Towards Green Concrete: A Data-Driven Machine Learning for Reducing Cement Content

This paper introduces an innovative data-driven approach to designing green concrete by minimizing cement content through the optimization of multi-component blends. A comprehensive dataset of ۲,۳۳۲ experimental concrete mix designs incorporating supplementary cementitious materials (SCMs) and nanomaterials was compiled from ۳۱ scientific publications. A hybrid machine learning model combining Artificial Neural Networks (ANN), Genetic Algorithms (GA), and XGBoost was developed to predict compressive strength and identify optimal mix compositions. To address the challenge of incomplete data, a binary-feature masking normalization strategy was implemented, allowing the model to process heterogeneous datasets with improved accuracy and robustness. The proposed framework successfully identifies sustainable concrete mixes that reduce cement consumption by up to ۴۰% without compromising mechanical performance. Hybrid combinations of SCMs such as fly ash, silica fume, and metakaolin, alongside nanomaterials like carbon nanotubes and nanoclay, demonstrated synergistic effects in enhancing strength while lowering environmental impact. The results confirm that intelligent integration of AI techniques with material science can significantly advance the development of high-performance, low-carbon concrete, offering a scalable solution for the construction industry's sustainability goals.

کلیدواژه ها:

Cement Reduction ، Green Concrete ، Machine Learning ، Genetic Algorithm ، Environmental Issues