Fayaz, Jawad;
Galasso, Carmine;
(2022)
A generalized ground-motion model for consistent mainshock-aftershock intensity measures using successive recurrent neural networks.
Bulletin of Earthquake Engineering
10.1007/s10518-022-01432-w.
(In press).
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Abstract
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 IMMS) and aftershocks (denoted as IMAS) using a framework of successive long-short-term-memory (LSTM) recurrent neural network (RNN). The vectors of IMMS and IMAS consist of geometric means of significant duration (D5-95,geom), Arias intensity (Ia,geom), cumulative absolute velocity (CAVgeom), peak ground velocity (PGVgeom), peak ground acceleration (PGAgeom) and RotD50 spectral acceleration (Sa(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 IMMS and IMAS. The proposed framework is finally illustrated to select MS–AS ground motions based on IMMS and IMAS 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.
| Type: | Article |
|---|---|
| Title: | A generalized ground-motion model for consistent mainshock-aftershock intensity measures using successive recurrent neural networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10518-022-01432-w |
| Publisher version: | https://doi.org/10.1007/s10518-022-01432-w |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Science & Technology, Technology, Physical Sciences, Engineering, Geological, Geosciences, Multidisciplinary, Engineering, Geology, Generalized ground motion model, Recurrent neural network, Deep learning, Mainshock, Aftershock, Ground motion selection, Ground motion sequences, SEISMIC PERFORMANCE, DEMAND, HAZARD, DAMAGE |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152462 |
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