A recurrent-neural-network-based generalized ground-motion model for the Chilean subduction seismic environment. (January 2023) - Fayaz, Jawad; Medalla, Miguel; Torres-Rodas, Pablo; Galasso, Carmine
Highlights: A generalized ground motion model (GGMM) is developed using recurrent neural networks to estimate a vector of 35x1 intensity measures (IMs). The proposed GGMM leads to higher mean prediction accuracy than conventional ground motion models of Chile while maintaining the IM internal cross-dependencies. Utilization of a multi-objective function is demonstrated in conjunction with the GGMM to select ground motions for time-history analysis of a 20-story structure. Abstract: This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and intraslab subduction earthquakes recorded in Chile. A total of ∼7000 ground-motion records from ∼1700 events are used to train the proposed GGMM. Unlike common ground-motion models (GMMs), which generally consider individual ground-motion intensity measures such as peak ground acceleration and spectral accelerations at given structural periods, the proposed GGMM is based on a data-driven framework that coherently uses recurrent neural networks (RNNs) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (denoted as IM ). The IM vector includes geometric mean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration (denoted as I a geom, PGV geom, PGA geom, and D 5 - 95 geom, respectively), and R o t D 50 spectral accelerations at 31 periods between 0.05 and 5 s for a 5 % damped oscillator (denoted as S a ( T ) ). The inputs to the GGMM include six causal seismic source and site parameters, including fault slab mechanism, moment magnitude, closest rupture distance, Joyne-Boore distance, soil shear-wave velocity, and hypocentral depth. The statistical evaluation of the proposed GGMM shows high prediction power with R 2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, the GGMM is carefully compared against two state-of-the-art Chilean GMMs, showing that the proposed GGMM leads to better goodness of fit for all periods of S a ( T ) compared to the two considered GMMs (on average 0.2 higher R 2 ). Finally, the GGMM is implemented to select hazard-consistent ground motions for nonlinear time history analysis of a sophisticated finite-element model of a 20-story steel special moment-resisting frame. Results of this analysis are statistically compared against those for hazard-consistent ground motions selected based on the conditional mean spectrum (CMS) approach. In general, it is observed that the drift demands computed using the two approaches cannot be considered statistically similar and the GGMM leads to higher demands.