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

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

doi:10.1007/s42107-024-01153-2

Asian J Civ Eng

2024

DOI: 10.1007/s42107-024-01153-2

|View full text |Cite

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

Alireza Mahmoudian,

Maryam Bypour,

Denise-Penelope N. Kontoni

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

Mahmoudian,

Bypour,

Kioumarsi

2024

Computation

View full text Add to dashboard Cite

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and tensile strength of rebars, concrete compressive strength and cover, embedment length, and test method. The dataset contains two test methods: pullout tests and beam tests. Four types of rebar, including carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), basalt, and steel rebars, were considered. The boosting ML models applied in this study include AdaBoost, CatBoost, Gradient Boosting, XGBoost, and Hist Gradient Boosting. After hyperparameter tuning, these models demonstrated significant improvements in predictive accuracy, with XGBoost achieving the highest R2 score of 0.95 and the lowest Root Mean Square Error (RMSE) of 2.21. Shapley values analysis revealed that tensile strength, elastic modulus, and embedment length are the most critical factors influencing bond strength. The findings offer valuable insights for applying ML models in predicting bond strength in FRP-reinforced UHPC, providing a practical tool for structural engineering.

show abstract

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

Mahmoudian,

Bypour,

Kioumarsi

2024

Computation

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Cited by 1 publication

References 61 publications

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

Contact Info

Product

Resources

About