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A definition of EPA, defined as the spectral acceleration at period 0.2 second divided by an average amplification factor of 2.5 is proposed based on statistical analysis of 154 existing accelerograms recorded at rock sites.
For each site, the geometric average amplification factor is derived from these 14 different loadings, and an artificial neural network approach is used to investigate the correlation between this average amplification factor and various sets of soil characteristics.
The main goal is to ensure a representative average amplification factor that is unbiased by spectral contents too rich in either short or long periods.
This layer greatly contributes to the amplification factor in the period range of 0.1 2.5 s, as indicated by the fact that AVS5 and AVS10 showed a better correlation with the average amplification factor than AVS30 did.
Fig. 12 Relationship between average amplification factor and AVs30 values for 29 sites with respect to engineering bedrock (Vs = 780 m/s).The amplification factors are calculated in the frequency band of 0.4 10 Hz.
The overall average amplification factor is close to 1 at long period (because long wavelengths do not "feel" the site structure over the first hundred meters), and it exhibits a very smooth and broad maximum with a value around 2 between 0.1 and 0.2 s.
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We used average amplification factors at frequencies between 0.4 and 10 Hz (Fig. 12).
Fig. 5 Average amplification factors as a function of real period for each set of soil profiles.
Fig. 6 Average amplification factors as a function of normalized frequency for each set of soil profiles.
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).
For each profile set, we compute the (n_{P} times 14) AF: ({text{AF}}left( {P_{k},theta,S_{l},T_{i} } right)), the (n_{P}) average amplification factors AFm: ({text{AF}}_{text{m}} left( {P_{k},theta,T_{i} } right)) together with their corresponding variability (sigma_{text{AF}} left( {P_{k},theta,T_{i} } right)).
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Justyna Jupowicz-Kozak
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