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Machine learning in concrete science: applications, challenges, and best practices
npj Computational Materials ( IF 9.7 ) Pub Date : 2022-06-06 , DOI: 10.1038/s41524-022-00810-x
Zhanzhao Li, Jinyoung Yoon, Rui Zhang, Farshad Rajabipour, Wil V. Srubar III, Ismaila Dabo, Aleksandra Radlińska

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.



中文翻译:

具体科学中的机器学习:应用、挑战和最佳实践

混凝土作为最广泛使用的建筑材料,与人类发展密不可分。尽管混凝土科学在概念和方法上取得了进步,但由于水泥体系的复杂性不断增加,目标特性的混凝土配方仍然是一项具有挑战性的任务。凭借自主处理复杂任务的能力,机器学习 (ML) 在具体研究中展示了其变革潜力。鉴于机器学习在混凝土混合物设计中的快速应用,有必要了解方法上的局限性并在这个新兴的计算领域制定最佳实践。在这里,我们回顾了 ML 对具体科学产生积极影响的领域,然后全面讨论了 ML 算法的实现、应用和解释。

更新日期:2022-06-06
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