Project Information

Link to Latest Report: Coming Soon

Background:

Over the last few years, Accelerated Bridge Construction University Transportation Center (ABC-UTC) at Florida International University has developed several types of 3D printers and has printed full-scale bridge elements.

However, during the printing of these bridge elements, UHPC was fed manually to the printing nozzle. Manual material feeding during 3D concrete (herein, UHPC) printing, as practiced currently, not only compromises the advantages of automation but also poses logistical and quality control challenges. Interruptions and inconsistencies in material supply, coupled with challenges in maintaining material workability and quality, can result in print defects, project delays, and increased construction costs. Despite the potential benefits of 3D concrete printing in terms of efficiency, speed, and sustainability, these challenges hinder its widespread adoption and realization of its full potential. To fully exploit the benefits of 3D concrete printing, an innovative and automated solution is needed to ensure a continuous supply of UHPC material to the 3D printer. Therefore, the primary problem to be addressed in this research is the development of an automated UHPC feeding system that seamlessly integrates with various 3D concrete printers developed by ABC-UTC.

This system will overcome issues related to material quality, flow consistency, and continuous material supply to optimize construction efficiency, enhance safety, and potentially lead to cost savings in construction projects. By addressing this problem, this research aims to advance the field of 3D concrete printing and contribute to its broader adoption in the construction industry.

Objective:

The proposed project objectives include:

• Developing an automated UHPC mixing and feeding system that ensures the continuous and uninterrupted supply of material to various types of 3D concrete printers developed at ABC-UTC at the laboratory scale.
• Investigating and optimizing UHPC mixtures for 3D printing, focusing on achieving consistent quality and flow properties.
• Implementing a real-time monitoring and adjustment system that utilizes sensor data to maintain
  precise material supply, quality, and printing conditions.

Scope:

The proposed project includes several tasks

• Task 1: Literature Review
  • An extensive literature review will be conducted to gather insights
    into the latest advancements in 3D concrete printing, automation, UHPC use, and related
    technologies to identify key research gaps and opportunities in the field.
• Task 2: Material Mix and Optimization
  • UHPC mixtures will be specifically tailored for 3D
    printing applications. To this end, different UHPC formulations and mix designs will be
    explored to ensure the desired quality, consistency, and flow properties.
• Task 3: Automated System Design
  • Collaboration will be initiated with experts in materials and
    mechanical engineering, automation, and construction to design the automated UHPC
    mixing and feeding system. This includes the development of storage silos, precision
    sensors, pneumatic conveyors, and customizable conveyor belt systems.
• Task 4: Real Time Monitoring and Adjustment
  • Advanced sensor technology and monitoring devices will be incorporated into the automated mixing and feeding system to collect real-time data on material properties, flow rates, and printing conditions. Furthermore, algorithms and feedback loops that use sensor data will be developed to make real-time adjustments to the material supply process, ensuring safety, and maintaining consistent quality and flow.
• Task 5: Proof of Concept Testing
  • Proof-of-concept testing of the automated real-time UHPC mixing and feeding system will be conducted to evaluate the system’s performance in terms of material supply, quality control, and efficiency.
• Task 6: Documentation and Reporting
  • Reports and presentations outlining the project’s progress, findings, and outcomes will be documented.

Research Team:
Principal Investigator: Mahyar Ramezani