Enhancing bond strength prediction at UHPC-NC interface: A data-driven approach with augmentation and explainability

• Published in: Construction & building materials Vol. 451; p. 138757
• Main Authors: Hu, Tianyu, Zhang, Hong, Khodadadi, Nima, Taffese, Woubishet Zewdu, Nanni, Antonio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2024
• Subjects: Bond strength; Data augmentation; Generative adversarial networks; Normal concrete; Ultra-high-performance concrete; Bond strength; Data augmentation; Generative adversarial networks; Ultra-high-performance concrete; Normal concrete
• ISSN: 0950-0618
• DOI: 10.1016/j.conbuildmat.2024.138757

Existing concrete structures often have difficulty reaching their design service life due to aging, increased loads, and natural disasters, and they need to be repaired and strengthened or replaced. Ultra-high-performance concrete (UHPC), known for its ultra-high strength and excellent toughness, offers a promising solution for repairing and strengthening normal concrete (NC) structures. It is expected to be a viable solution when applied to the repair and strengthening of NC structures. A reliable interfacial bonding between the two materials (NC substrate and UHPC) determines the overall performance of this composite structure. In this study, a data-driven approach is proposed to predict the bond performance at the UHPC-NC interface as accurately as possible. To overcome the problem of limited experimental data, a data augmentation model is introduced, combining kernel density estimation (KDE) and tabular generative adversarial networks (TGAN). The optimal model is determined from six decision tree-based ensemble learning models applied to two strategies: "synthetic training - real testing" and "real training - real testing." Finally, a parametric analysis is performed using SHapley Additive exPlanations (SHAP) to elucidate the importance and sensitivity of different features related to bond strength. The findings illustrate that the suggested KDE-TGAN model effectively captures the distribution of the original dataset, enhancing both the accuracy and robustness of the bond strength prediction models. Furthermore, the model's ability to explain the importance and sensitivity of different features provides valuable insights into bond strength prediction. Thus, the proposed data augmentation model provides a reliable approach for modeling experimental data with small samples in structural engineering applications. •Proposed a new solution—KDE-TGAN—for small sample machine learning modeling in structural engineering.•A review of factors affecting bond strength at the UHPC-NC interface was conducted.•The model developed to predict the bond strength under splitting tensile and slant shear tests has high accuracy and robustness.•The model explainability study conducted reveals the effect of the material and condition features on the bond strength.