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Performance-Based Concrete as a Route for CO2 reduction

高性能混凝土作为减少二氧化碳排放的途径

基本信息

批准号：
2887748
负责人：
金额：
--
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2887748
关键词：
Performance Based Concrete Route CO2

项目摘要

Concrete is the most widely used material in today's construction industry. It is estimated that 90% of concrete'sgreenhouse gas emissions can be attributed to cement production. Cement production is the source of approximately 7%of the world's CO2 emissions. Therefore, a rapid transition to lower-cement concrete is imperative. One of the methodsfor improving the sustainability of concrete production is the use of Supplementary Cementitious Materials (SCMs) toreplace cement in concrete mixtures. One of the most significant barriers to the broader use of SCMs is that it ischallenging to accurately predict the resulting concrete's properties. This research project will aim to better understandthe physical and chemical characteristics of selected SCMs through novel testing methods like electron microscopycombined with EDS microchemical analysis and to establish links between those characteristics and the performance ofthe produced concrete. In addition, a machine-learning model will be developed and trained to predict the strength anddurability properties of blended concrete and to identify the optimum SCM characteristics for maximising cementreplacement. The results of the machine-learning model will also be used to create empirical formulas that can be usedto predict properties of the produced concrete based on SCM characteristics as well as mix composition. This researchwill give concrete designers more confidence in using blended concrete mixes.

混凝土是当今建筑业中使用最广泛的材料。据估计，90%的混凝土温室气体排放可归因于水泥生产。水泥生产是全球约7%的二氧化碳排放源。因此，迅速过渡到低水泥混凝土势在必行。提高混凝土生产可持续性的方法之一是使用辅助胶凝材料（SCM）来取代混凝土混合物中的水泥。更广泛地使用SCM的最重要障碍之一是准确预测所得混凝土的性质具有挑战性。该研究项目旨在通过电子显微镜结合EDS微量化学分析等新的测试方法更好地了解所选SCMs的物理和化学特性，并建立这些特性与所生产混凝土性能之间的联系。此外，还将开发和训练一个机器学习模型，以预测混合混凝土的强度和耐久性，并确定最佳SCM特性，以最大限度地提高水泥替代率。机器学习模型的结果还将用于创建经验公式，这些公式可用于根据SCM特性和混合组成预测所生产混凝土的性能。这项研究将使混凝土设计人员在使用混合混凝土混合料时更有信心。