课题基金 / 基金详情

ERI: Correlating Cyclical and Coupled Exposure to Concrete Performance and Service Life

ERI: Correlating Cyclical and Coupled Exposure to Concrete Performance and Service Life
ERI:将周期性和耦合暴露与混凝土性能和使用寿命相关联
批准号:
2347037
负责人:
Richard Deschenes
金额:
$19.99万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-04-01 至 2026-03-31

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中文摘要
翻译
该工程研究启动(ERI)奖支持旨在评估暴露于恶劣环境中的混凝土性能的研究,这些环境在温暖潮湿和寒冷季节之间循环,由于气候变化而加剧。混凝土是使用最广泛的建筑材料,其生产释放了8%的人为二氧化碳。限制这种环境负担需要改进预测混凝土性能和延长新的和现有的混凝土基础设施的使用寿命的方法。混凝土的微观结构是多孔的,并且由于季节性或长期气候变化而随着时间的推移而演变。基于稳定环境的现有测试方法和模型可能无法检测到这种相互作用,导致具体的基础设施在恶劣的周期性环境中恶化。使用一种新的加速试验方法,混凝土将暴露在模拟极端环境的条件下,然后研究这些环境之间的相互作用。实验结果将为开发预测循环和变化环境中混凝土性能的模型提供数据。该研究将试图为工程师和材料科学家提供对混凝土微观结构演变及其在恶劣环境中各自使用寿命之间关系的基本了解。该研究将整合教育和课程开发,以增加本科生和研究生对先进材料科学,数据科学和整体模型开发的参与。本研究的目标是量化周期性,动态和耦合环境赋予的微观结构和传输特性的变化,并了解这些变化导致的相互作用机制。已经确定的是,侵蚀性环境改变混凝土微观结构,导致当暴露于另一种环境时性能下降,如由于季节或气候变化而发生的。本研究的目的是(a)模拟周期性,动态和耦合环境所造成的恶化,(B)测量在模拟的持续时间内的微观结构和传输特性的变化,和(c)相关的微观结构的变化性能。将解决以下基本问题:(1)当暴露于循环或动态环境时,混凝土的微观结构特性会发生什么变化?(2)在一种环境中产生的物理化学变化与混凝土在另一种环境中的耐久性之间是否发生了相互作用?(3)混凝土的性能如何因暴露于周期性、动态或耦合环境而降低?这项研究的长期目标是通过模型开发将微观结构和传输特性与性能和使用寿命相关联。该研究将为PI的团队提供材料科学、数据科学和整体模型开发方面的基础知识。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This Engineering Research Initiation (ERI) award supports research that aims to assess the performance of concrete exposed to harsh environments that cycle between warm-humid and cold seasons that are increasing in severity due to climate change. Concrete is the most widely used construction material and its production releases 8% of human-caused carbon dioxide. Limiting this environmental burden requires improved methods for predicting concrete performance and extending the useful life of new and existing concrete infrastructure. The microstructure of concrete is porous and evolves over time due to seasonal or long-term climate changes. Existing test methodologies and models based on stable environments may not detect this interaction, leading to concrete infrastructure that deteriorates in harsh, cyclical environments. Using a novel accelerated test method, concrete will be exposed to conditions that simulate extreme environments and then the interaction between these environments will be studied. The experimental results will supply data for the development of models to predict concrete performance in cyclical and changing environments. The research will attempt to supply engineers and material scientists with a fundamental understanding of the relationships between the evolving microstructure of concrete and its respective service life in harsh environments. This research will integrate education and curriculum development to increase involvement of undergraduate and graduate students in advanced materials science, data science, and holistic model development.The goal of this research is to quantify changes in microstructural and transport properties imparted by cyclical, dynamic, and coupled environments and to understand the mechanism of interaction resulting from those changes. It has been established that aggressive environments alter concrete microstructure resulting in degraded performance when exposed to another environment, as occurs due to seasonal or climate change. The objectives of this research are to (a) simulate the deterioration caused by cyclical, dynamic, and coupled environments, (b) measure the resulting changes in microstructural and transport properties over the duration of the simulation, and (c) correlate microstructural changes to performance. The following fundamental questions will be addressed: (1) what microstructural characteristics of concrete are altered when exposed to cyclical or dynamic environments? (2) does an interaction occur between the physicochemical changes imparted by one environment and the durability of concrete in another environment? (3) how is the performance of concrete diminished by exposure to cyclical, dynamic, or coupled environments? The long-term aim of this research initiation is to correlate microstructural and transport properties to performance and service life through model development. This research will provide the PI’s team with foundational knowledge in materials science, data science, and holistic model development.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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