Machine learning models for predicting ultimate bond strength of grouted sleeve connections

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@Article{Lou:2025:Structures,

• author = "Junbin Lou and Yixuan Li and Qian Feng and Rongqiao Xu",
• title = "Machine learning models for predicting ultimate bond strength of grouted sleeve connections",
• journal = "Structures",
• year = "2025",
• volume = "72",
• pages = "108186",
• keywords = "genetic algorithms, genetic programming, Grouted sleeve connection (GSC), Multi-fidelity modeling, Bond strength prediction, Neural networks, ANN",
• ISSN = "2352-0124",
• URL = "https://www.sciencedirect.com/science/article/pii/S2352012424023403",
• DOI = "doi:10.1016/j.istruc.2024.108186",
• abstract = "Grouted sleeve connection (GSC) is widely used in prefabricated concrete structures and accurately predicting its ultimate bond strength between rebar and grouting materials of GSC is crucial for ensuring structural safety. In this study, a parameter-sharing residual multi-fidelity neural network (PsRMFNN) is proposed to predict the ultimate bond strength of GSC. For this purpose, a database comprising 209 existing GSC tensile experimental data is established to train and evaluate the PsRMFNN. Comparative analyses are conducted with existing empirical equations, backpropagation neural networks, and residual multi-fidelity neural networks. The PsRMFNN exhibits superior performance, yielding R2 of 0.994, MAE of 6.17kN, RMSE of 12.97kN, and MAPE of 3.55percent across all data. Furthermore, comparative evaluations of the three neural network-based models show that the PsRMFNN accelerates convergence and enhances prediction accuracy. Finally, based on the sensitivity analysis, predictive equations for bond strength are developed using the genetic algorithm and the genetic programming. The outcomes demonstrate that neural network-based models and newly proposed predictive equations can effectively forecast the ultimate bond strength between rebar and grouting materials of GSC",

}

Genetic Programming entries for Junbin Lou Yixuan Li Qian Feng Rongqiao Xu

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