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Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

Author

Listed:
  • Celal Cakiroglu

    (Department of Civil Engineering, Turkish-German University, 34820 Istanbul, Turkey)

  • Gebrail Bekdaş

    (Department of Civil Engineering, Istanbul University-Cerrahpasa, 34320 Istanbul, Turkey)

  • Sanghun Kim

    (Department of Civil and Environmental Engineering, Temple University, Philadelphia, PA 19122, USA)

  • Zong Woo Geem

    (Department of Smart City & Energy, Gachon University, Seongnam 13120, Korea)

Abstract

Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.

Suggested Citation

  • Celal Cakiroglu & Gebrail Bekdaş & Sanghun Kim & Zong Woo Geem, 2022. "Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14640-:d:965609
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    References listed on IDEAS

    as
    1. Quang Hung Nguyen & Hai-Bang Ly & Lanh Si Ho & Nadhir Al-Ansari & Hiep Van Le & Van Quan Tran & Indra Prakash & Binh Thai Pham, 2021. "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, February.
    2. Hamed Safayenikoo & Fatemeh Nejati & Moncef L. Nehdi, 2022. "Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
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    Cited by:

    1. Celal Cakiroglu & Gebrail Bekdaş, 2023. "Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    2. Yaren Aydın & Ümit Işıkdağ & Gebrail Bekdaş & Sinan Melih Nigdeli & Zong Woo Geem, 2023. "Use of Machine Learning Techniques in Soil Classification," Sustainability, MDPI, vol. 15(3), pages 1-18, January.

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