Vol. 2 No. 1 (2023): The QUEST: Journal of Multidisciplinary Research and Development

Predicting Compressive Strength of M20 Concrete with Partial Replacement of Coarse Aggregates by Nueva Ecija Sourced Recycled Portland Cement Concrete Pavement using XGBoost Algorithm

Kyle Eduard Cortez
Faculty/ COE / NEUST
Nelson Notarte
University of the Cordilleras
Dexter Hansel Apnoyan
University of the Cordilleras

Published 06/30/2023


  • M20 Concrete,
  • Materials,
  • Recycled Concrete Aggregate,
  • XGBoost,
  • Machine learning

How to Cite

Cortez, K. E., Notarte, N., & Apnoyan, D. H. (2023). Predicting Compressive Strength of M20 Concrete with Partial Replacement of Coarse Aggregates by Nueva Ecija Sourced Recycled Portland Cement Concrete Pavement using XGBoost Algorithm. The QUEST: Journal of Multidisciplinary Research and Development, 2(1). https://doi.org/10.60008/thequest.v2i1.73


This research delves into the potential impact of replacing the coarse aggregates in M20 concrete with recycled concrete. The study aims to explore the possibility of recycling waste material from road construction to address sustainability challenges in the construction industry, especially in developing countries like the Philippines. The research adopts an experimental design involving preparing and testing recycled concrete aggregates, alongside sample preparation and testing. The study also employs the XGBoost machine learning algorithm to help predict the compressive strength of concrete. The XGBoost algorithm was trained using fine-tuned hyperparameters, and the model's evaluation was done using R-squared and Root Mean Squared Error. The research findings indicate a positive correlation between RCA content and compressive strength, with the average compressive strength of concrete mixtures increasing over time. The XGBoost outperforms the Multi Linear Regression model in predicting compressive strength, and incorporating RCA within the optimal range can enhance the strength properties of concrete. The study concludes that the XGBoost model with fine-tuned hyperparameters is reliable for predicting compressive strength, contributing to sustainable concrete production and reliable strength outcomes.

Full PDF Paper


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