Towards fully data driven ground-motion prediction models for Europe
By Boumédiène Derras, Pierre-Yves Bard, F. Cotton. Bulletin of Earthquake Engineering, 2014
165 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Geophysics
View Paper: https://www.semanticscholar.org/paper/377b05c786a6047d77e2ff3a8825f56a528318d6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Majid Ebad Sichani
- Jamie E. Padgett
- Reginald DesRoches
- N. Kuehn
- Marcel Hermkes
- Carsten Riggelsen
- Gregory C. Beroza
- Yazhou Xie
In Geophysics
- Gregory C. Beroza
- S. M. Mousavi
- Weiqiang Zhu
- Y. Sheng
- Karianne J. Bergen
- P. Johnson
- Peter Gerstoft
- Majid Ebad Sichani
Citations in the corpus
- STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019, methodology): “Linear regression analysis is commonly used for developing ground-motion prediction equations [79], [80].”
- Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression (2014, methodology): “There have been several attempts to develop GMPEs without relying on a fixed, prespecified form, using non-parametric regression approaches (e.g Anderson and Lei 1994; Derras et al. 2012, 2013; Kuehn et al. 2009; Tezcan and Cheng 2012; Tezcan and Piolatto 2012).”
- Machine Learning in Seismology: Turning Data into Insights (2018, background): “Derras et al. (2014, 2016) generalized this approach to multiple target variables, including PGA but also peak ground velocity and pseudospectral accelerations at periods of interest for structural design.”
- The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, methodology): “The ML tools utilized in GMPEs include the ANN (the top row in Figure 3) (Bakhshi et al., 2014; Derras et al., 2014; Dhanya and Raghukanth, 2018; Gu¨llu¨ and Erc x elebi, 2007; Kerh and Ting, 2005; Khosravikia et al., 2019), genetic programming (GP) (Cabalar and Cevik, 2009), multi-expression…”
- Machine learning for data-driven discovery in solid Earth geoscience (2019, methodology): “ANNs have been used to estimate short-period response spectra (62), to model ground motion prediction equations (22), to assess data quality”