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: 2221104
• 负责人: Surya Kalidindi
• 金额: $ 24.55万
• 依托单位: Georgia Tech Research Corporation
• 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=2221104&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筋混凝土结构最常见且成本最高的劣化机制。美国的腐蚀成本约相当于国内生产总值 (GDP) 的 3% 至 4%。仅公路桥梁腐蚀每年给美国经济造成的损失估计就达 23-310 亿美元。此外，腐蚀会缩短民用基础设施的使用寿命并导致材料的使用增加。这反过来又增加了建筑业的碳足迹，并影响气候变化缓解策略。因此，开发和利用创新、廉价且有效的耐腐蚀钢以尽量减少对美国经济和环境的负担至关重要。由于其可用性和低成本，碳钢是混凝土中最常用的增强材料。支持该合作研究项目的中心假设是可以优化碳钢微观结构以增强其在混凝土环境中的耐腐蚀性。研究微观结构和腐蚀特性之间的定量相关性具有挑战性，因为相应的微观结构设计空间非常大。传统的设计方法严重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。微观结构敏感设计和材料知识系统采用全面、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挑战。该项目的一个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整理高价值的实验数据。为了满足这一需求，将设计和实施新颖的高通量策略，包括合成涵盖各种不同微观结构的材料样本库以及评估其微观结构和腐蚀性能。这项研究旨在通过为研究人员和材料开发人员提供基本工具来假设、设计、优化和测试新材料，从而以一定的成本缓解与钢筋混凝土结构中钢材腐蚀相关的问题，从而产生深远的社会、政治和经济影响。 -有效的方法。该方法的科学新颖性在于它能够预测碳钢的微观结构对其腐蚀性能的影响。这些见解可用于调整微观结构，以优化钢的耐腐蚀性，而无需改变钢的化学成分。这项研究的主要动力来自于以下需求：(1) 阐明碱性混凝土环境中腐蚀与碳钢微观结构之间鲜为人知的联系，以及 (2) 弥合与优化微观结构敏感腐蚀相关的关键知识差距钢的电阻。这项工作重点关注四个重点：(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构建机器学习模型，(4) 设计和制造钢该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。