A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models
A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models
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DOI：
10.3389/fmats.2021.796476
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Taihao Han;Sai Akshay Ponduru;R. Cook;Jie Huang;G. Sant;Aditya Kumar
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作者：
Taihao Han;Sai Akshay Ponduru;R. Cook;Jie Huang;G. Sant;Aditya Kumar
为了降低波特兰水泥（PC）的能量强度和碳足迹，混凝土技术人员采用的普遍做法是用辅助胶凝材料[SCM：地质材料（例如，石灰石）;工业副产品（例如，飞灰）;和加工材料（例如，煅烧粘土）]。SCM的化学和含量深刻地影响PC水化动力学;这反过来又决定了[PC + SCM]粘结剂的微观结构和性能的演变。由于SCM的组成的实质性多样性-加上大量的组合空间，以及由SCM-PC相互作用产生的高度非线性和相互作用的过程-最先进的计算模型不能产生[PC + SCM]粘合剂的水合动力学或性质的先验预测。在过去的20年里，大数据和机器学习（ML）的结合-通常被称为科学的第四范式-已经成为一种有前途的方法来学习材料中的成分-性质相关性（例如，混凝土），并利用这种学习来产生具有新成分的材料的性质的先验预测。尽管有这些优点，但ML模型的广泛使用受到阻碍，因为它们：1）需要大数据来学习组成-属性相关性，并且一般来说，大型混凝土数据库并不公开; 2）作为黑盒子，因此像基于理论的分析模型一样，对材料定律几乎没有任何见解。本研究提出了一种深度学习（DL）模型，能够对[PC + SCM]糊剂中的成分和时间依赖性水化动力学和相组合发展进行先验高保真预测。DL与以下耦合：1）快速傅立叶变换算法，其降低训练数据集的维度（例如，动力学数据集），从而允许模型从小型数据库中学习内在的组成-性质相关性;以及2）约束模型的热力学模型，从而确保预测不违反基本材料定律。DL的培训和结果最终被用来开发一个简单，易于使用，封闭形式的分析模型，能够预测[PC + SCM]糊剂中的水化动力学和相组合发展，使用其初始组成和混合物设计作为输入。
To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs’ compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML)—commonly referred to as the fourth paradigm of science–has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs.