Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete

Author:

Mahmoudian Alireza,Bypour Maryam,Kontoni Denise-Penelope N.ORCID

Publisher

Springer Science and Business Media LLC

Reference66 articles.

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2. Alibrahim, H., & Ludwig, S. A. (2021). Hyperparameter optimization: comparing genetic algorithm against grid search and bayesian optimization. 2021 IEEE congress on evolutionary computation (CEC) (pp. 1551–1559). Kraków, Poland: IEEE.

3. Almejrb, R. M., Sallabi, O. M., & Mohamed, A. A. (2022). Applying C atboost Regression Model for Prediction of House Prices. 2022 International conference on engineering & MIS (ICEMIS) (pp. 1–7). Istanbul, Turkey: IEEE.

4. Arora, H. C., Kumar, S., Kontoni, D.-P. N., Kumar, A., Sharma, M., Kapoor, N. R., & Kumar, K. (2022). Axial capacity of FRP-reinforced concrete columns: computational intelligence-based prognosis for sustainable structures. Buildings, 12(12), 2137. https://doi.org/10.3390/buildings12122137

5. Bai, G.-L., Wu, S.-H., & Li, X.-W. (2010). Investigation of Bond-slip Behavior between Recycled Concrete and Steel Bars under Pull-out Test. In J. Z. Xiao, Y. Zhang, M. S. Cheung, & R. P. K. Chu (Eds.), 2nd International conference on waste engineering and management—ICWEM 2010 (pp. 628–637). RILEM Publications SARL.

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