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Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning

Author

Listed:
  • Ehsan Mansouri

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Natural Sciences, Duy Tan University, Da Nang 550000, Vietnam
    Department of Computer and Technology, Birjand University of Medical Sciences, Birjand 9717853577, Iran)

  • Maeve Manfredi

    (Department of Structural Engineering, Desimone Consulting Engineering Company, New York, NY 10005, USA)

  • Jong-Wan Hu

    (Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
    Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, Korea)

Abstract

In order to reduce the adverse effects of concrete on the environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically and environmentally sustainable alternative to portland cement. This is accomplished through the utilization of alumina-silicate waste materials as a cementitious binder. These geopolymers are synthesized by activating alumina-silicate minerals with alkali. This paper employs a three-step machine learning (ML) approach in order to estimate the compressive strength of geopolymer concrete. The ML methods include CatBoost regressors, extra trees regressors, and gradient boosting regressors. In addition to the 84 experiments in the literature, 63 geopolymer concretes were constructed and tested. Using Python language programming, machine learning models were built from 147 green concrete samples and four variables. Three of these models were combined using a blending technique. Model performance was evaluated using several metric indices. Both the individual and the hybrid models can predict the compressive strength of geopolymer concrete with high accuracy. However, the hybrid model is claimed to be able to improve the prediction accuracy by 13%.

Suggested Citation

  • Ehsan Mansouri & Maeve Manfredi & Jong-Wan Hu, 2022. "Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:12990-:d:939009
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    References listed on IDEAS

    as
    1. Bangzhu Zhu & Xuetao Shi & Julien Chevallier & Ping Wang & Yi‐Ming Wei, 2016. "An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(7), pages 633-651, November.
    2. Aman Kumar & Harish Chandra Arora & Nishant Raj Kapoor & Mazin Abed Mohammed & Krishna Kumar & Arnab Majumdar & Orawit Thinnukool, 2022. "Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    3. Mosbeh R. Kaloop & Bishwajit Roy & Kuldeep Chaurasia & Sean-Mi Kim & Hee-Myung Jang & Jong-Wan Hu & Basem S. Abdelwahed, 2022. "Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
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