Abstract:The JMA (Japan Meteorological Agency) seismic intensity scale has been used in Japan as a measure of earthquake ground shaking effects since 1949. It has traditionally been assessed after an earthquake based on the judgment of JMA officials. In 1996 the scale was revised as an instrumental seismic intensity measure (IJMA) that could be used to rapidly assess the expected damage after an earthquake without having to conduct a survey. Since its revision, Japanese researchers have developed several ground motion … Show more
“…As shown later, the residuals show that there are no trends or biases that result from the use of this functional form. This database and functional form have also been successfully used to develop GMPEs for peak ground motion and elastic response spectral parameters (Campbell and Bozorgnia 2008), inelastic response spectral parameters (Bozorgnia et al 2010), JMA instrumental seismic intensity (Campbell and Bozorgnia 2011a), and a standardized version of CAV that incorporates the damage criteria proposed by EPRI (Campbell and Bozorgnia 2011b). The ground motion component used to define AI is the geometric mean of the two as-recorded horizontal components (AI GM ), which is the same ground motion component (CAV GM ) that we used to define CAV.…”
Arias intensity (AI) and cumulative absolute velocity (CAV) have been proposed as instrumental intensity measures that can incorporate the cumulative effects of ground motion duration and intensity on the response of structural and geotechnical systems. In this study, we have developed a ground motion prediction equation (GMPE) for the horizontal component of AI in order to compare its predictability to a similar GMPE for CAV. Both GMPEs were developed using the same strong motion database and functional form in order to eliminate any bias these factors might cause in the comparison. This comparison shows that AI exhibits significantly greater amplitude scaling and aleatory uncertainty than CAV. The smaller standard deviation and less sensitivity to amplitude suggests that CAV is more predictable than AI and should be considered as an alternative to AI in engineering and geotechnical applications where the latter intensity measure is traditionally used.
“…As shown later, the residuals show that there are no trends or biases that result from the use of this functional form. This database and functional form have also been successfully used to develop GMPEs for peak ground motion and elastic response spectral parameters (Campbell and Bozorgnia 2008), inelastic response spectral parameters (Bozorgnia et al 2010), JMA instrumental seismic intensity (Campbell and Bozorgnia 2011a), and a standardized version of CAV that incorporates the damage criteria proposed by EPRI (Campbell and Bozorgnia 2011b). The ground motion component used to define AI is the geometric mean of the two as-recorded horizontal components (AI GM ), which is the same ground motion component (CAV GM ) that we used to define CAV.…”
Arias intensity (AI) and cumulative absolute velocity (CAV) have been proposed as instrumental intensity measures that can incorporate the cumulative effects of ground motion duration and intensity on the response of structural and geotechnical systems. In this study, we have developed a ground motion prediction equation (GMPE) for the horizontal component of AI in order to compare its predictability to a similar GMPE for CAV. Both GMPEs were developed using the same strong motion database and functional form in order to eliminate any bias these factors might cause in the comparison. This comparison shows that AI exhibits significantly greater amplitude scaling and aleatory uncertainty than CAV. The smaller standard deviation and less sensitivity to amplitude suggests that CAV is more predictable than AI and should be considered as an alternative to AI in engineering and geotechnical applications where the latter intensity measure is traditionally used.
“…The level for triggering the recording is set up for the base floor and at 1.5 in JMA (Japan Metrological Agency) Intensity (equivalent to about 20 mm/s 2 (0.002g) acceleration). The definition of JMA Intensity is found in JMA (1996) and Campbell and Bozorgnia (2011). Once it is triggered, recording begins, the maximum interstory drift ratio and the maximum floor acceleration are computed by the PC, and one of the three diagnoses-''Safe,''''Caution,'' or ''Danger''-is announced as commonly done in many emergency risk judgments.…”
In Japan, structural health monitoring (SHM) of building structures began in the 1950s, but, until recently, its widespread use was not realized. A new trend arrived a few years ago, and currently over 850 buildings have SHM systems installed. The most recent SHM systems have been installed voluntarily by owners in the private sector; that is, the major development of recent Japanese SHM has been based on market forces. This article reports on why SHM was not accepted widely in the past, what were the keys for change of the atmosphere, how the building owners evaluate SHM after it is deployed, and what tangible benefits the building owners realize by experience on SHM implementation. To investigate those, an SHM system named q-NAVI is introduced as an example. The system has been deployed for 450 buildings, and they experienced a few significant shakings from recent earthquakes. SHM is also found effective for acquiring information on the quantification of fragility curves for various nonstructural components, using the data samples collected in recent earthquakes.
“…Because the seismic stations in the borehole are nearly the rock soil profile, we do not consider the site amplification factor for simplicity. JMA intensity is defined as logarithm (log 10 ) of the mean square root of the three-component accelerogram's amplitude by band-pass from 0.5 to 10 Hz and weight (1/ freq) 1/2 (Campbell and Bozorgnia 2011;Hoshiba and Aoki 2015). Because the energy density F is also proportional to the band-passed accelerogram's amplitude, the relationship between the JMA intensity and the energy density F in RTT can be expressed as F ¼ C10 I j ðx;tÞ (Hoshiba and Aoki 2015), in which C is constant independent of location x and time t, and I j x; t ð Þ represents the time trace of JMA intensity measured at x in real-time manner.…”
In earthquake early warning systems, real-time shake prediction through wave propagation simulation is a promising approach. Compared with traditional methods, it does not suffer from the inaccurate estimation of source parameters. For computation efficiency, wave direction is assumed to propagate on the 2-D surface of the earth in these methods. In fact, since the seismic wave propagates in the 3-D sphere of the earth, the 2-D space modeling of wave direction results in inaccurate wave estimation. In this paper, we propose a 3-D space numerical shake prediction method, which simulates the wave propagation in 3-D space using radiative transfer theory, and incorporate data assimilation technique to estimate the distribution of wave energy. 2011 Tohoku earthquake is studied as an example to show the validity of the proposed model. 2-D space model and 3-D space model are compared in this article, and the prediction results show that numerical shake prediction based on 3-D space model can estimate the real-time ground motion precisely, and overprediction is alleviated when using 3-D space model.
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