Machine learning on concrete science: applications, challenges, and best practices
Zhanzhao Li, Jinyoung Yoon, Rui Zhang, Farshad Rajabipour, Wil V. Srubar III, Ismaila Dabo & Aleksandra Radlińska
npj Computational Materials 3:115 (2017)
doi.org/10.1038/s41524-022-00810-x
Published online:19 February 2017
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摘要：混凝土作为应用最广泛的建筑材料，与人类的发展密不可分。尽管混凝土科学在概念和方法上已经取得了进步，但是由于水泥材料的复杂性不断增加，因此获得所需性能的混凝土配方依然是一项具有挑战性的任务。凭借自主完成复杂任务的能力，机器学习 （ML）有望实现混凝土研究技术的革新。鉴于ML在混凝土配料设计中已经有了大量应用，因此有必要了解这一新兴计算领域在方法学上的局限性，并且制定最好的实施规范。我们在本文中首先回顾了ML对混凝土科学的推动作用，接着全面讨论了ML算法的实施，应用和机制；然后概述了在混凝土研究中如何完全发挥ML模型作用的设想。

Abstract: Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.