EPSCoR Research Fellows: NSF: Synergistic Multiscale and Multiphysics Characterization of Additively Manufactured Self-Sensing Concrete Structures

The inevitable aging and degradation of infrastructure are critical concerns that require urgent attention. A viable solution to this challenge is meticulous structural health monitoring (SHM) to evaluate the service life of infrastructure and plan for rehabilitation. However, many existing sensors and SHM approaches have limitations, including high costs, limited durability, compatibility issues, and frequent maintenance needs.

Self-sensing cementitious composites (SSCCs) offer a promising alternative for SHM due to their excellent sensing performance, compatibility with concrete structures, and relatively lower cost. Despite their potential, self-sensing concrete remains mostly confined to laboratory research after over two decades of development. This is due to the complexity of integration, the need for refined fabrication techniques, and a deeper understanding of its properties and mechanisms.

Additive manufacturing offers a promising approach to address these issues due to its design flexibility. This opens the door for integrating innovative self-sensing materials into concrete structural design, specifically tailored to enhance structural health monitoring capabilities. To accelerate the practical application of this novel technology and advance the progress of science, this project will establish a synergistic multiscale and multiphysics characterization approach.

The research will also be complemented by incorporating courses and outreach programs on additive manufacturing and advanced numerical methods for graduate, undergraduate, and K-12 students. Specifically, we will actively involve underrepresented students in the project, inspiring them to explore STEM fields, thereby enriching the educational landscape within the EPSCoR jurisdiction.

This EPSCoR Research Fellows program provides a fellowship to an Assistant Professor and training for a graduate student at the Louisiana State University. Harnessing the expertise and resources available through a partnership with Oak Ridge National Laboratory (ORNL), the project aims to pioneer a comprehensive understanding of additively manufactured self-sensing concrete.

The project will establish an innovative framework to investigate the multiscale and multiphysics characteristics, with a focus on identifying critical manufacturing parameters and material properties to unravel the process-structure-property relationships of 3D printed self-sensing concrete. By characterizing the microstructures under various environmental factors, the project aims to lay the scientific and technical groundwork necessary for the effective design and reliable implementation of these advanced materials in large-scale construction.

Furthermore, the project is designed to serve as a springboard for the PI and his group, facilitating a significant advancement in research capacity and collaborative potential. Access to top-tier computational and characterization tools at ORNL will enable the research team to extend current scientific knowledge and practice, leading to transformative breakthroughs in the additive manufacturing of self-sensing concrete.

This project has the potential to significantly impact the construction industry and contribute to the national aim of leading in the advanced manufacturing domain.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.