ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra

Abstract

Vertical ground motion plays a crucial role in the design of critical structures, with recorded vertical ground motions often surpassing their horizontal counterparts in the near-field. This study proposes a new simplified Ground Motion Model (GMM) for vertical spectra by combining comprehensive datasets from the NESS and NGA-West2 databases. The proposed Artificial Neural Network (ANN) architecture-based model requires only 288 unknowns to predict spectral accelerations ($Sa$) at 33 distinct periods ranging from 0 to 4 seconds. Notably, this model inherently captures known physical phenomena with reduced variability using a minimum number of unknowns compared to the GMMs existing literature, thus offering a valuable addition to current hazard estimation frameworks. Furthermore, recognizing the necessity for physics-based simulations in vertical ground motion analysis, we introduce a novel physics-based broadband model for vertical spectra using ANN methodology. The proposed broadband model exhibits better robustness due to the comprehensiveness of the dataset utilized and the inclusion of source path and site characteristics at the input layer. Additionally, the model effectively captures the physical trends with minimal deviation. Further, a comparison of the model's output with physics-based simulations for the 2008 Mw 6.9 Iwate earthquake and recorded data showcase its ability to accurately capture the non-ergodic nature of ground motion. The proposed models serve as essential tools for physics-based broadband simulations and hazard assessments in active shallow crustal regions.