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Maximum seismic shear stresses (t max) have been recognized as one of the important parameters in design practice. This study develops ground-motion parameters for t max and implements these in probabilistic seismic hazard analysis to provide the t max distribution of deep soil layers for design purposes. The application of improved ground-motion p...
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... for more than 10 million people and 700 thousand tons of cargo each year. The airport's land was reclaimed in the late 1950s and expanded until the mid-1960s from tidal marshes and shallow water areas. The site has relatively high seismicity because of the Hayward fault and the San Andreas fault, located 6-km east and 20-km west, respectively (Fig. 1). Fig. 2 shows the representative soil profile at the airport; this informa- tion was taken from previous studies (Kayen and Mitchell 1997;Arulnathan et al. 2009). The 3-5-m-thick artificial fill exists at the ground surface, and it primarily comprises sand and silt with standard penetration test (SPT) blow counts (N) of 6-20. A Young ...
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... . 10 illustrates the variations in CMS given I tau at 10% prob- ability of exceedance in 50 years, as obtained by Eq. (22) compared with UHS. The CMS decreases at a period of 0.1 s with increasing contribution of S 1 to PGA. On the other hand, the CMS increases at 1.0 s with rising contribution, indicating that the shorter-period motion ...
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... t max distributions are calculated given PGA, UHS, and I tau at 10% probability of exceedance in 50 years. Fig. 11 shows the variations in these calculations against depth. The PGA-based t max distribution is obtained from Eq. (1) with the expected values of r d by Kishida et al. (2009b) in Eq. (8) using the CMS in Fig. 10. Similarly, the UHS-based t max distribution is calculated from Eqs. (1) and (8) with the UHS in (Table 2) at a depth of 20 m. ...
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... t max distributions are calculated given PGA, UHS, and I tau at 10% probability of exceedance in 50 years. Fig. 11 shows the variations in these calculations against depth. The PGA-based t max distribution is obtained from Eq. (1) with the expected values of r d by Kishida et al. (2009b) in Eq. (8) using the CMS in Fig. 10. Similarly, the UHS-based t max distribution is calculated from Eqs. (1) and (8) with the UHS in (Table 2) at a depth of 20 m. The corresponding total vertical stress is 373 kPa (Fig. 2). Given that I tau is 0.38 g in Fig. 7, t max at a 20-m depth is calculated as lnðt max Þ ¼ lnð0:38 gÞ þ lnð373 kPaÞ 2 lnðgÞ 2 ...
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... PGA-based t max distribution is approximately identical to that given I tau at a depth of less than 7 m. This result is attributed to the fact that I tau is approximately the same as PGA at a period of less than 0.2 s, as indicated by the smaller c 1 values in Table 2 and as illustrated by the similar frequency contents of CMS in Fig. 10. However, the difference between these increases with increasing depth because I tau deviates from PGA given the larger c 1 values in Table 2, as well as the dominance of longer periods of CMS in Fig. 10. As a result, PGA-based t max provides reasonable estimates only for near the ground surface and underestimates by 17% and 28% at ...
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... at a period of less than 0.2 s, as indicated by the smaller c 1 values in Table 2 and as illustrated by the similar frequency contents of CMS in Fig. 10. However, the difference between these increases with increasing depth because I tau deviates from PGA given the larger c 1 values in Table 2, as well as the dominance of longer periods of CMS in Fig. 10. As a result, PGA-based t max provides reasonable estimates only for near the ground surface and underestimates by 17% and 28% at depths of 16 and 20 m, respectively, compared with I tau -based t max . Therefore, these differences have a similar order compared with the model biases of r d as described in the residual analysis in Table ...
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... by Idriss (1999) or Cetin et al. (2004) are used in PSHA because the variations in frequency contents do not affect the model given PGA and earthquake magnitude. Therefore, these underestimations of t max are reasonably evaluated by Kishida et al. (2009b) given that these can reflect the variations in frequency contents through parameter S 1 . Fig. 11 also shows the UHS-based t max distribution with r d values by Kishida et al. (2009b). It indicates that UHS provides better estimates of t max with underestimations by 4 and 10% at depths of 16 and 20 m, respectively, compared with those by I tau . This result stems from the fact that the inclusion of UHS of long-period motions are ...
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Citations
... Kramer and Mayfield (2007) considered the combination of PGA and M w for liquefaction analysis because the seismic demand of the liquefaction potential depends on both parameters. Kishida and Tsai (2013) developed the ground-motion parameter for maximum seismic shear stress (t max ) by combining spectral acceleration (Sa) values (PGA, 0.2 and 1.0 s, respectively) and incorporated these values into PSHA. These studies demonstrate the importance of considering additional dependent parameters when estimating seismic demand for design purposes. ...
... K max distributions are calculated inFig. 17 by multiplying PGA with r d by Kishida et al. (2009a), where r d values are conditioned on UHS [a UHS-based approach as defined by Kishida and Tsai (2013)] and CMS given PGA [a PGA-based approach as defined by Kishida and Tsai (2013)] inFig. 15. ...
... K max distributions are calculated inFig. 17 by multiplying PGA with r d by Kishida et al. (2009a), where r d values are conditioned on UHS [a UHS-based approach as defined by Kishida and Tsai (2013)] and CMS given PGA [a PGA-based approach as defined by Kishida and Tsai (2013)] inFig. 15. ...
The conventional liquefaction potential assessment methods (also known as simplified methods) profoundly rely on empirical correlations based on observations from case histories. A probabilistic framework is developed to incorporate uncertainties in the earthquake ground motion prediction, the cyclic resistance prediction, and the cyclic demand prediction. The results of a probabilistic seismic hazard assessment, site response analyses, and liquefaction potential analyses are convolved to derive a relationship for the annual probability and return period of liquefaction. The random field spatial model is employed to quantify the spatial uncertainty associated with the in-situ measurements of geotechnical material.
Equivalent number of cycles (Ncyc) is a significant factor in assessing liquefaction potential during earthquakes. However, the effects of Ncyc are considered a deterministic value by using a magnitude scaling factor, and the uncertainties of these effects have not been included in the current design practice. This paper calculates Ncyc within the soil mass by deconvolution analysis as a function of the period of soil layer from the ground surface (Ts) and the soil property (b) using a wide range of acceleration time histories. The predictive model of Ncyc is developed as variable dependent on earthquake magnitude, peak ground acceleration (PGA), and ratio of spectral acceleration, Ts and b. Then, the Ncyc model is combined with the ground-motion prediction equation of PGA and the prediction equation of seismic shear-stress reduction coefficient (rd) to obtain the prediction equation of the seismic demand of the liquefaction potential (K1cyc). The standard deviation of K1cyc is also modeled on the basis of the aleatory variability of PGA, rd, and Ncyc with correlations between these residuals. This predictive model of K1cyc is implemented in probabilistic seismic hazard analysis (PSHA) to show the impact of these uncertainties on design practice.
