-Performance Concrete

This research project is dedicated to the development of cost-effective ultra-high performance concrete (UHPC) shotcrete for infrastructure repair applications and for 3D-printed concrete to enhance interlayer bond and continuity. UHPC, in contrast to conventional concrete, offers several distinct advantages. Its exceptional mechanical properties enable the application of thinner shotcrete layers and structural strengthening. The high tensile strength and inclusion of fibers reduce the need for reinforcing bars. Additionally, UHPC's remarkable impermeability eliminates the necessity for extra coatings, and its reduced rebound rates compared to conventional shotcrete result in less material wastage and a more efficient batching and cleanup process. The achievement of these goals involves the use of machine learning techniques to optimize mixture proportions. The project comprehensively investigates key engineering parameters, including shootability, buildability, rebound behavior, stability, mechanical properties, and shrinkage resistance within UHPC shotcrete mixtures. The machine learning models developed in this research will empower construction decision-makers to select a UHPC shotcrete mixture design that aligns with both performance and cost-effectiveness objectives. This approach has the potential to revolutionize the repair and enhancement of deteriorated infrastructure while promoting sustainable and budget-conscious construction practices.

Language

• English

Project

• Status: Active
• Contract Numbers:

  69A3552348339

• Sponsor Organizations:

  Center for Durable and Resilient Transportation Infrastructure

  University of Texas, Arlington
  Arlington, TX  United States  76019

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Start Date: 20230901
• Expected Completion Date: 0
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Machine learning; Mix design; Optimization; Shotcrete; Ultra high performance concrete
• Subject Areas: Construction; Highways; Maintenance and Preservation; Materials;

Filing Info

• Accession Number: 01909694
• Record Type: Research project
• Source Agency: Center for Durable and Resilient Transportation Infrastructure
• Contract Numbers: 69A3552348339
• Files: UTC, RIP
• Created Date: Feb 23 2024 4:26PM