UTC 2022 Funding - Cycle 3 Research Projects

Project No.: CY3-UNM-05
Title: Smart Healing in Additively Manufactured Engineered Cementitious Composites Beams for Durable Transportation Infrastructure 
Performing Institution: University of New Mexico
Principal Investigator: Maryam Hojati
Start and Anticipated Completion Dates: 01/01/2026 to 01/15/2027
Abstract: This project investigates the self-healing capabilities of 3D-printed Engineered Cementitious Composites (ECC) for transportation infrastructure applications, focusing on enhancing the durability and longevity of 3D-printed concrete structures. In particular, the research will examine how factors such as material composition, fiber reinforcement, and curing mechanisms influence the self-healing behavior of 3D-printed ECC beams. This self-healing capability has significant potential benefits as the layer-by-layer deposition process used in 3D printing can introduce "cold joints" or interlayer weaknesses, which may negatively impact long-term durability. The project will explore whether ECC’s intrinsic self-healing ability can mitigate these effects and enhance the durability of printed infrastructure, such as pavements, bridges, and retaining walls, which are subjected to harsh environmental conditions. The specific objectives of the project are to: evaluate the influence of supplementary cementitious materials like fly ash and blast furnace slag on the self-healing properties of 3D-printed ECC; assess the effect of different fiber lengths (6 mm and 10 mm) on crack control and healing kinetics; investigate the impact of various curing regimes (e.g., water immersion, relative humidity conditions) on the healing process; and conduct mechanical testing, microstructural analysis, and data modeling to develop predictive models for self-healing behaviors. 

The research will produce implementable results in the form of optimized ECC formulations with enhanced self-healing properties for 3D-printed infrastructure. It will also generate valuable data, including mechanical performance metrics, microstructural insights, and predictive models that could shape future design practices and standards for 3D-printed construction. 
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