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- W4380244349 abstract "Engineered cementitious composite (ECC) is a unique product that can significantly contribute to self-healing when continuously hydrated. To measure its self-healing capacity, it is necessary to evaluate the crack-width once the healing process is complete. ECC has a remarkable ability to self-heal, but predicting its self-healing potential is challenging. In this study, two different ensemble machine learning (ML) algorithms i.e. bagging regressor (BR) and stacking regressor (SR) were employed to estimate ECC’s self-healing capacity. Model effectiveness was assessed using error analysis and k-fold cross-validation methods. The SR model had a higher R2 and was more successful in predicting the outcomes than the BR model. However, both ensemble models with smaller error values also showed improved model performance. Furthermore, the crack-healing characteristics of wheat straw ash, rice husk ash, and pumice powder are recommended to be evaluated in future studies using ML methods." @default.
- W4380244349 created "2023-06-12" @default.
- W4380244349 creator A5004968882 @default.
- W4380244349 creator A5026368254 @default.
- W4380244349 creator A5057578694 @default.
- W4380244349 creator A5058875606 @default.
- W4380244349 date "2023-08-01" @default.
- W4380244349 modified "2023-10-16" @default.
- W4380244349 title "Forecasting the self-healing capacity of engineered cementitious composites using bagging regressor and stacking regressor" @default.
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- W4380244349 doi "https://doi.org/10.1016/j.istruc.2023.05.140" @default.
- W4380244349 hasPublicationYear "2023" @default.