Explainable machine learning-aided efficient prediction model and software tool for bond strength of concrete with corroded reinforcement
Author | Wakjira, Tadesse G. |
Author | Abushanab, Abdelrahman |
Author | Alam, M. Shahria |
Author | Alnahhal, Wael |
Author | Plevris, Vagelis |
Available date | 2024-08-08T05:23:23Z |
Publication Date | 2024 |
Publication Name | Structures |
Resource | Scopus |
ISSN | 23520124 |
Abstract | The bond strength between concrete and reinforcement is crucial for the composite action and serviceability of reinforced concrete (RC) structures. However, it is vulnerable to deterioration from the corrosion of reinforcement bars, especially in marine structures. Thus, a precise and reliable model for the bond strength in corrosive environments is necessary to evaluate the serviceability and structural performance of corroded RC members. This study employs explainable machine learning (ML) techniques to assess the bond strength between concrete and corroded bars. Eight ML models are developed to establish the best predictive model for bond behavior, considering seven input parameters: corrosion level (CL), steel yield strength, compressive strength of concrete, concrete cover-to-bar diameter ratio, bar diameter-to-bonded length ratio, reinforcement type, and test type. The super learner (SL) model, integrating three ML models, outperforms other models and analytical methods with a large R2 value (98% on the test set) and minimal statistical errors. The SHapley Additive exPlanation (SHAP) technique identifies CL as the most influential parameter on bond strength, while the reinforcement and test types have the least effect. Finally, a user-friendly graphical user interface (GUI) tool is established to facilitate the practical implementation of the developed model and support accurate bond strength prediction in concrete with steel reinforcement under corrosive environments. |
Sponsor | This publication was made possible by GSRA grant GSRA6-1-0509-19022 from the Qatar National Research Fund (QNRF, a member of Qatar Foundation). Also, the financial support from Qatar University through grant no. QUST-1-CENG-2020-17 is acknowledged. The findings achieved herein are solely the responsibility of the authors. |
Language | en |
Publisher | Elsevier |
Subject | Bond strength Concrete Corrosion Graphical user interface Machine learning SHAP |
Type | Article |
Volume Number | 59 |
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Civil and Environmental Engineering [850 items ]