Comparative Assessment Using Machine Learning Algorithms for Ultimate Bond Strength Estimations
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
Recommended Citation
Parida, Lukesh Veloso de; Moharana, Sumedha Melo de; and Giri, Sourav Kumar, "Comparative Assessment Using Machine Learning Algorithms for Ultimate Bond Strength Estimations" (2023). UBT International Conference. 36.
https://knowledgecenter.ubt-uni.net/conference/IC/civil/36
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.