Development of New Ground Motion Prediction Equation for the North and Central Himalayas Using Recorded Strong Motion Data

Ramkrishnan, R.; Kolathayar, Sreevalsa; Sitharam, T. G.

doi:10.1080/13632469.2019.1605318

Cited by 32 publications

(6 citation statements)

References 60 publications

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“…From Table 7, it can be seen that the seismic parameters, a and b, obtained from the present study, compare well with the values reported by others (NDMA 2010; Sharma and Malik 2006;and Bahuguna and Sil 2020). For SSS in the present study, the PGA values corresponding to return periods of 475, 950, and 2475 years, obtained using GMPE (Ramkrishnan et al 2020, Jain et al 2000, are in the range of 0.14-0.69g, 0.17-0.86g, and 0.22-0.93g, respectively. The calculated PGA values for some selected cities in the region are compared with those from previous studies as shown in Table 8.…”

Section: Resultssupporting

confidence: 91%

“…For focal depths greater than 45 km, the PGA values given by the JAIN2000 model lies in the range of observed values, and for lesser depth, the RAMK2020 model is found to be more appropriate. Therefore, in the present study, GMPEs RAMK2020 (Ramkrishnan et al 2020) is adopted for seismic source zones with an average focal depth of less than 45 km and JAIN2000 (Jain et al 2000) is adopted for seismic source zones with an average focal depth greater than or equal to 45 km, respectively. with SE = 0.59…”

Section: Ground Motion Prediction Equation (Gmpe)mentioning

confidence: 99%

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An integrated assessment of seismic hazard exposure and its societal impact in Seven Sister States of North Eastern Region of India for sustainable disaster mitigation planning

Agrawal¹,

Gupta²,

Dixit³

et al. 2021

Preprint

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The Seven Sister States of the North Eastern Region of India, located on the complex seismotectonic belt, is characterized by high seismicity. A comprehensive seismic hazard exposure assessment is carried out by quantifying hazard using a probabilistic approach, vulnerability by factor analysis, and exposure mapping by integrating seismic hazard and vulnerability. Peak ground acceleration (PGA) values at bedrock are calculated with the help of ground motion prediction equations (GMPE) for 10% probability of exceedance in 50 years (475 years) and 100 years (950 years), and 2% probability of exceedance in 50 years (2475 years). The resulting spatial distribution of the PGA values considering return periods of 475, 950, and 2475 years are presented through seismic hazard maps. The social vulnerability analysis indicates that 21 districts covering 91.43% area of the state of Assam and the entire state of Tripura are under high vulnerability. With the help of spatial cluster analysis, it is found that 17.14% of the study area are having an average social vulnerability index (SVI) score of 0.329 and therefore can be considered as hotspots. Through seismic hazard analysis, it is observed that more than 50% of the area of North East India is under moderate to very high exposure class. The seismic hazard maps developed can help in disaster mitigation planning and execution leading to sustainable development goals and targets.

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Section: Resultssupporting

confidence: 91%

Section: Ground Motion Prediction Equation (Gmpe)mentioning

confidence: 99%

An integrated assessment of seismic hazard exposure and its societal impact in Seven Sister States of North Eastern Region of India for sustainable disaster mitigation planning

Agrawal¹,

Gupta²,

Dixit³

et al. 2021

Preprint

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show abstract

“…Sharma et al [2] provided GMM based on earthquake data for the Himalayan region, which is an active inter-plate region. Ramkrishnan et al [3] generated GMM for the north and central Himalayan region using actual earthquake record data. Bajaj and Anbazhagan [4] provided attenuation relations for the active region using limited strong ground motion data.…”

Section: Introductionmentioning

confidence: 99%

Development of a New Ground Motion Model for a Peninsular Indian Rock Site

Akella

Agrawal

Chattopadhyay

2023

Proc. eng. technol. innov.

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The ground motion model (GMM) plays a vital role in the generation of seismic design basis ground motion parameters. Even though many intra-plate GMMs are available, very few of them are based on Peninsular India (PI) region-specific seismological parameters. Hence, it is imperative to develop a GMM using seismological parameters derived from earthquakes in the Peninsular Indian region. In this study, a new GMM is developed for a PI rock site. Due to the scarcity of real earthquakes, artificial earthquake records are simulated to generate a new GMM for PI. The accelerograms of these artificial earthquakes are obtained from the stochastic finite fault simulation technique. Region-specific seismological parameters are obtained from the available PI earthquakes. The generated GMM is compared with other intra-plate GMMs for different earthquake magnitudes. Also, the generated GMM is validated with the Koyna earthquake record and it is observed that the GMM’s predictions are closer to the record.

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“…These calculations are known as attenuation relationships, and they express ground motion as a function of distance and magnitude, as well as other variables like faulting type [2]. In the past, such relationships have been developed for numerous locations of the world [3,4,[13][14][15][16][17][18][5][6][7][8][9][10][11][12]. Most of these studies use regression analysis.…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE13 Variation of PGA obtained by existing relationship and developed ANN model for NGA-West2 database for V s30 = 760 m/s.…”

mentioning

confidence: 99%

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Challagulla,

Suluguru,

Noroozinejad Farsangi

et al. 2023

The Journal of Engineering

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The seismic resilience of a structure has been evaluated using peak ground acceleration (PGA). Ground motion parameters such as source characteristics, local site conditions are used to forecast the PGA of the ground motion. This paper aims to develop an Artificial Neural Network (ANN) based model to predict the PGA. Here, hypocentral distance (Rhypo${R}_{hypo}$), shear wave velocity (Vs30${V}_{s30}$), and moment magnitude (Mw${M}_w$), are used as input parameters. The model uses 12,706 ground motion recordings from 283 earthquakes from the revised NGA‐West2 database supplied by Pacific Engineering Research Centre. Among the whole data, 70% of the data is set for training, 15% for validation, and 15% for testing the network. The R value derived from the testing dataset is 0.952, indicating the excellent performance of a network. An extensive parametric study is conducted with the PGA values, and the results indicate that the PGA increases with the magnitude and decreases with the hypocentral distance. The predicted PGA values from the present study are comparable with those from the existing relationships in the global database. The generated ANN model is further verified by comparing the predicted and recorded PGA values of an actual recorded event.

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Development of New Ground Motion Prediction Equation for the North and Central Himalayas Using Recorded Strong Motion Data

Cited by 32 publications

References 60 publications

An integrated assessment of seismic hazard exposure and its societal impact in Seven Sister States of North Eastern Region of India for sustainable disaster mitigation planning

An integrated assessment of seismic hazard exposure and its societal impact in Seven Sister States of North Eastern Region of India for sustainable disaster mitigation planning

Development of a New Ground Motion Model for a Peninsular Indian Rock Site

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

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