Predictive Modeling of Internal Beam Health Under Earthquake Forces Using Machine Learning

Abstract

This research investigates the application of machine learning, specifically Support Vector Machine (SVM) regression with a Radial Basis Function (RBF) kernel, to predict the structural performance of internal beams in high-rise buildings subjected to earthquake-induced lateral forces. The study focuses on utilizing SVM to address the nonlinear behavior of structural components and predict key parameters such as lateral displacements in the X and Y directions (Disp(X), Disp(Y)), maximum axial normal stress (Avr(sig-max)), and maximum shear stress (Avr(tau-max)). A comprehensive hyperparameter tuning process, using GridSearch optimization with five-fold cross-validation, was conducted to enhance model accuracy and prevent overfitting. The model demonstrated exceptional performance, with R2 values exceeding 0.91 across all target variables, indicating its high reliability in predicting structural behavior. The results show that the model can accurately forecast displacement and stress distributions, which are critical for evaluating the stability and integrity of internal beams under seismic loading conditions. 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 incorporating real-time sensor data and exploring its applicability to diverse structural configurations.