@inproceedings{d208b887a8e441108672384b4faaf99b,
title = "A Data-Driven Approach to Evaluate the Compressive Strength of Recycled Aggregate Concrete",
abstract = "This paper examines the application of machine learning (ML) techniques in the prediction of the compressive strength of recycled aggregate concrete (RAC). The ML models are trained on a comprehensive dataset composed of 981 different RAC test results. Four algorithms of multiple linear regression, lasso regression, random forest, and histogram-based gradient boosting are investigated. The ML training workflow consists of careful feature selection and rigorous hyperparameter tuning based on cross-validation on the training set. The prediction accuracy of ML models was measured by calculating R-squared and root mean squared error metrics. Lastly, sensitivity analysis was performed to measure the impact of input features on the RAC compressive strength. The results indicate that histogram-based gradient boosting model results in the highest accuracy among the studied ML algorithms where water to cement ratio and cement content were found to be the most influential parameters.",
author = "Henry Barth and Srishti Banerji and Adams, {Matthew P.} and Esteghamati, {Mohsen Zaker}",
note = "Publisher Copyright: {\textcopyright} 2023 by the American Society of Civil Engineers. All Rights Reserved.; ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World ; Conference date: 16-11-2023 Through 18-11-2023",
year = "2023",
language = "English (US)",
series = "ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World - Selected Papers from ASCE Inspire 2023",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "433--441",
editor = "Ayyub, {Bilal M.}",
booktitle = "ASCE Inspire 2023",
address = "United States",
}