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The role of interdependencies in infrastructure modeling and community resilience
Published in Paolo Gardoni, Routledge Handbook of Sustainable and Resilient Infrastructure, 2018
Power delivery systems perform well in the US for minor and moderate seismic events, so most derivations of fragilities and recovery have been determined using data from outside the US. The two events discussed in this chapter are the 2010 Chilean Earthquake and the Tohoku 2011 Earthquake and Tsunami in Japan. The USGS shakemap adapted for grayscale using ArcGIS for instrumental intensity is shown for the 2010 Chile Earthquake in Figure 25.9. Infrastructure damage data were available from Duenas-Osorio and Kwasinski (2012) for the two regions of Maule (Region VII) and Bio-Bio (Region VIII).
Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis
Published in Geomatics, Natural Hazards and Risk, 2023
Huaiqun Zhao, Yijiao Jia, Wenkai Chen, Dengjie Kang, Can Zhang
ShakeMap, as an important international platform for earthquake disaster information services, has applicable ground motion attenuation relationships set for different earthquake magnitudes and different regions, and constantly updates the earthquake disaster damage assessment results with the accumulation of information on the affected areas (Wald et al. 2006; Worden et al. 2020). As shown in Figure 8b, we obtained the most recent version (version 5) of the ShakeMap intensity assessment findings and compared them to our own intensity evaluation results. ShakeMap’s greatest intensity rating is VIII degrees, which is compatible with our highest intensity value when measured using the MMI scale. Statistics of the hardest-hit areas for various intensity outcomes are shown in Table 2. The area of the hardest-hit regions predicted by ShakeMap was closest to the findings of the field survey, but the location and extent diverged significantly from the actual intensity distribution. The earthquake strength projected by ShakeMap, particularly the size of the hardest-hit regions, may include significant mistakes due to incomplete seismogenic region data. The location and size of the hardest-hit regions as assessed by aftershocks in the buffer were in excellent agreement with the real scenario, and the intensity zones as assessed by the 1.5-km radius buffer selection for aftershocks did not differ noticeably from the findings of the 1-km radius buffer screening. This demonstrates that the method proposed in this study may accurately pinpoint the size of the worst-hit regions.
Development of empirical relationship between the observed and the estimated ground acceleration values of small to moderate earthquakes in northwest (Gujarat) and northeast (NE) regions of India
Published in Geomatics, Natural Hazards and Risk, 2022
Pallabee Choudhury, Ketan Singha Roy, Charu Kamra, Sumer Chopra
Immediately after an earthquake, the shaking level is generally presented by a ShakeMap. A ShakeMap is simple but approximated, due to the simple formalism of the GMPEs. GMPEs predict a ground shaking that is more uniform than it would be expected for actual earthquake. One of the important limitations of the ShakeMap is that it fails to show the directivity and local site-effect. In view of this, many times, the ground motions are underestimated. This will be reflected in ground motion maps, which we have prepared using the relationships developed for the Gujarat and the NE India region. Using the developed relationship, we prepare a ground motion map, which is a representation the PGAs of a M5.2 earthquake that occurred in Kachchh on 19 June 2012 (Figure 7(a)). This was referred as the Dholavira earthquake, having a strike-slip mechanism (Choudhury et al. 2016). Though the event was well recorded at 15 SMA sites, we incorporated data of 16 BBS sites after converting their amplitudes to PGA using the developed empirical relationship. We used data from 31 stations spread widely all over Gujarat region (Figure 7(a)) and prepared a PGA map. From Figure 7(a), it is evident that the directivity effect is well-captured as we can see that the lobes are elongated in the strike direction, both close and away from the fault. If we use simple GMPE for plotting, then circular concentric circles will be formed for each PGA and directivity and local site-effect will not be captured. To test another scenario, we prepared a ground motion distribution map of the 14 June 2020 Bhachau earthquake (M5.3) using data from 27 stations (Figure 7(b)). Along with data from 15 SMAs, we used data from 12 BBS stations, converted them into accelerations using the developed relationship. The earthquake occurred on a ESE-WNW reverse fault, dipping towards south. Here, in this case also, the directivity effect is clearly reflected. It is observed that at nearly the same distance, the sites on the hanging wall side experienced higher PGAs than those on footwall side. If we compare Figure 7(b) with Figure 7(a), we see that the ground motions generated by the M5.2 earthquake is less (max PGA ∼ 0.1 g) than those generated by the M5.3 earthquake (max PGA∼0.12g), though the magnitude difference is very minor. Also, the distribution of ground motions are entirely different. It is a well-known fact that the strike-slip, thrust, or normal-faulting earthquakes differ systematically. The same size reverse earthquake at a same distance to the site and site condition generates 20–30% larger ground motion than a strike-slip earthquake (Somerville and Abrahamson 1995; Spudich et al. 1996; Bolt and Abrahamson 2004).