Session

Civil Engineering, Infrastructure and Environment

Description

Corrosion-induced bond strength reduction is a critical problem in infrastructure maintenance and repair. This study investigates several machine learning techniques, i.e., SVR, XG Boost, and random forest, for predicting the ultimate bond behavior between corroded reinforcement and concrete. In this study author employed 218 datasets of corroded samples collected from past studies containing input and output parameters used for predicting the models. The model's performance was evaluated and compared using various performance metrics, i.e., MAE, RMSE, MAPE, and MASE. The results show that random forest algorithms can reliably estimate ultimate bond strength with an RMSE value of 1.26 over SVR and XG Boost models. This research helps in efficient structural evaluations and maintenance planning for corroded reinforced concrete buildings.

Keywords:

Corrosion, Bond Strength, Random Forest, Support Vector Regression, XG Boost, Machine Learning

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-550-95-6

Location

UBT Lipjan, Kosovo

Start Date

28-10-2023 8:00 AM

End Date

29-10-2023 6:00 PM

DOI

10.33107/ubt-ic.2023.371

Included in

Engineering Commons

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Oct 28th, 8:00 AM Oct 29th, 6:00 PM

Comparative Assessment Using Machine Learning Algorithms for Ultimate Bond Strength Estimations

UBT Lipjan, Kosovo

Corrosion-induced bond strength reduction is a critical problem in infrastructure maintenance and repair. This study investigates several machine learning techniques, i.e., SVR, XG Boost, and random forest, for predicting the ultimate bond behavior between corroded reinforcement and concrete. In this study author employed 218 datasets of corroded samples collected from past studies containing input and output parameters used for predicting the models. The model's performance was evaluated and compared using various performance metrics, i.e., MAE, RMSE, MAPE, and MASE. The results show that random forest algorithms can reliably estimate ultimate bond strength with an RMSE value of 1.26 over SVR and XG Boost models. This research helps in efficient structural evaluations and maintenance planning for corroded reinforced concrete buildings.