Site-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum

The importance of the effect of sediments above bedrock in modifying the strong ground motion has been long recognized (Boore, 2004; Boore & Joyner, 1997; Idriss & Seed, 1970; Seed & Idriss, 1969; Lam et al., 2001; Govindarajulu et al., 2004; Tezcan et al., 2002; Bakir et al., 2005; Kamatchi et al., 2007) in literature. The nature of soil that changes the amplitude and frequency content has a major influence on damaging effects of earthquake. To account for these effects, most of the seismic codes, for example the Indian code (IS 1893 (Part 1) 2002) has defined response spectra for three types of soil viz., hard soil, medium soil and soft soil. As an improvement over this approach, amplification factors based on empirical and theoretical data (Borcherdt, 1994) have been introduced in International Building Codes (IBC, 2009; ASCE 7 2005) for site classes A to E for the short period range and long period range based on the average shear wave velocity of top 30 m soil stratum. For site class F (soft soil) it has been recommended that site-specific analysis need to be carried out. However, Sun et al. (2005) showed that the site coefficients specified in IBC 2000 (IBC 2000) are not valid for Korean Peninsula due to the large difference in the depth of bedrock and the soil stiffness profile. Further, building codes are highly simplified tools and do not adequately represent any single earthquake event from a probable source for the site under consideration. Recently, it is being suggested (Heuze et al., 2004 ) that in addition to use of seismic code provisions, site-specific analysis which includes generation of strong ground motion at bedrock level and propagating it through soil layers (Heuze et al., 2004; Mammo 2005; Balendra et al., 2002) and arriving at the design ground motions and response spectra at surface should also be carried out. In this chapter the procedure to carry out site-specific seismic analysis of framed buildings is illustrated with examples for Delhi city. Rock outcrop motions are generated for the scenario earthquakes of magnitude, Mw = 7.5, Mw = 8.0 and Mw = 8.5. Three actual soil sites of Delhi have been modeled and the free field surface motions and the response spectra are obtained. It has been observed that the PGA amplifications and the response spectra of the three sites are quite different for the earthquakes considered. The same has reflected in considerable


Introduction
The importance of the effect of sediments above bedrock in modifying the strong ground motion has been long recognized (Boore, 2004;Boore & Joyner, 1997;Idriss & Seed, 1970;Seed & Idriss, 1969;Lam et al., 2001;Govindarajulu et al., 2004;Tezcan et al., 2002;Bakir et al., 2005;Kamatchi et al., 2007) in literature.The nature of soil that changes the amplitude and frequency content has a major influence on damaging effects of earthquake.To account for these effects, most of the seismic codes, for example the Indian code (IS 1893 (Part 1) 2002) has defined response spectra for three types of soil viz., hard soil, medium soil and soft soil.As an improvement over this approach, amplification factors based on empirical and theoretical data (Borcherdt, 1994) have been introduced in International Building Codes (IBC, 2009;ASCE 7 2005) for site classes A to E for the short period range and long period range based on the average shear wave velocity of top 30 m soil stratum.For site class F (soft soil) it has been recommended that site-specific analysis need to be carried out.However, Sun et al. (2005) showed that the site coefficients specified in IBC 2000 (IBC 2000) are not valid for Korean Peninsula due to the large difference in the depth of bedrock and the soil stiffness profile.Further, building codes are highly simplified tools and do not adequately represent any single earthquake event from a probable source for the site under consideration.Recently, it is being suggested (Heuze et al., 2004 ) that in addition to use of seismic code provisions, site-specific analysis which includes generation of strong ground motion at bedrock level and propagating it through soil layers (Heuze et al., 2004;Mammo 2005;Balendra et al., 2002) and arriving at the design ground motions and response spectra at surface should also be carried out.In this chapter the procedure to carry out site-specific seismic analysis of framed buildings is illustrated with examples for Delhi city.Rock outcrop motions are generated for the scenario earthquakes of magnitude, M w = 7.5, M w = 8.0 and M w = 8.5.Three actual soil sites of Delhi have been modeled and the free field surface motions and the response spectra are obtained.It has been observed that the PGA amplifications and the response spectra of the three sites are quite different for the earthquakes considered.The same has reflected in considerable procedure the band limited Gaussian white noise is windowed and filtered in the time domain and transformed into frequency domain.The Fourier amplitude spectrum is scaled to the mean squared absolute spectra and multiplied by a Fourier amplitude spectrum obtained from source path effects.Then, the spectrum is transformed back to time domain and the time history is obtained.From the analysis of recorded ground motions, it has been reported (Beresnev & Atkinson 1997) that point source models are not capable of reproducing the characteristic features of large earthquakes (M w > 6) viz., long duration and radiation of less energy at low to intermediate frequencies (0.2-2 Hz).Simulation of strong ground motion from finite fault model has been developed by Beresnev andAtkinson (1997&1998).In this model, the fault rupture plane is represented with an array of sub-faults and the radiation from each subfault is modeled as a point source similar to Boore's model (Boore, 1983).According to finite source model, the fault rupture initiates at the hypocenter and spreads uniformly along the fault plane radially outward with a constant rupture velocity triggering radiation from subfaults in succession.The improvements to finite source model viz., extended finite source model (Motazedian & Atkinson, 2005) implementing the effects of radiated energy on subfault size and dynamic corner frequency, are reported in literature when this chapter is written, however finite sources model (Beresnev & Atkinson 1997&1998) is used in the studies reported in this chapter.The details of the Fourier amplitude spectrum adopted in the present study and the assumptions made are illustrated subsequently.The Fourier amplitude spectrum A() of the point source of one element (sub-fault) is defined (Boore, 1983;Boore & Atkinson 1987;Brune, 1970) as Where,  is the angular frequency, S() is the source function, P() is the filter function for high frequency attenuation, G(R) is the geometric attenuation function, A n () is anelastic whole path attenuation function.S(), P(), G(R) and A n () are further defined below:

Source function, S()
The shape and amplitude of the theoretical source spectrum ( 2 -model, Aki (1967) is given by, where, P is the partition factor to represent one horizontal component, F is the free surface amplification factor, R  is the spectral average for radiation pattern, m o is the seismic moment of a sub-fault,  c is the corner frequency,  is the density in the vicinity of the source in g/cm 3 ,  is the shear wave velocity in km/sec and R is the epicentral distance in km.In the simulation of ground motion for Delhi region in the present study, the values of different parameters are adopted (Singh et al., 2002) as P=1/2.0;F=2.0; R  = 0.55; =2.85 gm/cc and  = 3.6 km/sec.
The moment magnitude (M w ) which defines the size of earthquake is related (Hanks & Kanamori 1979)  The rupture area (A) and sub-fault length (l) corresponding to a moment magnitude of earthquake can be calculated from empirical equations (Beresnev & Atkinson, 1998) as follows, For a sub-fault of equal dimensions (w=l, w, l being the dimensions of the sub-fault) the seismic moment of a sub-fault, m o is given by where Δσ is the stress parameter (Beresnev & Atkinson, 1998).The number of sub sources (N sub ) to be summed to reach the target seismic moment (M o ) is given by The corner frequency  c governs the acceleration amplitude and controls the frequency content of the earthquake at source is given by, where, y r is the constant representing the ratio of rupture velocity to shear wave velocity of source which is set to a value of 0.8 by Beresnev and Atkinson (1997), z s is the parameter indicating maximum rate of slip also known as strength factor.The value of z s may vary from 0.5 to 2.0 and in the present study a value of 1.4 (Singh et al., 2002) has been adopted for the simulation of earthquake motions for Delhi region.

Filter function for high frequency attenuation, P()
In order to account for the high frequency attenuation by the near-surface weathered layer either a fourth order Butterworth filter with cut off frequency  m =2f max or a spectral decay parameter kappa () is widely used in stochastic models.In the present study, Butter worth filter function P() with cutoff frequency f max = 15 Hz (Singh et al., 2002) has been adopted.

Geometric attenuation factor, G(R)
Geometric attenuation accounts for the decay and type of seismic waves.According to Singh et al. (2002) and Herrmann and Kijko (1983) for a distance of twice the crustal thickness the body waves dominate (direct seismic shear waves) and after that surface waves dominate (reflected L g waves).Depending on the earth's crust thickness tri-linear or bilinear relationships are used for the calculation of G. has been reported to be 45 -50 km (Tewari & Kumar, 2003) and bilinear relationship (Eq.10) is adopted in the present study.

Anelastic whole path attenuation factor, A n ()
The anelastic whole path attenuation factor A n () which represents wave energy loss due to radiation damping of rock is accounted by this factor A n (), where Q is the quality factor.The Q factor depends on the wave transmission quality of rock.For Himalayan arc region Q factor has been estimated by Singh et al. (2002) from the available earthquake records as follows.
where, f is the frequency in Hz.

Simulation of time history
The Fourier amplitude spectrum derived from the section above gives the frequency content of the earthquake ground motion.This frequency information is combined with random phase angles in a stochastic process to generate artificial ground motion (Boore, 1983) for each sub-fault.Simulations from each sub-fault are lagged and summed to get the time history of earthquake.Duration of the sub-fault time window, T w is represented as the sum of its source duration, T s and distance dependant duration, T d (Beresnev & Atkinson, 1997;Boore 2003).
In references Beresnev and Atkinson (1997) and Boore (2003), T s is taken as proportional to inverse of the corner frequency (1/f c ) and T d is taken as 0.05R.Finite fault simulation program (FINSIM) has been widely used for the generation of ground motions of large size earthquakes (Atkinson & Beresnev, 2002;Beresnev & Atkinson 1998;Roumelioti & Beresnev 2003;Singh et al., 2002) and hence has been adopted in the present study.
The seismological parameters (Table 1) used in the generation of rock outcrop motions for Delhi region have been broadly adopted from Singh et al. (2002).In order to minimize the noise due to random fault rupture in the simulation, 15 ground motions have been generated for each earthquake magnitude.One of the simulations of the time histories generated for rock outcrop (Ridge observatory) in the present study have been compared (Fig. 2), with one simulation obtained from Singh (2005), for each of the magnitudes 7.

Propagation of strong ground motion through soil layer using one dimensional equivalent linear analysis
One dimensional equivalent linear vertical wave propagation analysis is the widely used numerical procedure for modeling soil amplification problem (Idriss, 1990;Yoshida et al., 2002).In one dimensional wave propagation analysis, soil deposit is assumed to be having number of horizontal layers with different shear modulus (G), damping () and unit weight ().In the linear analysis, G and  are assumed to be constant in each layer.Since the soil will be subjected to nonlinear strain (Yoshida et al., 2002) even under small earthquake excitation equivalent linear analysis is preferred over linear analysis and the equivalent linear analysis program SHAKE (Ordonez, 2000, Schnabel et al., 1972 ) is used in the present study.Equivalent linear modulus reduction (G/G max ) and damping ratio () curves generated from laboratory test results are adopted from Vucetic and Dobry (1991) depending on the plasticity index of different soil layers.Since SHAKE is a total stress analysis program (Schnabel et al., 1972) depth of water table has not been considered in the analysis.

Typical soil strata for Delhi region
Three actual soil sites designated as site 1, site 2 and site 3 located in Delhi as shown in Fig. 3 are chosen in the present study.The layer wise soil characteristics (medium type) and the depth to the base of the layer from the surface is given in Tables 2 to 4. The shear wave velocity, V s measurements are not available for the sites chosen.However the variations of N values with depth are available from the geotechnical data as given in Tables 2 to 4. The variation of shear wave velocity along the depth in the present study is obtained by using the correlations suggested for Delhi region by Rao and Ramana (2004) as given in eq.14.
Site-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum 201

Response of three sites for the scenario earthquakes
The amplification ratios and response spectra are the engineering outputs required to calculate the response of buildings for site-specific analysis.Shear wave velocity of bedrock (quartzite) at Delhi is reported to be in the range of 1000 m/s to 2000 m/s (Parvez et al., 2004).The rock outcrop motions simulated as per the procedure given in section 2.2 are considered as bedrock motions for soil sites and the same are propagated through the soil strata of the three sites and the free field motions are obtained.As a typical case the bedrock level and free field motions at the top of three sites for one simulation of earthquake (M w = 7.5) for the three magnitudes are shown in Fig. 4. The variations of average amplification ratios of 15 earthquake simulations for the three sites are obtained.As a typical case, variations of average amplification ratios for M w = 8.5 earthquake is shown in Fig. 5 for the three earthquake magnitudes.It can be seen from the results that the peak amplification ratios as well as the frequencies at which the peak amplification ratios occur are quite different for the three sites.The fundamental time periods of the three sites for different earthquakes are given in Table 5. Difference in site period for larger magnitude earthquake is due to nonlinear response of soil sites for higher magnitudes.
M The average ratios of PGA of free field motion to the PGA of bedrock motions for the three sites are shown in Table 6.Also shown in Table 6 are the average peak ground accelerations (PGA) for the 15 simulations of bed rock motions and free field motions for site 1, site 2 and site 3.It can be observed that the PGA amplifications of the three sites are different for the three magnitudes of earthquake.Response spectra (5% damping) for the 15 simulations of free field motions and their average, on top of three sites have been obtained for all the three earthquake magnitudes.Typically for M w = 7.5, these have been shown for site 2 in Fig. 6.Further, the comparison of average response spectra for the three sites for the earthquake magnitudes M w = 7.5, M w = 8.0, M w = 8.5 are shown in Fig. 7. From the comparisons it can be inferred that the shapes of the response spectra vary quite significantly for the three sites under the same earthquake.

Response of buildings on the three sites
It is clearly seen from the comparison of response spectra, that buildings situated on the three sites will be subjected to different force levels during the same earthquake.In order to demonstrate the site-specific response of buildings, a three storey building and a fifteen storey building designated as B1 and B2 with plan details as shown in Fig. 8 are chosen for the present study.The buildings are assumed to be situated on the three soil sites (Fig. 3) chosen at Delhi.The earthquake is applied in y direction.Both the buildings are assumed to be having frames as stiffening elements with uniform beam and column sections along the height of the building.The beams are assumed to be axially rigid and have infinite flexural rigidity.All the columns are square and are assumed to be axially rigid.The structural properties of buildings are given in Table 7.  5).

Performance evaluation of RC framed building for site-specific earthquake
Inelastic response of buildings plays vital role in earthquake resistant design and performance based procedures aim to evaluate the inelastic response of building.After establishing the procedure and studying the elastic response of framed building for sitespecific scenario earthquake, in this section the procedure to determine the inelastic response of building for site-specific earthquake is illustrated in performance based environment.
Two important elements of seismic performance evaluation of buildings are demand spectrum and capacity spectrum.Demand spectrum is the representation of the severity of the ground motion while capacity spectrum depicts the ability of the structure to withstand forces of specific nature.While carrying out performance evaluation for site-specific earthquake, code based response spectrum needs to be replaced with site-specific spectrum and the same will be considered as demand spectrum.Capacity spectrum method (ATC 40, 1996;FEMA 273&274, 1996) has provisions to modify a demand spectrum to account for lengthening of the period or increase in the damping of the structure.The average response spectra for the three sites are represented in Acceleration Displacement Response Spectra (ADRS) format in Fig. 11.It is seen that for the same time period of the building the spectral acceleration and spectral displacement are different for the three sites.This clearly indicates that the same building will be subjected to different levels of damage due to the difference in elastic or inelastic displacement experienced by the building for the same earthquake when situated on different sites for Delhi region.2002), variation is from less than 50 m to more than 300 m (Fig. 12).In the present study 8 representative soil strata defined by depths 10m, 20m, 30m, 50m, 75m, 100m, 150m and 200m have been chosen.Shear wave velocity, modulus reduction curve and damping curve are the other important properties that influence the modification of ground motion through soil layer.For shear wave velocity, regression relations (Eq.15) have been suggested by Satyam (2006) based on seismic refraction and MASW tests.Based on the measured values, Delhi has been divided into three regions (Fig. 13) viz., (i) south and south central Delhi, (ii) north and north western Delhi and (iii) trans Yamuna Delhi.Separate shear wave velocity models viz., V s1 , V s2 , V s3 has been proposed for each region as given in equation 15.
V s1 =281 D s 0.08 (South and South Central Delhi) (15a) V s2 =217 D s 0.13 (West and North Western Delhi) (15b) where D s is the depth of soil stratum below the ground level in m.However, the model V s3 only has been considered for the performance evaluation of building in the present study.For dynamic characteristics, from the large number of borelog data available for Delhi region, it is observed that the Plasticity Index (PI) of soils at Delhi region varies from 0% to 15%.Modulus reduction curves and damping curves for Delhi soil corresponding to PI=0% (Non plastic), PI=15% (low plasticity) soil have been adopted as explained in section 3 from Vucetic and Dobry (1991).For rock, modulus reduction curves and damping curves have been chosen from Schnabel and Seed (1972).Artificial ground motions are generated for rock outcrop site for earthquake magnitude M w = 8.5 with the parameters given in Table 1, and further propagated through different depths of soil stratum.Ground motions are obtained at the top of representative soil sites by conducting equivalent linear one dimensional wave propagation analysis as explained earlier, using the program SHAKE2000 (Ordonez, 2000).The time periods of the 8 different soil stratum depths considered in the present study are given in Table 8.Using the surface ground motions, the average Depth Dependent Response Spectra (DDRS) of 15 random simulations of the ground motions at soil surface have been obtained corresponding to 2%, 5%, 10%, 15%, 20% and 25% damping.

Sl.
No.After carrying out the site-specific analysis the response spectra of a site is available in the standard spectral acceleration (S a ) versus time period (T) format and the same is converted to Acceleration-Displacement Response Spectra (ADRS) format using eq.16.

Depth of soil stratum (m)
where:

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T i = time period of the building in secs g = acceleration due to gravity in m/s 2 i = i th point of the spectra

Generation of capacity spectrum from capacity curve through nonlinear static analysis
Plan of an eight storey building chosen (designated as B3) for the present study is shown in Fig. 14.Overall length and width of the building are 11.4m and 10.9m, respectively.Height of the building is 23.6m.Cross section and reinforcement details of the beams and columns are modeled as given in the construction drawings of the building.The total lumped mass due to dead and participating live loads of the building for the bottom six stories is equal to 179.2 tons while the lumped mass for seventh and eighth stories is equal to 90.1 tons and 17.9 tons, respectively.Building is modeled using SAP2000 computer program ( 2004), with default PMM hinge properties for column and default M3 properties for beam.Displacement controlled nonlinear static pushover analysis has been carried out for the 3D building model and the capacity curve of the building is obtained.Further, the capacity curve is transformed to capacity spectrum using Eqns.17 and 18. where: V j = base shear at the j th point of the capacity curve W = weight of the building as sum of dead load and percentage live load 1 = modal mass coefficient for the first natural mode = modal participation factor for the first natural mode  1 , roof = amplitude at roof level in first natural mode

Determination of performance point
In the present study, site-specific demand spectrum for 2%, 5%, 10%, 15%, 20% and 25% damping are obtained for the building.According to ATC 40 (1996), effective damping (β eff ) of the building during earthquake excitation is combination of viscous damping that is inherent in the building (about 5%) and hysteretic damping ( o ) (that is related to the area inside the hysteretic loops formed when the earthquake force is plotted against the structural displacement).In view of this, it is required to modify the demand spectrum to account for the effective damping of the structure.An iterative method as suggested by authors (Kamatchi et al., 2010 a ) is used to determine the performance point, wherein, demand spectrum has to be updated in each iterative cycle till convergence is achieved.In the process, effective damping is obtained as per the procedure suggested in ATC 40 (1996).

Performance points for the chosen building
The capacity and demand curves for the eight different depths of soil stratum are obtained for building and shown in Fig. 15.For the soil stratum depths of 10m, 20m, 30m, 50m and 200m, the 5% demand curve intersect the capacity curve in the elastic response region.For the other depths (75m, 100m and 150m), the intersection points are found to lie in inelastic response region.For these three depths, spectral reduction factors are applied to 5% demand spectra and the performance points are obtained as per ATC 40 (1996).This is carried out through number of trials as shown in Fig. 16.The trial performance points are arrived by using effective damping ( eff ), Spectral reduction factor for acceleration predominant region (SR A ) and velocity predominant region (SR v ) corresponding to soil stratum depths of 75m, 100m and 150m.The base shear (V b ) and roof displacements( inel ) corresponding to the final performance points of the building for 75m, 100m and 150m depths of soil stratum are compared with corresponding values obtained for DBE earthquake and Medium soil site conditions as per IS 1893(Part 1)-2002 (2002)

Development of artificial neural networks for site-specific seismic analysis of buildings
In the earlier sections, procedure for carrying out site-specific analysis for scenario earthquakes is demonstrated with examples of RC framed buildings.It is accepted in literature, site-specific seismic analysis is mandatory for sites of specific soil (F type) (IBC 2009) and for earthquake-resistant design of important and critical structures.It is being insisted in this chapter that, even for other structures, and for medium soil, it is preferred to carry out a detailed site-specific analysis to arrive at the design force levels.Site-specific analysis requires considerable modelling and computational effort w.r.t.representing the generation as well as propagation of strong ground motion and it's effect on the structures.As an alternative, ANN models are generated in this chapter for the prediction of sitespecific spectral accelerations.Standard feed forward back propagation neural network algorithm with one hidden layer has been adopted to implement this.The main advantage of the neural network model lies in calculation of the realistic site-specific design base shear values without the need to generate strong ground motions and complex modelling of the soil profile.

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Site-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum 217

Input and output parameters for ANN models
Initially the following parameters are identified as probable input parameters and the average spectral acceleration (S a /g) is identified as the output parameter for sensitivity analysis.
Moment magnitude of the earthquake (M w ), Shear wave velocity of rock half-space (V sr ), Average plasticity index of the soil stratum (PI), Average soil density of the soil stratum (), Depth of the soil stratum above the bedrock (h), Shear wave velocity model (V sm ), Damping ratio of SDOF oscillator () and Time period of the SDOF oscillator (T b ) Based on the results of the sensitivity studies (Kamatchi, 2008; Kamatchi et al., 2010 b ) for the development of neural network models five input parameters viz., M w , h, , T b, V sm and one output parameter S a /g are chosen.

Choosing the configuration of the neural networks
Multilayer neural network with neurons in all the layers and fully connected in a feed forward manner has been chosen for the present implementation.Sigmoid function (with output in the range of 0 to 1) is used for activation and the back propagation learning algorithm is used for training.The feed forward back propagation algorithm has been used successfully for many civil engineering applications and is considered as one of the most efficient algorithms for engineering applications (Adeli, 2001).One hidden layer has been chosen for the network and the number of neurons in the hidden layer is decided in the learning process by trial and error.

Training of neural network
As the number of data sets is large, for the sake of convenience in handling the data during training, it has been decided to have two neural networks.The soil stratum depths up to 75 m have been taken in the first network designated as NET1 with rest of the parameters assuming all the values in their respective ranges.Similarly, the second network designated as NET2 includes soil stratum depths up to 200 m and has number of patterns as that for NET1, i.e., 49815.The data has been randomly partitioned (Reich & Barai, 1999), with two third of the data sets being used for training and the remaining are used for testing.The number of sampling points for the input parameters and the number of data sets are shown in Table 10., 1999).For training, the number of neurons in the hidden layer has been varied and several trials have been carried out.Architecture of the network (5-14-1) with 5 neurons in the input layer, 14 neurons in the hidden layer and 1 neuron in the output layer has been found to predict the results with good accuracy for the network NET 1 and 5-15-1 architecture is found to predict good results for NET 2. The standard back propagation algorithm with learning rate () equal to 0.9 has been used for training.The

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patterns are found to be more than 0.9 which guarantees better performance of network for any new input.

Estimation of base shear for framed buildings using ANN models
A six storey building designated as B4 (Fig. 17) and fifteen storey building, B2 with details as given in section 5 (Fig. 8, Table 7), are considered for the validation of ANN models.Both the buildings are assumed to be situated in Delhi and analyzed for earthquakes having moment magnitude of 7.8 and 8.2 and originate from central seismic gap of Himalayan region.The building B4 is assumed to be at a location with shear wave velocity model V s2 and on a soil stratum of depth 52 m above the bedrock.The building B2 is assumed to be in a location with shear wave velocity model V s3 and on a soil stratum of depth 105 m above the bedrock.The first three time periods of buildings for B4, B2 are evaluated as T b4 =0.679sec, 0.23sec, 0.14sec and T b2 = 1.62sec, 0.491sec, 0.359sec respectively.The damping ratios for B4 and B2 are assumed to be 0.03 and 0.06 respectively for all the modes.The input values and the corresponding output S a /g values predicted by neural network along with those obtained by a detailed SSA are given in

Summary
In this chapter, the importance and procedures to carry out site-specific seismic analysis of framed buildings for long distance large magnitude earthquakes including the effect of depth of soil stratum are illustrated for Delhi capital city of India, with scenario earthquakes from central seismic gap of Himalayan region as an example.Steps involved in carrying out site-specific seismic analysis for scenario earthquakes are discussed.Rock outcrop motions are generated for Delhi for the scenario earthquakes of magnitude, M w = 7.5, M w = 8.0 and M w = 8.5.Three actual soil sites (medium soil type) are modeled and the free field surface www.intechopen.comSite-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum 221 motions and the response spectra are obtained.It has been observed that the PGA amplifications and the response spectra of the three sites are quite different for the earthquakes considered.It is clear from response of two RC framed building that the performance of the buildings will be different when situated on three different soil sites.
From the studies made, it can be concluded that, it may be necessary to perform the sitespecific analyses of buildings at sites having medium types of soil as well.
Having established the importance of carrying out site-specific seismic analysis for moderate sites and RC framed buildings, the procedure to carry out seismic performance evaluation of existing building for site-specific earthquake is demonstrated for an eight storey building assumed to be situated on different depths of soil stratum for Delhi region.Rock outcrop motions generated for earthquake of M w = 8.5 are propagated through different depths of representative soil stratum and depth dependant demand spectrum are obtained.The effect of depth dependant response spectrum on the performance of building is studied for eight different depths of soil stratum above bedrock.From the studies made, it is clear that considering the design spectra suggested by seismic codes and only the top 30 m soil stratum to include the effects of soil amplification may not ensure safe seismic performance of a building.It is further seen that the site-specific earthquake and the depth of soil stratum have significant influence on the performance of the building both in terms of inelastic displacement as well as inelastic base shear.Procedure to develop ANN models to rapidly estimate the site-specific spectral acceleration of structures is illustrated with Delhi as an example.From the results of sensitivity studies conducted earlier for Delhi city, moment magnitude of the earthquake, M w , depth of the soil stratum above the bedrock, h, shear wave velocity model, V sm , damping ratio,  and time period, T b , of the SDOF oscillator are identified as governing input parameters for predicting the output of spectral acceleration, Sa/g.Excellent performance of the trained neural networks has been demonstrated by the calculated values of coefficient of correlation.Trained neural networks are validated by using borelog data of an actual soil site.In addition, the neural networks have been validated for two different buildings assumed to be located in Delhi city.Performance of ANN models developed are checked and validated with number of examples.Validation results for two different buildings included in this chapter indicates that the root mean square percentage error is within tolerable values for both the buildings analysed in the present study.The procedures suggested in this chapter is suitable for carrying out site-specific seismic analysis for framed buildings for any region for which information about seismic hazard in terms of scenario earthquake and the relevant geological and geotechnical details of the region are made available.

205Fig. 4 .
Fig. 4. Bedrock level and free field motions at the top of three sites for one simulation of earthquake, M w =7.5

Building
The storey shears have been obtained by response spectrum method as per IS 1893(Part 1)-2002, (2002).In the evaluation of storey shears response reduction factor has been taken equal to one.www.intechopen.comSite-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum 207 Structural properties of the buildings The comparison of storey shears for buildings B1 and B2 for site-specific earthquakes and storey shears obtained by considering the three sites as medium soil sites(MS) for design basis earthquake(DBE) as per seismic code IS 1893 (Part 1)-2002, (2002) for Delhi are shown in Fig. 9.For both the buildings storey shears obtained as per IS 1893 (Part 1)-2002, (2002) are different from the storey shears from site-specific analysis.Comparison of displacement responses for B1 and B2 are given in Fig. 10.It may be noted that, larger variation in base shear and displacement response for different sites for B1 is observed due to the proximity of fundamental time period (0.3 sec) of B1 to the site periods (Table

Fig. 11 .
Fig. 11.Comparison of response spectra for three sites in ADRS format

Fig. 13 .
Fig. 13.Three zones of Delhi based on surface geology

Fig. 14 .(Fig. 16 .
Fig. 14.Plan of the building, B3 to seismic moment (M o ) of the earthquake by, The thickness of crust near Delhi www.intechopen.comSite-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum 199

Table 2 .
Geotechnical profile at Site 1

Table 4 .
Geotechnical profile at Site 3

Table 5 .
Fundamental time periods of the three sites in seconds

Table 8 .
Time periods of soil strata ordinate in m S ai = spectral acceleration ordinate in units of g www.intechopen.comSite-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum

Table 9 .
in Table9.Comparison of V b and Δ inel for reduced spectra with DBE Normalisation of input and output parameters is carried out and the factors are available in Table11.Input parameters are normalised in linear sense while the output parameter (S a /g) is normalised in logarithmic scale to accommodate the exceptionally large variation in its value.Further, a bias has been added to satisfy the mathematical validity.The training is carried out using the Stuttgart Neural Network Simulator (SNNS

Table 12 .
Table12gives the mean square error at this stage.The percentage root mean square error of 14,815 test patterns is found to be 3.479% and 3.658% for the two networks NET1 and NET2, respectively.Using weights and biases of the trained neural networks, a simple program has been developed to arrive at the spectral acceleration values for everyday use in a design office.Configuration of networks, mean square errors and number of epochs

7.4 Checking the performance of the neural networks
Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum

Table 13 .
It may be noted that parameters of these validation examples have not been used in training or testing of the neural network.

Table 13 .
Input parameters and the output from the neural network Storey level shears obtained for buildings B4 and B2 by Site-specific seismic analysis and those obtained using predicted S a /g values from ANN are presented in Table14for comparison.The root Mean Square percentage Errors (MSE) in the predicted storey shears of buildings B4 & B2 are 1.43%, 4.3% respectively.The maximum percentage errors in the predicted storey shears for B4 & B2 are 2.99% and 4.98%, respectively.This has to be seen in the background that the time involved for the generation of strong motion for a given magnitude of earthquake, ground response analyses through soil layer and the computation of surface level spectral acceleration involves huge effort and enormous time and computational cost.The neural network based on the proposed methodology is, on the other hand, capable of predicting the spectral acceleration values at a fraction of these resources.

Table 14 .
Comparison of storey shear This chapter is published with kind permission of the Director, CSIR-Structural Engineering Research Centre.The first author sincerely acknowledges the support extended by Dr. J. Rajasankar, Senior Principal Scientist, Dr. K. Balaji Rao, Senior Principal Scientist, Dr. S. Arunachalam, Chief Scientist, Dr. N. Lakshmanan, Former Director, CSIR-Structural Engineering Research Centre in preparation of this chapter.