Sustainable and Durable Calcium Sulfoaluminate Binders Enabled by Multi-Physics Characterization and Theory-Guided Machine Learning

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On a global scale, Portland Cement (PC) concrete is one of the most widely used material. While the continual development of infrastructure warrants that demand for concrete is increasing, the production of PC consumes considerable energy and produces large amount of carbon dioxide that is detrimental to the environment. Cements based on calcium sulfoaluminate compositions (CSACs) have been considered as sustainable alternatives to PC. Production of CSAC yields significantly less carbon dioxide than PC and can be produced using low-cost and widely-available raw materials. Studies on use of CSAC binders (CSAB), however, are limited. This research aims to pursue fundamental material research to enhance the utilization of CSAC to formulate sustainable and durable binders for concrete. Techniques of machine learning (ML) and experiments that are guided by thermodynamics will be employed to reveal mechanisms, and underlying composition-performance links, that drive the physicochemical behavior of CSABs. In addition, synergistic interactions between CSAC and abundant waste materials such as calcined clay and fly ash to produce CSABs that exhibit superior strength and durability will be investigated. This study has potential of advancing practical utilization of CSAC for infrastructure. Knowledge dissemination activities, including training of underrepresented and female students and outreach activities to inform professionals and public of socioeconomic impacts of sustainable concrete, will extend the impact of this research. The research strategy is premised on two themes. The first research theme focuses on optimization of CSAB microstructure. Towards this theme, a comprehensive thermodynamic model that is validated against experiments, and in conjunction with theory-guided machine learning (ML) will be employed to provide guidance on regulating chemistry of CSAC, such that CSABs with optimum phase assemblages are obtained. The term 'optimum' pertains to CSABs featuring optimal distribution of both ettringite and AFm phases. Additionally, for optimization of CSAB microstructure, novel active microstructure design methods that use relevant nano-seeds to enhance inter-locking of acicular ettringite crystals and nucleation, and growth of low-density AFm phases will be developed. The second research theme will develop experimental datasets of performance metrics of CSABs encompassing large numbers of different precursor chemistries using ASTM methods as well as embedded fiber-optic physical (e.g. strain sensors) and chemical (e.g. in-situ Raman spectroscopy) sensors. The datasets of CSABs performance, in relation to precursor chemistry, mixture design and curing conditions, will be processed using theory guided ML. The platform will enable high-fidelity prediction of CSAB performance, using readily measured physicochemical information pertaining to the binder as inputs. The ML platform will ultimately be leveraged to determine optimum chemistry and processing conditions of CSABs that exhibit superior strength and durability compared to their PC counterparts, while restricting the CSAC content of CSABs to 50%. By enabling prediction and optimization of performance, this effort will stimulate practical utilization of CSABs in construction.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.