Work

Support Vector Machines to Propose a Ground Motion Prediction Equation for the Particular Case of the Bojorquez Intensity Measure INp

Year: 2025

Type: article

Abstract: This study proposes the first ground motion prediction equation (GMPE) for the parameter INp, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. INp also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T1). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of INp for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity.

Source: AI

DOI: 10.3390/ai6100254