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A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete

Author

Listed:
  • Akshita Bassi

    (Dr B R Ambedkar National Institute of Technology)

  • Aditya Manchanda

    (Dr B R Ambedkar National Institute of Technology)

  • Rajwinder Singh

    (Dr B R Ambedkar National Institute of Technology)

  • Mahesh Patel

    (Dr B R Ambedkar National Institute of Technology)

Abstract

The cementitious behavior of Rice Husk Ash (RHA) has caused its possible addition as a replacement material for cement which has been proven to influence the strength of concrete. In this study, Machine Learning (ML) algorithms have been used to predict the compressive strength of RHA-based concrete in a shorter period without any errors. In this regard, six different ML techniques, i.e., Linear Regression, Decision Tree, Gradient Boost, Artificial Neural Network, Random Forest and Support Vector Machines, have been employed to predict the compressive strength using twelve input features and 462 data points. The performances of models have been checked using errors, Pearson correlation coefficient (R2), Taylor’s diagram, box plots and Sensitivity analysis. The outcome of this study indicated that the Decision Tree, Gradient Boost, and Random Forest models had provided better results (R2 > 0.92) than the other algorithms in terms of minimal errors and high accuracy in predicting compressive strength. The sensitivity analysis indicated that the specific gravity of RHA and water–cement ratio significantly (more than 95%) impact the compressive strength of the RHA-based concrete in contrast to the other parameters.

Suggested Citation

  • Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    • Handle: RePEc:spr:nathaz:v:118:y:2023:i:1:d:10.1007_s11069-023-05998-9
    DOI: 10.1007/s11069-023-05998-9
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    References listed on IDEAS

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    1. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    2. Safar Marofi & Hossein Tabari & Hamid Abyaneh, 2011. "Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1417-1435, March.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Pengzhen Lu & Shengyong Chen & Yujun Zheng, 2012. "Artificial Intelligence in Civil Engineering," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-22, December.
    5. Ankit Gautam & Rahul Batra & Nishant Singh, 2019. "A Study On Use Of Rice Husk Ash In Concrete," Engineering Heritage Journal (GWK), Zibeline International Publishing, vol. 3(1), pages 1-4, January.
    6. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    7. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
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    Cited by:

    1. Miljan Kovačević & Marijana Hadzima-Nyarko & Ivanka Netinger Grubeša & Dorin Radu & Silva Lozančić, 2023. "Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash," Mathematics, MDPI, vol. 12(1), pages 1-25, December.

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