Site specific design response spectrum proposed for the capital city of Agartala, Tripura

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1 Geomatics, Natural Hazards and Risk ISSN: (Print) (Online) Journal homepage: Site specific design response spectrum proposed for the capital city of Agartala, Tripura Arjun Sil & TG Sitharam To cite this article: Arjun Sil & TG Sitharam (2016) Site specific design response spectrum proposed for the capital city of Agartala, Tripura, Geomatics, Natural Hazards and Risk, 7:5, , DOI: / To link to this article: Informa UK Limited, trading as Taylor & Francis Group Published online: 17 Dec Submit your article to this journal Article views: 814 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at

2 GEOMATICS, NATURAL HAZARDS AND RISK, 2016 VOL. 7, NO. 5, Site specific design response spectrum proposed for the capital city of Agartala, Tripura Arjun Sil a and TG Sitharam b a Department of Civil Engineering, NIT Silchar, India; b Department of Civil Engineering, Indian Institute of Science, Bangalore, India ABSTRACT The design response spectrum is smooth in shape compared to other response spectra. The objective of design spectra is to estimate the possible earthquake lateral loads which may experience on a particular structure during its design life. In this study, an attempt has been made to develop, design response spectra for the Agartala city, Tripura which is one of the North Eastern states in India considered at the highest level of seismic activity in the country having a zone factor 0.36g as per Indian seismic code (BIS ). Based on the present data set collected from , the region is characterized and the seismicity parameters estimated separately for each source zone by Gutenberg and Richter (G-R) relationship. The two ground motion models were used for the hazard prediction. However, hazard curve, and uniform hazard response spectra (UHRS) (2% and 10% probability level for 50 years) has been developed for the Agartala at seismic bedrock level condition. Further, the NEHRP (National Earthquake Hazards Reduction Program) site classes D and E-types have been identified based on the average shear wave velocity at an upper 30 m depth of the subsurface soil profile. The direct shear wave velocity profiles were obtained through multi channel analysis of surface wave tests conducted at 27 locations at Agartala city. The design response spectra have been developed at the surface level for both the site classes. The results could be used as direct inputs for earthquake resistant design of civil engineering infrastructures of the study area. ARTICLE HISTORY Received 14 May 2015 Accepted 23 November 2015 KEYWORDS Risk; earthquake lights; earthquake predictions; seismic zones; seismic hazard; UHRS; NEHRP; PGA; spectral acceleration; ground motion; site class; Vs30 1. Introduction A design response spectrum is generally smooth in shape compared to the other response spectrum (uniform hazard spectrum (UHRS), and site specific) which is basically developed and widely used as an input for earthquake-resistant design of man-made structures. It helps to evaluate the possible earthquake lateral loads that can be subjected on a particular structure during its design period. The input can be selected depending on the functional use of the structures (based on importance) or performance-based manner and the owner choice. The design response spectrum provides a general procedure to estimate the expected dynamic load on a structure which is expressed as a function of natural period. Thus, knowing the period of the structure, design load could be calculated. The design response spectrum could be developed from seismic hazard assessment (using deterministic (DSHA) and probabilistic (PSHA)) of ground motions and also from the site-specific studies. As per NEHRP (National Earthquake Hazard Reduction Program) guidelines, design response CONTACT Arjun Sil arjun@civil.nits.ac.in 2015 Informa UK Limited, trading as Taylor & Francis Group

3 GEOMATICS, NATURAL HAZARDS AND RISK 1611 spectrum is developed from the PSHA framework. The 2% probability hazard level could be used for the development of design response spectra which is actually satisfying a maximum considered earthquake level condition. However, the hazard potentiality could be reduced through the designing of earthquake-resistant structures with the appropriate hazard value estimated considering all aspects such as geology, seismology, and geotechnical engineering background. For earthquakeresistant design practice, hazard map provides necessary inputs for the designer and the administration to mitigate the damages. However, considering the seismicity, the north-east (NE) region of India is one of the six most active seismic areas in the world. The BIS code delineates this region as seismic zone V, the most severe zone in the country. In this region, five historical earthquakes of magnitude M w > 8 and 15 events of magnitude M w >7 have occurred in the last 100 years. However, most of the area is hilly, steep-terrain in nature and altitude varies from 0 to 3.0 km from mean sea level. Due to the complex geological and topographical distribution of terrains, the area is also more prone to landslides. In the recent past, researchers have found a seismic gap known as Assam gap (Khattri & Wyss 1978) since 1950 in between the Eastern Himalaya (EH), Shillong Plateau (SP), and Indo- Burma Ranges (IBR) with the Eurasian plate for a long time in this belt comparison to the past history. They have estimated the return period and are expecting a big earthquake in this region at any time in future (Kayal 1998; Khattri & Wyss 1978); Yadav et al. 2010; Sil et al. 2015). In 2011, the state experienced 37 shocks having magnitudes ranging from 2.5 to 6.9. Out of these, five big earthquakes of magnitude M 6.4 (4 February 2011), M6.7 (24 March 2011), M6.9 (18 September 2011), M6.4 (30 October 2011), and M6.9 (13 Dec 2011) occurred within this region (source: Indian Meteorological Department (IMD), India). The earthquake (M w 6.9) that occurred on 18 September 2011, known as Sikkim earthquake, done huge destructions such as building collapse, landslides, and casualties, disrupted the transportation networks by road damages, and disconnected for more than half of the month from the rest of the country and caused various infrastructural damages in the Sikkim state and affected the entire NE India. Although many researchers have studied and reported about the status of seismicity in the north-eastern Region of India; however, very few studies in detail have been carried out throughout this region except the city of Guwahati, the states of Sikkim, and Manipur. Considering the geographical location and complex seismotectonic environment of the region, there is a need of a detail site-specific study for Tripura (Sil et al. 2013), whereas almost the whole of the area is highly vulnerable to severe shaking, amplification, liquefaction, and landslide. The widely used methods for seismic hazard assessment are DSHA and PSHA. In DSHA, hazard is evaluated considering the close distance between the source and site of interest and the past maximum magnitude occurred within the fault. In hazard estimation procedures, epistemic and aleatory uncertainties exist which could be explained using PSHA. In this study, an attempt has been made to develop surface-level design response spectrum for the Agartala city through a reliable and consistent PSHA including seismic site characterization using in situ geophysical survey of the study area. A systematic PSHA procedure suggested by Cornell (1968) and McGuire (1976, 1995) was adopted. The PSHA procedure assumes that the distance and magnitude probability/uncertainty are distributed uniformly throughout the fault rupture. The procedure of PSHA that includes a collection of events and faults, homogenization, and declustering of the data, data completeness, and evaluation of seismicity parameters a and b using Gutenberg and Richter (G-R) relationship, and selection of ground motion models. The ground motions at bedrock level were assessed using attenuation models of Atkinson and Boore (2003) which has developed using global database, and also Gupta (2010) for subduction inslab earthquakes specifically developed for NE India. Peak ground accelerations (PGAs) were assessed for the entire state of Tripura at bedrock level considering a grid size of 5 km x 5 km. The spatial variations of PGA and spectral accelerations were presented for the return periods of 475 years and 2475 years at 2% and 10% probability of exceedance (PE). Thereafter, the UHRS is developed for the Agartala city at the rock level for the same return periods at 5% damping value.

4 1612 A. SIL AND T. G. SITHARAM Finally, the design response spectrum has been proposed for the city of Agartala at the surface level by NEHRP (BSSC 2001) andibc(2006) procedures. 2. Study area and tectonic characteristics The study area covers the latitude N 29 N and longitude E E having 500 km distance from the political boundary of the state (figure 1). It covers all the north-eastern states of India, Bangladesh and West part of Burma in East, South part of China in north, also some part of the Indian Ocean in South, and part of West Bengal in West direction. However, in a seismically active region, delineation of potential seismic source zones is often a key issue in seismic hazard assessment. It has been recognized that the combination of seismicity and tectonic data could provide a better understanding about the complex mechanism of the seismic activity (Karnik 1969). However, delineation of seismic source zones is not a straightforward task, even, since the beginning of understanding of seismicity and tectonic mechanism of the earth, no method has been found adequate to properly demarcate the seismic source boundary. In various seismically active regions, there is not much information available to delineate potential seismic sources with a sufficient degree of confidence. The scientific study in this field has been progressing since 1960 onwards. However, a considerable effort has been made by the several researchers to understand the seismicity and tectonics of various regions; indeed, knowledge about the earthquake-generating process is still inadequate for detailed seismic source delineation. There are examples which support the inadequacy of our knowledge to understand the seismic source mechanism such as Rudbar earthquake (M w 7.2, 1990) in Iran, which occurred in an unknown fault could not been previously identified. Similarly, the Tabas earthquake fault in Iran was not known as an active fault prior to the event (M s 7.4, 1978) and generated higher ground motion in the region (Berberian et al. 1992). The aim of potential seismic source delineation is to identify all faults/lineaments that are tectonically active with an average rate of seismicity (EERI Committee on seismic Risk 1989). Proper identification of potential seismic sources requires the consideration of several factors; however, tectonic maps and epicentre locations are generally used as a prime guidance. There is no standard approach for delineating seismic Figure 1. Seismotectonic map of the study area (scale 1:1million).

5 GEOMATICS, NATURAL HAZARDS AND RISK 1613 sources; therefore, to model the geometry of sources, that mostly depends on personal judgment and expert advice to take the final decision (Yucemen & Gulkan 1994). However, characterization of seismic source zones is an important step while carrying out seismic hazard assessments. Due to the technological advancements, there are several possible approaches (geological, geophysical, and remote sensing) to identify and characterize seismic sources based on their fault activity, slip rate characteristics, focal mechanism, rupture mechanism, epicentral depth/ location, and the seismicity rates. Geological characterization includes terrain features or topographical phenomenon such as abrupt changes in elevation/altitude and river alignment. Geophysical characterization involves a variation of strength or stiffness of rock profiles or velocity profiles; however, generally low stiffness is observed in all the faults and lineaments. Considering, based on the epicentral depth with locations, the seismicity characteristics could provide better information regarding the source mechanism that helps the characterization of buried seismic sources. The spatial variation of epicentral depth in and around NE India is presented in figure 2. However, knowing the space limit from the background of seismicity and the epicentral depth, one can have an idea about the seismic source pattern. Kayal (1998) divided the region (NE India) into five zones based on the seismicity and the epicentral depth. Gupta (2006) has also divided the region into a number of smaller seismic sources (19 zones) based on the tectonic features, source mechanism, and the seismicity characteristics. Figure 2. Spatial variation of epicentral depth in and around north-east India with the delineation of seismic source zones.

6 1614 A. SIL AND T. G. SITHARAM Figure 3. Spatial variation of magnitude (M w ) size in and around north-east India showing with the delineation of seismic source zones. In this study, we have delineated the spatial distribution of the catalogue by locations (latitude and longitude) after classifying each size of magnitude ranges. Thereafter, tectonic map (SEISAT 2000) prepared by the Survey of India (SOI) is superimposed to search the connectivity between the seismicity (considering the variation of magnitude size) with the faults alignment/orientations (figure 3) and the fault behaviour. The study area has been divided into six major seismic source zones (Sitharam & Sil 2014) such as IBR, SP, EH, Bengal Basin (BB), Mishmi Thrust (MT), and Naga Thrust (NT) considering seismicity, fault rupture mechanism, epicentral depth, and the variation of magnitude sizes. Tripura is mainly situated in the BB zone. In the South West, and South East side of Tripura covers whole Bangladesh, which extends between the longitude E to E and latitude N to N. The NE part is covered by Assam-Mizoram states. Among the six major tectonic zones, two major zones are very close to the Tripura boundaries that are IBR and SP. IBR is one of the major subduction zones, that is mainly due to subduction activities between the Indian and the Burmese plates. The IBR is approximately 340 km distance from the Tripura boundary (east side) and the depth of epicentre ranges normally around km (central part); however, for small or moderate size events, the depth varies from 2 32 km. The distance of SP is approximately 200 km range on the north side of the study area. Intra-plate seismic activity was observed within the boundary such as Sreemangal earthquake (1918) along the Sylhet fault and Cachar earthquake (1984) occurred in the Tripura fold belt.

7 GEOMATICS, NATURAL HAZARDS AND RISK Earthquake catalogue and processing A reliable seismic hazard study of a region strongly depends on the data statistics of the event. Data statistics include consistent recording with accurate measurement for a long duration. The event data were collected from various national and international seismological agencies such as IMD, Geological Survey of India (GSI), United State Geological Survey (USGS), and the International Seismological Centre (ISC). The seismic sources were identified from the seismotectonic atlas (SEISAT 2000), published by the GSI, literature, and from remote sensing images. The SESAT-2000 contains 43 maps presented in 42 sheets covering the entire India and adjacent countries with 1:1 million scale. Sheets representing the features of the study area were scanned, digitized, and geo-referenced using MapInfo 10.0 version. Earthquake data collected from various agencies were homogenized based on the local correlations (Sitharam & Sil 2014) of moment magnitude with other magnitude scales. The declustering of the catalogue was performed using declustering algorithm to remove the fore shocks and aftershocks in time and space window of the catalogue. A total 3251 declustered seismic events (main shocks) in the study area since were identified and used for the study. The data-set contains 825 events less than 4 magnitude, 1279 events from 4 to 4.9, 996 events from 5 to 5.9, 131 events from 6 to 6.9, 15 events from 7 to 7.9, and 5 events of above magnitude 8. Thereafter, the tectonic features and seismic events (main shocks) were superimposed on the map of the study area to prepare a seismotectonic map of this region (figure 1). 4. Estimation of seismicity parameter The estimation of seismicity parameters requires consistent and reliable data (events), data completeness, and proper methods. The seismicity parameters were estimated after the completeness analysis of the catalogue. In 1972, Stepp proposed a method based on the length of the period over which a certain particular magnitude is complete. In this method, an effort has been made to group each magnitude class as 10-year interval. Different magnitude classes are expressed as an occurrence rate, which is actually a function of time. The magnitude of completeness is the lowest magnitude above which the earthquake recording is assumed to be complete. As a first step for the evaluation of the completeness period, the number of earthquakes reported during each decade for the given magnitude ranges were evaluated. The plot shows the variation of s λ with time. The earthquake data is considered as complete as long as its variation is along the p 1 ffiffi line. The plotted points are T assumed to have a straight line following a slope as long as the data becomes complete. After the analysis of data, it has been found that the magnitude range 4 5 is complete for 50 years, 5 6 is complete for 90 years, 6 7 is complete for 100 years, 7 8 is complete for 130 years, and greater than 8 is complete for 200 years shown in figure 4. Further, the completeness of the catalogue was also estimated based on the visual cumulative method (CUVI). In this method, the cumulative number of events per year is plotted against the period of occurrence in years for each size of magnitude range (figure 5). The completeness periods based on both the methods (Stepp1972 and CUVI) are presented in tabular form (table 1). Considering the complete part of the data-set for all the magnitude ranges, the Gutenberg Richter (G-R) parameters a and b were estimated through regression analysis which follows an exponential distribution of magnitude and the relation is expressed as: Log N D a bm w (1) where N is the cumulative number of events per year, and a represents seismic activity, b represents the relative proportion of smaller to larger size events. The estimated seismicity parameters for each source zones are presented in table 2.

8 1616 A. SIL AND T. G. SITHARAM Figure 4. Completeness analysis of catalogue based on Stepp (1972) method. 5. Ground motion model The ground motion model is developed basically for assessment of seismic hazard in terms of PGA or spectral acceleration (Sa), expressed as a function of period. The variables that influence the ground motion parameters are mainly earthquake magnitude (M w ) and distance (R in km) from source to site of interest. The ground motion that transmits from seismic bedrock to the surface depends on the attenuation path, local geology, and soil properties of the subsurface materials (site effects). The prediction model is generally developed from the available strong motion records or synthetic motions generated for the known regional seismological parameters. The ground motion is assumed to follow a lognormal distribution and the equation provides the median ground motion with its variance. In this study, two ground motion models (Atkinson & Boore 2003; Gupta 2010) were used. Gupta (2010) developed his ground motion model specifically for subduction zone earthquakes in NE India. This equation was proposed after modifying Atkinson and Boore (AB-2003) model, using 56 horizontal accelerogram records collected from three earthquakes of NE India. However, the AB-2003 model was developed using the global database for subduction zone earthquakes. According to the geographical location of Tripura, the IBR subduction zone is having a very close distance, comparing among other tectonic domains (plate boundary) within this region. Indeed, only very few attenuation relationships are available for this region. Therefore, these two equations have been selected to predict the hazard of this region. The following prediction equation is used up to the bedrock level condition: Log Y D C 1 C C 2 M w C C 3 h C C 4 R g log R (2) where Y represents mean PGA at the bedrock level in cm/sec 2, M w the moment magnitude, h is the focal depth in km, and R is hypocentral distance in km, and g is the geometric attenuation parameter. However, the parameters C 1, C 2, C 3 and C 4 are the attenuation coefficients up to the bedrock level condition. In this study, the entire area was divided into a number of grids, considering grid size 5 x 5 km (for finer resolution) and the hazard was calculated at the centre of each grid cell and finally, hazard level has been mapped for the entire study area in different PE level.

9 GEOMATICS, NATURAL HAZARDS AND RISK 1617 Figure 5. Completeness analysis of catalogue based on CUVI method. Table 1. Completeness period of catalogue based on Stepp (1972) and CUVI methods. CUVI method Stepp method (1972) Magnitude interval completeness period (years) completeness period (years) 4.0 < M w < D D 50 5 < M w < D D 90 6 < M w < D D < M w < D D 130 M w > D D 200

10 1618 A. SIL AND T. G. SITHARAM Table 2. Seismicity parameters calculated for each zone. Sl. No Seismic regions Parameter-(a) value Parameter-(b) value 1 Indo-Burma Region (IBR) Eastern Himalaya (EH) Shillong Plateau (SP) Bengal Basin (BB) Naga Thrust (NT) Mishmi Thrust (MT) Estimation of maximum magnitude In seismic hazard analysis, the knowledge of estimating the maximum magnitude is important and used as one of the most key input parameters in the seismic design. It indicates the highest potential of accumulated strain energy to be released in the region or a seismic source/fault. Alternatively, the M max is an upper limit or a largest possible earthquake that may produce highest seismic hazard scenarios of the region. However, in the study region, very limited amount of data is available for the last few decades (based on the instrumental recorded data, since 1964) which do not sufficiently reveal of full seismic potential characteristics of any seismic source/fault with confidence. Further, there is no well-known or well-defined methodology available for evaluation of maximum magnitude. Some of the methods have been proposed by various researchers such as Kijko and Sellevoll (1989), Gupta (2002), Mueller (2010) and Wells and Coppersmith (1994). In the present work, M max is estimated considering three approaches. These are Kijko and Sellevoll (1989) method, by adding incremental values (Gupta 2002) and using fault rupture relationship (Wells & Coppersmith 1994). Method-A: To determine the maximum magnitude of a fault or source, Wells and Coppersmith (1994) proposed some empirical equations based on the subsurface fault rupture characteristics such as length, area, and slip rate of the fault with the moment magnitude. These empirical equations were developed by standard statistical regression using a global database of the events. These relations are given based on tectonic regime characteristics such as strike-slip, reverse, and normal faulting and also the average relation for all slip types are developed to be appropriate for most applications in general (if the fault type is unknown). In this work, the length of faults was estimated from the seismotectonic atlas (SEISAT 2000) of India published by GSI and some of the faults were extracted from the literature. All these faults/lineaments were digitized using Mapinfo software version 10 and evaluated the length of the respective faults. The relation proposed by Wells and Coppersmith (1994) to estimate expected moment magnitude of a linear fault is given below: LogðSRLÞ D 0:57M w 2:33 (3) The relation between M w and surface rupture length (SRL) was developed using reliable source parameters and this is applicable for all types of faults, shallow earthquakes, and interplate or intraplate earthquakes (Wells & Coppersmith 1994). Using this equation along with a parametric study, it is being observed that the subsurface fault rupture length of about 3.8% of the total fault length provides moment magnitude closely matching with the past earthquakes. The estimation procedure is presented in tabular form (method-a) in table 3. Method-B (By adding incremental value): This method has been proposed by Gupta (2002) after adding an incremental unit. In this method to estimate M max, an increment of 0.5 is added to the observed maximum magnitude. This approach is simple and provides unarguable lower limit for M max (Wheeler 2009). This incremental technique has been used by various researchers to estimate the seismic hazard in India (Jaiswal & Sinha 2007a, 2007b, 2008; Menon et al. 2010; NDMA 2010; Roshan & Basu 2010).

11 GEOMATICS, NATURAL HAZARDS AND RISK 1619 Table 3. Estimation of maximum magnitude (M max ) for faults/lineaments of the study area. Fault coordinates Sl No Lat Long Lat Long M w observed in the fault Length of fault (TFL) (km) Method-A SRL 3.8% of TFL (km) M max Method-B M max,by incremental value (Gupta 2002) Method- C by Kijko and Sellevoll (1989) M max considered for the present study (continued)

12 1620 A. SIL AND T. G. SITHARAM Table 3. (Continued ) Fault coordinates Sl No Lat Long Lat Long M w observed in the fault Length of fault (TFL) (km) Method-A SRL 3.8% of TFL (km) M max Method-B M max,by incremental value (Gupta 2002) Method- C M max considered by Kijko and for the Sellevoll (1989) present study

13 GEOMATICS, NATURAL HAZARDS AND RISK 1621 Method-C: Method to estimate M max proposed by Kijko and Sellevoll (1989) is applicable only when the b value of seismicity parameter of the region is known. The method has been established using the doubly truncated G-R relation as given below. M max D Mmax obs C E 1ðn 2 Þ E 1 ðn 1 Þ C M min expð nþ (4) bexpð n 2 Þ where, M max is the upper bound maximum magnitude, Mmax obs is the observed maximum magnitude in each fault, and 'n' is the total number of earthquakes of magnitude (M w > 4). The M min denotes the minimum magnitude. In the present study, M min is taken as 4, because below this magnitude, there is a less interest in engineering design. However, n 1 D n f1 exp½ bðm max m min ÞŠg (4a), n 2 D n 1 fexp½ bðm max m min ÞŠg (4b), and E 1 ðn i Þ expresses the exponential integration function that could be evaluated as E 1 ðnþ D n 2 C a 1 n C a 2 nðn 2 C b 1 n C b 2 Þ expð nþ (4c), where a 1, a 2, b 1 and b 2 are the constant coefficients (Abramowitz & Stegun 1970). The above method proposed by Kijko and Sellevoll (1989) has been used by various researchers worldwide for seismic hazard studies including India. We have characterized the study area (NE India) into six major seismic source zones and estimated seismicity parameters for each zone separately by considering the seismic events occurred within the respective zones. These six b value parameters were used for estimation of M max using the above equation for each fault lies within the respective zone. The maximum magnitude estimated for each fault obtained from the above methods was presented in table Seismic hazard assessment at rock level Seismic hazard assessments require four components as input parameters. These are (1) seismicity parameters (a, b) andm max (2) sources or faults/lineaments and (3) selection of ground motion model, and the fourth (4) being an input historical earthquake catalogue. To assess the hazard, the entire study area was divided into a number of grid cells (0.045 x ) and the centre of each grid cell is considered as a hazard point or location from all four sides of the grid calculated by considering all the seismic sources within a radius of 500 km. Now, considering every individual point as a site/location in space, the shortest distance is calculated for each source and finally, maximum hazard predicted value is selected from the entire sources calculated for the study area for this particular location. However, applying interpolation technique, the delineation is done in the entire study area. The advantage of using this gridding technique is to have a simple linear mathematical relation to find out the required unknowns between the two known points in the space/plane. The occurrence of an earthquake within a seismic source is assumed to follow a Poisson distribution. In PSHA, the probability of ground motion parameter, Z, at a given site, will exceed a specified level, z, during a specified time; T is represented by the expression: PðZ > zþ D 1 e nðzþt nðzþt (5) where nðzþ is the mean annual rate of exceedance of ground motion parameter, Z, with respect to z. The function nðzþ incorporates the uncertainty in time, size, and location of future earthquakes and uncertainty in the ground motion they produce at the site. It is given by: nðzþ D XN n D 1 N n ðm 0 Þ Z m max m D m 0 f n ðmþ " Z 1 r D 0 f n ðr j mþpðz > z j m; rþdr dm (6) where N n (m 0 ) is the frequency of earthquakes on a seismic source n, having a magnitude equal to or greater than a minimum magnitude m 0 (in this study it is taken as 4.0); f n (m) is the probability #

14 1622 A. SIL AND T. G. SITHARAM density function for a minimum magnitude of m 0 and a maximum magnitude of m max ;f n (rjm) is the conditional probability density function (probability of occurrence of an earthquake of magnitude m at a distance r from the site for a seismic source n); P(Z > zjm, r) is the probability at which the ground motion parameter Z exceeds a predefined value of z, when an earthquake of magnitude m occurring at a distance of r from the site. The integral in equation (6) can be replaced by summation and the density functions f n (m) and f n (rjm) could be replaced by discrete mass functions. The resulting expression for nðzþ is given by: nðzþ D XN m i XD m max n D 1 m i D m 0 λ n ðm i Þ " r jx D r max r j D r min P n ðr D r j j m i ÞPðZ > z j m i ; r j Þ # (7) where λ n ðm i Þ is the frequency of occurrence of magnitude m i at the source n obtained by discretizing the earthquake recurrence relationship for the source n. The primary output from PSHA analysis is a hazard curve, which explains the mean annual rate of exceedance for a certain target level PGA value. In this procedure, magnitude and distance probabilities that account the variability or uncertainties are considered as a uniform distribution pattern. The hazard curve is space and period dependant; hence, it is the responsibility of the designer and the choice of the owner to define the location and type of structure that are to be built. The hazard curve will provide the necessary design inputs based on the requirements of the owner and the designer for a certain particular risk level. For a standard PSHA analysis, the results are presented for 2% and 10% PE in 50 years considering the design life of the structure. The hazard value of 2% PE is used for design of critical structures such as nuclear power plant, important buildings, towers, and water tanks, whereas 10% PE is generally used for normal or ordinary structural design such as residential buildings. In our analysis, six hazard curves have developed (IBR, EH, NT, MT, BB, and SP) separately after considering all associated faults and lineaments coming under each source zone boundary (see figure 6). The hazard is calculated for the respective source zones after their deaggregation of seismicity parameters. The final hazard curve is developed after integration of all six hazard curves. To account the model-related uncertainties, a logic tree approach has been adopted in this work after assessing the hazard using both the equations (Atkinson & Boore 2003; Gupta 2010) with 60% and 40% weighting factors in order to get a rational value of hazard from these different uncertainties. A systematic flowchart of logic tree approach employed is presented in figure 7.The PGA at Figure 6. Hazard curve at rock level for Agartala city.

15 GEOMATICS, NATURAL HAZARDS AND RISK 1623 Figure 7. Logic tree structure employed with different models and their respective weightages. the rock level was estimated for the entire area. The contour maps showing the spatial variation of PGA (2% and 10%) are evaluated (see figure 8). The UHRS for the same return periods is also developed (figure 9) for the Agartala city on the seismic bedrock level condition. 8. Site class Site classes are introduced to obtain the average dynamic behaviour of the subsurface soil deposits and mapping its effects on the ground surface. The various site classes are suggested by NEHRP and IBC-2006 guidelines. As per the guidelines, the average dynamic behaviour of a site is expressed by various forms such as based on average shear wave velocity (Vs30) at upper 30 m depth, based on average standard penetration test-n (SPT-N) values, and the shear strength. However, the most popular and efficient way of assessing is the shear wave velocity measurements in the field. The shear wave velocity is directly related to the shear modulus, damping ratio, and the density of soil. Therefore, for site response analysis and microzonation purpose, direct measurement of shear wave velocity (Vs) is recommended. There are various methods such as spectral analysis of surface wave (SASW), multichannel analysis of surface wave (MASW), cross hole, up and down-hole tests available to obtain the Vs profiles in the field. The SASW method used two receiver geophones (Nazarian et al. 1983, 1992; Stokoe et al. 1994; Ganji et al. 1997), whereas MASW used 24 geophones. Both MASW and SASW methods are non-destructive and low strain method, however MASW is an advanced technique (Park & Elrick 1998; Park et al. 1999, 2002; Xia et al. 1999; Zhang et al. 2004; Xu et al. 2006) to obtain Vs profiles more accurately and less time-consuming method. Most of the time to cover larger area is practically difficult to measure Vs in the field. To overcome this problem/limitation, a simple in situ testing, SPT is suggested. The SPT has many advantages (Ohta & Goto 1978) such as it provides subsurface profile information (including water table, density, N- value, particle size, PI, and types of soil) with depth (normally 0 30 m). However, the SPT-N-value is the direct indication of the stiffness properties of soil. The reports (SPT bore logs) are widely available for any city, as it widely used for calculation of bearing capacity for foundation design. Hence, the correlations exist between SPT-N and Vs30 in the literature to obtain the Vs profiles with depth (Imai & Tonouchi 1982; Seed et al. 1983; Sisman 1995).

16 1624 A. SIL AND T. G. SITHARAM Figure 8. (a). Spatial variation of PGA(g) at seismic bedrock level with (A) 2% and (B) 10% probability of exceedance in 50 years. (b). Spatial variation of spectral accelerations (g) at seismic bedrock level with 2% probability of exceedance in 50 years.

17 GEOMATICS, NATURAL HAZARDS AND RISK 1625 Figure 8. (Continued). In this study, Vs profiles were obtained through MASW tests at 27 locations in the Agartala city area and Vs30 estimated for these 27 locations. A typical shear wave velocity calculation is shown in table 4. Most of the locations fall under D-type (180 < Vs30 < 360 m/s) and some locations fall under E-type (Vs30<180 m/s) site categories which is shown in figure Development of surface level design spectra As per NEHRP procedure, design response spectrum could be developed from the UHRS for a 2% PE in 50 years. There are site coefficients for short-periods (Fa) and long-periods (Fv) of mapped spectral accelerations for different site classes. In NE India, all outcropping motions are classified as B-type categories (Mitra et al. 2005). Therefore, no corrections are required, as the B-type site is the reference class adopted in NEHRP manual. In our study, the short-period mapped spectral acceleration (2% PE) found to be 0.39g and for mid-period mapped spectral acceleration 0.27g found for the Agartala city (see figure 9). Considering site classes D and E types, and the known intensity levels at rock site condition, the corresponding amplification factors (AFs) for short period are estimated that varies

18 1626 A. SIL AND T. G. SITHARAM Figure 9. UHRS at rock level of return periods 2475 and 475 years of Agartala city (Sil et al. 2013). from 1.2 to 1.1 whereas for longer period, this varies from 1.8 to 1.6 and the design response spectra has been developed following the procedure suggested by NEHRP as shown in figure 11. Theequation developed for estimation of spectral acceleration for the D-type site is as follows: 8 0:145 C 1:21T n 0T n 0:18 Sa >< g D 0:364 0:18T n 0:89 0:324 >: T n 0:89 T n ðfor D type siteþ (8) where T n is the period of structure, Sa g is the ratio of spectral acceleration with the acceleration due to gravity; similarly, the equation developed for E-type site categories in Agartala city is also expressed as: 8 0:18 C 1:15T n 0T n 0:23 Sa >< g D 0:44 0:23T n 1:15 0:50 >: T n 1:15 T n ðfor E type siteþ (9) Table 4. Typical average shear wave velocity estimation. Depth (metre) Vs (m/sec) Soil thickness (metre) Average Vs30 (m/s)

19 GEOMATICS, NATURAL HAZARDS AND RISK 1627 Figure 10. Spatial variation of average shear wave velocity (Vs30) at Agartala city (Sil & Sitharam 2014). Figure 11. Proposed site-specific design spectra at surface for Agartala city derived as per the NEHRP provisions.

20 1628 A. SIL AND T. G. SITHARAM The zero period acceleration (ZPA) for D and E-type site categories are found to be 0.145g and 0.18g. The zone factor for both the site categories are also found 0.29g and 0.36g, which comes under seismic zone V as per Indian seismic code IS Results and discussion As per NEHRP provisions, the design response spectrum generation requires site class and mapped spectral acceleration for short and long periods. In this study, site classes were obtained based on the average shear wave velocity (Vs30) calculated which has been obtained by direct measurement of shear wave velocity in the field. The shear wave velocity was obtained through MASW tests conducted at 27 locations in Agartala city. The mapped spectral accelerations were obtained through PSHA method considering the seismic bedrock level condition. The primary output from PSHA is a hazard curve showing the PGA against the mean annual rate of exceedance. The hazard curves for all six major sources were prepared and compiled to get the total hazard curve, which represents the cumulative hazard of all sources. The figure 6 represents the hazard curves for the Agartala city at bedrock level. This highlights that the IBR is the most active seismic zone and the secondand third are 'SP and 'BB to produce more earthquakes compare to other sources. The contour maps showing the spatial variation of PGA (2 % and 10%) and spectral acceleration (0.2, 0.4, 1 and 2 s) values were evaluated (figure 8(a) and 8(b)). However, the estimated spectral accelerations of the study area, it has been observed that in shorter or mid-periods (0.2 and 0.4 s), spectral acceleration shows higher hazard in the central middle part of the state running from west to east direction whereas comparatively low hazard has been found in both north and south part of the state. In case of period 0.2 s, the minimum spectral values vary from 0.1g to 1.35g, whereas for the period 0.4 s, the spectral values vary from 0.05g to 1.30g. This indicates, the structures located in this area would be highly sensitive to velocity rather than acceleration and displacement responses. On the other hand, the longer periods (1 and 2 s) spectral acceleration shows higher values in the northern side, whereas comparatively lower hazard has been observed in the south part of the state; in both the cases, the spectral acceleration varies from 0.1g to 0.9g (T D 1 s) whereas for periods having T D 2 s varies from 0.04g to 0.19g indicating displacement-sensitive areas in the northern part of the state. The UHRS for the 2475 and 475 years return periods were developed for the Agartala city (figure 9). The site classes were estimated based on the average shear wave velocity (Vs30) at upper 30 meters depth of subsurface soil layers. The estimated Vs30 for Agartala city ranges from 150 to 360 m/s, which falls under D- and E-type categories. In this study, the design response spectra (for site class D and E types) at the surface is developed as per NEHRP procedure and presented. The ZPA and zone factor for the respective site classes were estimated that ranges from 0.145g to 0.18g and 0.29gto 0.36g. Finally, the equations of spectral acceleration developed for the respective site classes were presented. 11. Conclusions This paper proposed the design response spectra for the capital city of Agartala, Tripura. The design response spectrum was developed based on NEHRP provisions. However, the site classes were obtained by direct measurements of shear wave velocity through field investigation. Based on the average shear wave velocity estimated, the Agartala city falls under site class D- and E-type categories as per NEHRP provisions. The design response spectra (surface) proposed for each site class and their respective equations for the estimation of spectral acceleration were expressed as a function of period. In order to estimate seismic hazard at rock level, the highest hazard has been observed in the NE side of the considered area, because this part is very close to the Sylhet fault (Bangladesh) that generated two magnitudes of earthquakes (M w > 7) in the past (Sil et al. 2013); however, this area is also nearer to the SP zone in the north side which is highly seismically active (intra plate) for the generation of higher ground motion in the region. Further, for seismic hazard evaluation of this region, all three types of sources (point, line, and areal) could be considered.