Health Monitoring of Main Columns in High-Rise Buildings Using Gradient Boosting

Abstract

This research investigates the application of machine learning, specifically Gradient Boosting, to predict the structural performance of main columns in high-rise buildings subjected to various vertical and lateral loading conditions. The study focuses on utilizing Gradient Boosting, an ensemble learning technique, to address the nonlinear behavior of structural components and predict key parameters such as vertical displacement (Dis(vert)), lateral displacements in the X and Y directions (Dis(X), Dis(Y)), maximum axial normal stress (Avr(sig-max)), and maximum shear stress (avr(tau-max)). A comprehensive GridSearchCV process, involving 3 folds for each of 972 candidates, totaling 2916 fits, was used for hyperparameter optimization to enhance model accuracy. The model demonstrated exceptional performance, with R² values consistently above 0.99 for all target variables, indicating its high reliability in predicting structural behavior. The results show that the model can accurately predict displacement and stress values, which are critical for evaluating the stability and integrity of high-rise buildings under dynamic loads. Additionally, the model exhibited strong potential for early damage detection and predictive maintenance, enabling the identification of structural anomalies before they lead to critical failure. The ability to predict future deterioration trends further supports the development of proactive maintenance strategies. Overall, this study highlights the significant potential of machine learning in Structural Health Monitoring (SHM), offering a powerful tool for real-time monitoring, predictive maintenance, and safety management in high-rise buildings. Future research could expand the model’s capabilities by integrating additional environmental data or exploring its applicability to other types of structures.