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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset

Author(s): ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2023
Page(s): 1-23
DOI: 10.1155/2023/5076429
Abstract:

The use of alternative materials and recycling in construction has gained popularity in recent years as part of the industry’s commitment to sustainability. One such material, recycled aggregates, has been extensively studied over the past two decades for its potential to replace natural aggregates in cement-based composites. However, the unique properties of recycled aggregates make traditional concrete mix design methods ineffective in determining their target compressive strength. To address this challenge, four machine learning models based on ensemble learning algorithms, including CatBoost regressor (CatBoost), light gradient-boosting machine regressor (LGBM), random forest regressor (RFR), and extreme gradient-boosting regressor (XGBoost), were employed to predict the compressive strength of recycled aggregate concrete. Results demonstrate that the proposed models are highly accurate and generalizable, with high coefficients of determination and low error predictions. The CatBoost model performed the best, exhibiting an R2 of 0.938 and low mean absolute error and root mean squared error values of 2.639 and 3.885, respectively, in the blind evaluation process. Although the random forest regression algorithm performed the least well among the four models, it still outperformed conventional machine learning algorithms such as support vector machines and artificial neural networks. The findings in this study suggested that the CatBoost model is the optimal choice for predicting concrete’s compressive strength due to its high accuracy and low prediction error.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1155/2023/5076429.
  • About this
    data sheet
  • Reference-ID
    10727319
  • Published on:
    30/05/2023
  • Last updated on:
    30/05/2023
 
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