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Title: Assessment Of Seismic Hazard With Local Site Effects : Deterministic And Probabilistic Approaches
Authors: Vipin, K S
Advisors: Sitharam, T G
Keywords: Seismic Hazards - Probabilistic Methods
Earthquake Engineering
Seismic Hazards - South India
Seismic Hazard Assessment
Seismic Sources
Probabilistic Seismic Hazard Analysis (PSHA)
Ground Motion Prediction Equations (GMPE)
Deterministic Seismic Hazard Analysis (DSHA)
Submitted Date: Dec-2009
Series/Report no.: G23640
Abstract: Many researchers have pointed out that the accumulation of strain energy in the Penninsular Indian Shield region may lead to earthquakes of significant magnitude(Srinivasan and Sreenivas, 1977; Valdiya, 1998; Purnachandra Rao, 1999; Seeber et al., 1999; Ramalingeswara Rao, 2000; Gangrade and Arora, 2000). However very few studies have been carried out to quantify the seismic hazard of the entire Pennisular Indian region. In the present study the seismic hazard evaluation of South Indian region (8.0° N - 20° N; 72° E - 88° E) was done using the deterministic and probabilistic seismic hazard approaches. Effects of two of the important geotechnical aspects of seismic hazard, site response and liquefaction, have also been evaluated and the results are presented in this work. The peak ground acceleration (PGA) at ground surface level was evaluated by considering the local site effects. The liquefaction potential index (LPI) and factor of safety against liquefaction wee evaluated based on performance based liquefaction potential evaluation method. The first step in the seismic hazard analysis is to compile the earthquake catalogue. Since a comprehensive catalogue was not available for the region, it was complied by collecting data from different national (Guaribidanur Array, Indian Meterorological Department (IMD), National Geophysical Research Institute (NGRI) Hyderabad and Indira Gandhi Centre for Atomic Research (IGCAR) Kalpakkam etc.) and international agencies (Incorporated Research Institutions for Seismology (IRIS), International Seismological Centre (ISC), United States Geological Survey (USGS) etc.). The collected data was in different magnitude scales and hence they were converted to a single magnitude scale. The magnitude scale which is chosen in this study is the moment magnitude scale, since it the most widely used and the most advanced scientific magnitude scale. The declustering of earthquake catalogue was due to remove the related events and the completeness of the catalogue was analysed using the method suggested by Stepp (1972). Based on the complete part of the catalogue the seismicity parameters were evaluated for the study area. Another important step in the seismic hazard analysis is the identification of vulnerable seismic sources. The different types of seismic sources considered are (i) linear sources (ii) point sources (ii) areal sources. The linear seismic sources were identified based on the seismotectonic atlas published by geological survey of India (SEISAT, 2000). The required pages of SEISAT (2000) were scanned and georeferenced. The declustered earthquake data was superimposed on this and the sources which were associated with earthquake magnitude of 4 and above were selected for further analysis. The point sources were selected using a method similar to the one adopted by Costa et.al. (1993) and Panza et al. (1999) and the areal sources were identified based on the method proposed by Frankel et al. (1995). In order to map the attenuation properties of the region more precisely, three attenuation relations, viz. Toto et al. (1997), Atkinson and Boore (2006) and Raghu Kanth and Iyengar (2007) were used in this study. The two types of uncertainties encountered in seismic hazard analysis are aleatory and epistemic. The uncertainty of the data is the cause of aleatory variability and it accounts for the randomness associated with the results given by a particular model. The incomplete knowledge in the predictive models causes the epistemic uncertainty (modeling uncertainty). The aleatory variability of the attenuation relations are taken into account in the probabilistic seismic hazard analysis by considering the standard deviation of the model error. The epistemic uncertainty is considered by multiple models for the evaluation of seismic hazard and combining them using a logic tree. Two different methodologies were used in the evaluation of seismic hazard, based on deterministic and probabilistic analysis. For the evaluation of peak horizontal acceleration (PHA) and spectral acceleration (Sa) values, a new set of programs were developed in MATLAB and the entire analysis was done using these programs. In the deterministic seismic hazard analysis (DSHA) two types of seismic sources, viz. linear and point sources, were considered and three attenuation relations were used. The study area was divided into small grids of size 0.1° x 0.1° (about 12000 grid points) and the PHA and Sa values were evaluated for the mean and 84th percentile values at the centre of each of the grid points. A logic tree approach, using two types of sources and three attenuation relations, was adopted for the evaluation of PHA and Sa values. Logic tree permits the use of alternative models in the hazard evaluation and appropriate weightages can be assigned to each model. By evaluating the 84th percentile values, the uncertainty in spectral acceleration values can also be considered (Krinitzky, 2002). The spatial variations of PHA and Sa values for entire South India are presented in this work. The DSHA method will not consider the uncertainties involved in the earthquake recurrence process, hypocentral distance and the attenuation properties. Hence the seismic hazard analysis was done based on the probabilistic seismic hazard analysis (PSHA), and the evaluation of PHA and Sa values were done by considering the uncertainties involved in the earthquake occurrence process. The uncertainties in earthquake recurrence rate, hypocentral location and attenuation characteristic were considered in this study. For evaluating the seismicity parameters and the maximum expected earthquake magnitude (mmax) the study area was divided into different source zones. The division of study area was done based on the spatial variation of the seismicity parameters ‘a’ and ‘b’ and the mmax values were evaluated for each of these zones and these values were used in the analysis. Logic tree approach was adopted in the analysis and this permits the use of multiple models. Twelve different models (2 sources x 2 zones x 3 attenuation) were used in the analysis and based on the weightage for each of them; the final PHA and Sa values at bed rock level were evaluated. These values were evaluated for a grid size of 0.1° x 0.1° and the spatial variation of these values for return periods of 475 and 2500 years (10% and 2% probability of exceedance in 50 years) are presented in this work. Both the deterministic and probabilistic analyses highlighted that the seismic hazard is high at Koyna region. The PHA values obtained for Koyna, Bangalore and Ongole regions are higher than the values given by BIS-1893(2002). The values obtained for south western part of the study area, especially for parts of kerala are showing the PHA values less than what is provided in BIS-1893(2002). The 84th percentile values given DSHA can be taken as the upper bound PHA and Sa values for South India. The main geotechnical aspects of earthquake hazard are site response and seismic soil liquefaction. When the seismic waves travel from the bed rock through the overlying soil to the ground surface the PHA and Sa values will get changed. This amplification or de-amplification of the seismic waves depends on the type of the overlying soil. The assessment of site class can be done based on different site classification schemes. In the present work, the surface level peak ground acceleration (PGA) values were evaluated based on four different site classes suggested by NEHRP (BSSC, 2003) and the PGA values were developed for all the four site classes based on non-linear site amplification technique. Based on the geotechnical site investigation data, the site class can be determined and then the appropriate PGA and Sa values can be taken from the respective PGA maps. Response spectra were developed for the entire study area and the results obtained for three major cities are discussed here. Different methods are suggested by various codes to Smooth the response spectra. The smoothed design response spectra were developed for these cities based on the smoothing techniques given by NEHRP (BSSC, 2003), IS code (BIS-1893,2002) and Eurocode-8 (2003). A Comparison of the results obtained from these studies is also presented in this work. If the site class at any location in the study area is known, then the peak ground acceleration (PGA) values can be obtained from the respective map. This provides a simplified methodology for evaluating the PGA values for a vast area like South India. Since the surface level PGA values were evaluated for different site classes, the effects of surface topography and basin effects were not taken into account. The analysis of response spectra clearly indicates the variation of peak spectral acceleration values for different site classes and the variation of period of oscillation corresponding to maximum Sa values. The comparison of the smoothed design response spectra obtained using different codal provisions suggest the use of NEHRP(BSSC, 2003) provisions. The conventional liquefaction analysis method takes into account only one earthquake magnitude and ground acceleration values. In order to overcome this shortfall, a performance based probabilistic approach (Kramer and Mayfield, 2007) was adopted for the liquefaction potential evaluation in the present work. Based on this method, the factor of safety against liquefaction and the SPT values required to prevent liquefaction for return periods of 475 and 2500 years were evaluated for Bangalore city. This analysis was done based on the SPT data obtained from 450 boreholes across Bangalore. A new method to evaluate the liquefaction return period based on CPT values is proposed in this work. To validate the new method, an analysis was done for Bangalore by converting the SPT values to CPT values and then the results obtained were compared with the results obtained using SPT values. The factor of safety against liquefaction at different depths were integrated using liquefaction potential index (LPI) method for Bangalore. This was done by calculating the factor of safety values at different depths based on a performance based method and then the LPI values were evaluated. The entire liquefaction potential analysis and the evaluation of LPI values were done using a set of newly developed programs in MATLAB. Based on the above approaches it is possible to evaluate the SPT and CPT values required to prevent liquefaction for any given return period. An analysis was done to evaluate the SPT and CPT values required to prevent liquefaction for entire South India for return periods of 475 and 2500 years. The spatial variations of these values are presented in this work. The liquefaction potential analysis of Bangalore clearly indicates that majority of the area is safe against liquefaction. The liquefaction potential map developed for South India, based on both SPT and CPT values, will help hazard mitigation authorities to identify the liquefaction vulnerable area. This in turn will help in reducing the liquefaction hazard.
URI: http://etd.iisc.ernet.in/handle/2005/1973
Appears in Collections:Civil Engineering (civil)

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