Several recent studies have investigated the risk posed to structures by earthquake sequences, proposing state-dependent fragility/vulnerability models for assets in damaged conditions. However, a critical component for such efforts, i.e., ground-motion record selection, has received relatively minor consideration. Specifically, utilization of “consistent” mainshock (MS)–aftershock (AS) ground motions is desirable in practical applications. Such consistency in selecting MS–AS sequences requires proper consideration of the correlations between and within the intensity measures of MS and AS ground motions. Most of the studies in this domain utilize spectral accelerations as the considered ground-motion intensity measures and rely on empirical linear correlation models between the intensity measures of MS and AS ground motions for developing, for instance, record selection approaches. This study proposes a generalized ground-motion model (GGMM) to estimate consistent 30 × 1 vectors of intensity measures for mainshocks (denoted as IM MS) and aftershocks (denoted as IM AS) using a framework of successive long-short-term-memory (LSTM) recurrent neural network (RNN). The vectors of IM MS and IM AS consist of geometric means of significant duration (D 5 - 95 , g e o m), Arias intensity (I a , g e o m), cumulative absolute velocity (CAV geom), peak ground velocity (PGV geom), peak ground acceleration (PGA geom) and RotD50 spectral acceleration (S a(T)) at 25 periods for both MS and AS ground motions. The proposed RNN-based GGMM is trained on a carefully selected set of ~ 700 crustal and subduction recorded MS–AS sequences. The inputs to the framework include a 5 × 1 vector of source and site parameters for MS and AS recordings. The residuals of the trained LSTM-based RNN are further used to develop empirical covariance structures for IM MS and IM AS. The proposed framework is finally illustrated to select MS–AS ground motions based on IM MS and IM AS using a multi-criteria objective function. The selected MS–AS ground motion sequences are then used to perform non-linear time-history analyses of a case-study two-spanned symmetric bridge structure. The obtained engineering demand parameters are evaluated and critically discussed.
Bibliographical noteFunding Information:
This research is funded by the European Union’s Horizon 2020 research and innovation program, expressly grant agreement number 821046: TURNkey “Towards more Earthquake-resilient Urban Societies through a Multi-sensor-based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response actions.”
© 2022, The Author(s).