| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | January 13, 2023 |
| Latest Amendment Date: | May 31, 2024 |
| Award Number: | 2221098 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Lesley Sneed
lsneed@nsf.gov (703)292-7732 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | May 1, 2023 |
| End Date: | April 30, 2026 (Estimated) |
| Total Intended Award Amount: | $245,238.00 |
| Total Awarded Amount to Date: | $261,238.00 |
| Funds Obligated to Date: |
FY 2024 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
201 SIKES HALL CLEMSON SC US 29634-0001 (864)656-2424 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
SC US 29634-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | ECI-Engineering for Civil Infr |
| Primary Program Source: |
01002425DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Corrosion of carbon steel in concrete is the most common and costly deterioration mechanism of steel-reinforced concrete structures. Corrosion costs in US are equivalent to about 3 to 4 percent of the gross domestic product (GDP). The annual cost of corrosion of just highway bridges to the US economy is estimated to be US$23-31 billion. Furthermore, corrosion reduces the lifetime of civil infrastructure and leads to increased use of material. This, in turn, increases the carbon footprint of the construction industry and affects climate change mitigation strategies. Thus, it is critical to develop and utilize innovative, inexpensive, and effective corrosion-resistant steel to minimize this burden on the US economy and on the environment. Carbon steel is the most used reinforcing material in concrete due to its availability and low cost. The central hypothesis underpinning this collaborative research project is that the carbon steel microstructure can be optimized to enhance its corrosion resistance in a concrete environment. Studying the quantitative correlations between microstructure and corrosion properties is challenging since the corresponding microstructure design space is very large. Traditional design approaches are woefully inadequate for systematically exploring such large design spaces and identifying optimal solutions. Microstructure-sensitive design and materials knowledge systems employ a comprehensive and quantitative microstructure treatment, which together with emergent machine learning tools can address the grand challenge described above. An equally important and novel component of this project lies in exploiting high-throughput strategies to collect and curate high-value experimental data. In order to address this need, novel high-throughput strategies, both in synthesizing material sample libraries spanning a wide range of distinct microstructures and evaluating their microstructures and corrosion performances, will be designed and implemented. This research aims to have far-reaching social, political, and economic impacts by enabling researchers and material developers with the fundamental tools to hypothesize, design, optimize, and test new materials to mitigate issues associated with steel corrosion in reinforced concrete structures in a cost-effective way.
The scientific novelty of the approach lies in its ability to predict the influence of the microstructure of carbon steel on its corrosion performance. These insights can be used to tune the microstructure to optimize the corrosion resistance of the steel without changing the steel chemistry. The main impetus for this research comes from the need to (1) elucidate the poorly understood linkages between corrosion and the microstructure of carbon steel in an alkaline concrete environment, and (2) bridge a critical knowledge gap related to optimizing the microstructure-sensitive corrosion resistance of steels. This work is focused on four thrusts: (1) high-throughput synthesis of samples, (2) high-throughput characterization of corrosion performance, (3) microstructure feature engineering and building machine learning models, and (4) designing and fabricating steel with an optimal microstructure.
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.
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