Using Machine Learning for Sustainable Concrete Material Selection and Optimization in Building Design

Qian Meng; Haoran Xu; Jingwen He

doi:10.70393/6a6374616d.323530

Authors

Qian Meng University of Technology Sydney
Haoran Xu Columbia University
Jingwen He Washington University in St. Louis

DOI:

https://doi.org/10.70393/6a6374616d.323530

ARK:

https://n2t.net/ark:/40704/JCTAM.v2n1a02

Disciplines:

Machine Learning

Subjects:

Computer Application Technology

References:

35

Keywords:

Machine Learning, Sustainable Concrete, Material Optimization, Building Design

Abstract

This paper explores the application of machine learning (ML) in the selection and optimization of concrete materials for sustainable building design. It discusses how AI-driven platforms, such as Concrete Copilot and SmartMix, are revolutionizing concrete mix design by improving performance, reducing costs, and minimizing environmental impact. By leveraging ML techniques, these platforms enable real-time optimization of concrete ingredients, enhancing both resource efficiency and sustainability. The paper highlights the potential of machine learning to drive innovation in the concrete industry, contributing to the development of greener, more efficient building materials for future construction projects.

Author Biographies

Qian Meng, University of Technology Sydney

School of Architecture and Design, University of Technology Sydney, Sydney, Australia, 2007, mengqian519@gmail.com.

Haoran Xu, Columbia University

Graduate School of Architecture, Planning and Preservation, Columbia University, New York, 10027, USA, hx2304@columbia.edu.

Jingwen He, Washington University in St. Louis

Samfox School of Architecture, Washington University in St. Louis, California, USA, 94608, jingwenhe@wustl.edu.

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