Application and Prospect of Machine Learning in Predicting the Performance of Grouted Sleeve Connections

Abstract

As a key connecting component in prefabricated buildings, the mechanical properties of steel pipe grouted sleeves, such as tensile and compressive properties, are directly related to the safety and reliability of the overall structure. However, traditional research methods rely on physical tests and theoretical models, which have limitations such as long test cycles, high costs, and insufficient prediction accuracy for nonlinear mechanical behaviors. It is difficult to efficiently meet the performance evaluation requirements under complex working conditions. In recent years, machine learning technology has provided an innovative solution for predicting the performance of grouted sleeve connections. By mining the nonlinear relationships between parameters through a data - driven model, it significantly improves the prediction efficiency and accuracy and effectively overcomes the shortcomings of traditional methods. Research shows that there are significant differences in the prediction accuracy of different machine learning models (such as support vector machines, logistic regression, decision trees, etc.). Among them, ensemble learning algorithms (such as random forests, gradient boosting, etc.), with their high robustness and generalization ability, exhibit better prediction performance and have become a hot research direction. However, the application of machine learning in the field of grouted sleeves is still in the development stage. In the future, it is necessary to further optimize the model architecture, expand high - quality datasets, and explore more interpretable hybrid prediction models by combining mechanical mechanisms to promote the in - depth transformation of this technology into engineering practice.