Collaborative Research: High-Throughput Exploration of Microstructure-Sensitive Design for Steel Microstructure Optimization to Enhance its Corrosion Resistance in Concrete

合作研究：微观结构敏感设计的高通量探索，用于优化钢微观结构以增强其在混凝土中的耐腐蚀性能

• 批准号: 2221098
• 负责人: Amir Poursaee
• 金额: $ 24.52万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221098&HistoricalAwards=false
• 关键词: Collaborative; Research; Throughput; Exploration; Microstructure

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.

混凝土中碳钢的腐蚀是钢增强混凝土结构的最常见和昂贵的恶化机制。美国的腐蚀成本相当于大约3％至4％的国内生产总值（GDP）。仅高速公路桥梁的年度腐蚀成本估计为23-31亿美元。此外，腐蚀减少了民用基础设施的寿命，并导致材料的使用增加。反过来，这增加了建筑行业的碳足迹，并影响了气候变化的缓解策略。因此，开发和利用创新，廉价且有效的耐腐蚀钢以最大程度地减少对美国经济和环境的负担至关重要。碳钢由于其可用性和低成本而是混凝土中使用的最常用的钢筋材料。该协作研究项目的基础的中心假设是，可以优化碳钢微结构以增强其在混凝土环境中的耐腐蚀性。由于相应的微观结构设计空间非常大，研究微结构和腐蚀特性之间的定量相关性是有挑战性的。传统的设计方法非常不足以系统地探索如此大的设计空间并确定最佳解决方案。微观结构敏感的设计和材料知识系统采用了全面和定量的微观结构处理，与新兴的机器学习工具一起可以解决上述巨大的挑战。该项目同样重要且新颖的组成部分在于利用高通量策略来收集和策划高价值实验数据。为了满足这一需求，新型的高通量策略在综合材料样品库中，涵盖了各种不同的微观结构，并评估其微观结构和腐蚀性能。这项研究旨在通过使研究人员和物质开发人员拥有基本工具来假设，设计，优化和测试新材料，以减轻与钢腐蚀相关的问题，以一种成本效益的方式来减轻与钢腐蚀相关的问题。该方法的科学新颖性在于它可以预测碳钢微结构对其腐蚀性能的影响的能力。这些见解可用于调整微结构，以优化钢的耐耐药性，而无需改变钢化学。这项研究的主要动力来自（1）在碱性混凝土环境中阐明腐蚀与碳钢的微观结构之间的联系不足，以及（2）桥接与优化钢铁对微结构敏感腐蚀耐药性相关的关键知识差距。这项工作的重点是四个推力：（1）样品的高通量合成，（2）腐蚀性能的高通量表征，（（3）微观结构特征工程和建筑机器学习模型，以及（4）使用最佳的微观结构设计和制造钢，这些奖项反映了NSF的智力和良好的依据，该奖项是通过智力构建的依据，是既定的，又是依赖于良好的构建，该奖项的既定均具有良好的支持。 标准。