Data-Driven Optimisation of Cement-Based Mixes with Chemical Admixtures and Supplementary Cementitious Materials

数据驱动的化学外加剂和补充胶凝材料的水泥基混合物的优化

• 批准号: 2601053
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601053
• 关键词: Data; Driven; Optimisation; Cement; Based

Concrete is no longer a construction material but an ever-expanding range of cement-based products which can be tailored to specific performance requirements through the combination of chemical admixtures and supplementary cementitious materials (SCMs). During the development of such admixtures and additions, the industrial partner in this project has generated an enormous wealth of data through the proportioning, production and testing of trial mixes where they are used at different dosages and in various combinations. This project aims at utilising the methodological framework and techniques of data mining and multivariate statistics to combine and integrate all this information in a consolidated database and to derive semi-empirical models that can be used to optimise the proportioning of mixes from the point of view of their engineering properties. The focus of the project will be on the engineering properties, that is, fresh and hardened state properties that are relevant to engineers, designers, manufacturers and contractors in terms of product specification and quality control. Experimental work is planned to generate validation data, as well as to link microstructure and composition to performance parameters. Here, performance will be understood from a basis of fundamental cement science, giving a physical aspect to the models.The project will generate a holistic understanding of the interactions between different SCMs and chemical admixtures at different dosages and provide quantitative tools for the prediction of their combined effects on the fresh and hardened properties of concrete and related cement-based materials (grouts, mortars). This is a completely novel approach which will address a significant research gap. To date, an accurate prediction of such properties is almost never possible without significant efforts, involving parametric adjustment of mix designs in trial batches, i.e. a process of trial and error. Precise performance predictions are complicated by variations in material composition and mix designs, severely limiting the validity of such predictions. By bringing the number of data from a scale of a few dozens to a scale of thousands of cases, the predictive power of the data-based models will improve, leading to more robust conclusions and wider applicability.It is anticipated that this will lead to semi-automated tools for the proportioning of cement-based products, which will help the construction industry to make a more efficient use of resources. This can be linked to decarbonisation of construction.

混凝土不再是一种建筑材料，而是一种不断扩大的水泥基产品，可以通过化学外加剂和辅助胶凝材料（SCM）的组合来满足特定的性能要求。在这些外加剂和添加剂的开发过程中，该项目的工业合作伙伴通过以不同剂量和各种组合使用的试验混合物的配比，生产和测试产生了大量的数据。该项目旨在利用数据挖掘和多元统计的方法框架和技术，将所有这些信息联合收割机和整合到一个统一的数据库中，并推导出半经验模型，从工程特性的角度出发，可用于优化混合料的配比。该项目的重点将是工程特性，即与工程师、设计师、制造商和承包商在产品规格和质量控制方面相关的新鲜和硬化状态特性。计划进行实验工作，以生成验证数据，并将微观结构和成分与性能参数联系起来。在这里，将从基础水泥科学的基础上理解性能，为模型提供物理方面的知识。该项目将全面了解不同SCM和不同剂量的化学外加剂之间的相互作用，并提供定量工具，用于预测它们对混凝土和相关水泥基材料（灌浆，砂浆）的新鲜和硬化性能的综合影响。这是一种全新的方法，将解决一个重大的研究差距。到目前为止，准确预测这样的属性几乎是不可能的，没有显着的努力，涉及参数调整的混合设计在试验批次，即一个过程中的试错。精确的性能预测是复杂的材料成分和混合设计的变化，严重限制了这种预测的有效性。通过将数据数量从几十个案例的规模提高到数千个案例的规模，基于数据的模型的预测能力将得到提高，从而得出更可靠的结论和更广泛的适用性。预计这将导致半自动化的水泥产品配料工具，这将有助于建筑行业更有效地利用资源。这可能与建筑的脱碳有关。