Computers and Concrete

  • 제34권3호
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  • Pages.279-296
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  • 2024
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A generalized explainable approach to predict the hardened properties of self-compacting geopolymer concrete using machine learning techniques

  • Endow Ayar Mazumder (Department of Civil Engineering, National Institute of Technology Silchar) ;
  • Sanjog Chhetri Sapkota (Department of Civil Engineering, Sharda University) ;
  • Sourav Das (Department of Civil Engineering, Barak Valley Engineering College) ;
  • Prasenjit Saha (Department of Civil Engineering, ICFAI University) ;
  • Pijush Samui (Department of Civil Engineering, National Institute of Technology)
  • 투고 : 2023.09.05
  • 심사 : 2024.02.05
  • 발행 : 2024.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R2 values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.

키워드

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