Deriving amplification factors from simple site parameters using generalized regression neural networks: implications for relevant site proxies

Abstract

Most modern seismic codes account for site effects using an amplification factor (AF) that modifies the rock acceleration response spectra in relation to a "site condition proxy, " i.e., a parameter related to the velocity profile at the site under consideration. Therefore, for practical purposes, it is interesting to identify the site parameters that best control the frequency-dependent shape of the AF. The goal of the present study is to provide a quantitative assessment of the performance of various site condition proxies to predict the main AF features, including the often used shortand mid-period amplification factors, F a and F v , proposed by Borcherdt (in Earthq Spectra 10: 1994). In this context, the linear, viscoelastic responses of a set of 858 actual soil columns from Japan, the USA, and Europe are computed for a set of 14 real accelerograms with varying frequency contents. The correlation between the corresponding site-specific average amplification factors and several site proxies (considered alone or as multiple combinations) is analyzed using the generalized regression neural network (GRNN). The performance of each site proxy combination is assessed through the variance reduction with respect to the initial amplification factor variability of the 858 profiles. Both the whole period range and specific short-and mid-period ranges associated with the Borcherdt factors F a and F v are considered. The actual amplification factor of an arbitrary soil profile is found to be satisfactorily approximated with a limited number of site proxies (4-6). As the usual code practice implies a lower number of site proxies (generally one, sometimes two), a sensitivity analysis is conducted to identify the "best performing" site parameters. The best one is the overall velocity contrast between underlying bedrock and minimum velocity in the soil column. Because these are the most difficult and expensive parameters to measure, especially for thick deposits, other more convenient parameters are preferred, especially the couple (V s30 , f 0 ) that leads to a variance reduction in at least 60%. From a code perspective, equations and plots are provided describing the dependence of the short-and mid-period amplification factors F a and F v on these two parameters. The robustness of the results is analyzed by performing a similar analysis for two alternative sets of velocity profiles, for which the bedrock velocity is constrained to have the same value for all velocity profiles, which is not the case in the original set.

Indicators for site characterization at seismic station: recommendation from a dedicated survey

“…As also highlighted by Zhu et al (2020), the period-dependency of site amplification implies that there is no such single proxy that performs the best over the whole frequency band. To improve the site amplification estimation, it is then more viable to use a combination of site proxies than to use a single, predictor variable (Trifunac 2016;Boudghene-Stambouli et al 2017;Derras et al 2017;Zhu et al 2020 and references therein;Felicetta et al 2021).…”

“…Because of the combination of this concern and of various recent studies and papers, discussing the limitations of V S30 taken as a single indicator and proposing other proxies (Trifunac 2016;Boudghene-Stambouli et al 2017;Derras et al 2017;Zhu et al 2020;Bergamo et al 2021;Felicetta et al 2021), we thought it timely to question the scientific and engineering community about the optimal site proxies to be used in the future for improved ground motion predictions. The results of this inquiry are discussed in two companion papers.…”

Influence of the variability of soil profile properties on weak and strong seismic response

“…An optimal selection of site parameters is an important tool for the prediction of the expected ground motion. Based on recent results [14,[22][23][24], the site dominant frequency 0 f and the shear wave velocity gradient 30 B are chosen as complementary proxies in this study.…”

“…Stambouli et al [22] conduct a numerical investigation on 858 soil columns corresponding to real sites profiles from Japan, USA, and Europe. They show that the best performing site proxy is the impedance contrast between bedrock velocity and minimum surface velocity but even the pair ( ,30 s v , 0 f ) can reduce significantly the variability of the site response at least around 60%.…”

Special issue “Effect of surface geology on seismic motion: challenges of applying ground motion simulation to seismology and earthquake engineering”

“…They also identify significant site response nonlinearities and discuss the ability of various site proxies and loading to account for them. The objective of Stambouli et al (2017) is similar, i.e., to identify optimal site condition proxies, but their approach is based exclusively on 1D, linear numerical simulations, together with the use of a special kind of neural network to derive appropriate relationships between various site parameters and site amplification functions.…”