Predicting the Compressive Strength of Recycled Aggregate Concrete Using  Machine Learning: A Comparative Study of ANN and Ensemble Models

Sachin Kumar,Kamlesh Chaudhary,Arti Kumari,Chandra Shekhar kumar,Sujit kumar

Predicting the Compressive Strength of Recycled Aggregate Concrete Using Machine Learning: A Comparative Study of ANN and Ensemble Models

Authors

Sachin Kumar,Kamlesh Chaudhary,Arti Kumari,Chandra Shekhar kumar,Sujit kumar

Keywords:

Recycled Aggregate Concrete; Compressive Strength; Machine Learning; Ensemble Learning; Artificial Neural Network; Sustainable Construction; XGBoost

Abstract

The global construction industry faces the dual challenge of managing vast quantities of construction anddemolition (C&D) waste and reducing its significant carbon footprint. Recycled aggregate concrete (RAC) offers a sustainable alternative to natural aggregate concrete

References

Akhtar, A., & Sarmah, A. K. (2018). Construction and demolition waste generation and properties of recycled aggregate concrete: A global perspective. Journal of Cleaner Production, *186*, 262–281.

Andrew, R. M. (2019). Global CO₂ Emissions from Cement production, 1928–2018. Earth System Science Data, *11*, 1675–1710.

Downloads

Published

2024-10-12

How to Cite

Sachin Kumar,Kamlesh Chaudhary,Arti Kumari,Chandra Shekhar kumar,Sujit kumar. (2024). Predicting the Compressive Strength of Recycled Aggregate Concrete Using Machine Learning: A Comparative Study of ANN and Ensemble Models . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8165–8176. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5231