Machine learning in concrete science: applications, challenges, and best practices

Zhanzhao Li, Jinyoung Yoon, Rui Zhang, Farshad Rajabipour, Wil V. Srubar, Ismaila Dabo, Aleksandra Radlińska

Research output: Contribution to journalReview articlepeer-review

83 Scopus citations

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.

Original languageEnglish (US)
Article number127
Journalnpj Computational Materials
Volume8
Issue number1
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • General Materials Science
  • Mechanics of Materials
  • Computer Science Applications

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