TY - GEN
T1 - Development of Region-Specific New Generation Attenuation Relations for North India Using Artificial Neural Networks
AU - Huang, He
AU - Ramkrishnan, R.
AU - Kolathayar, Sreevalsa
AU - Garg, Ankit
AU - Yadav, Jitendra Singh
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - Present study focuses on developing region-specific New Generation Ground Motion Prediction Models using Artificial intelligence technique for North India purely based on a measured ground motion data from specific region. Simple single hidden layered feed forward multilayer perceptron networks with back-propagation learning algorithm are used. A total of 280 data points of recorded strong motion data from the Kangra and Uttar Pradesh (UP) arrays, made available by the Program for Excellence in Strong Motion Studies (PESMOS), were used to train these networks. The first model predicts Moment Magnitude for a given Hypocentral Distance and Peak Ground Acceleration. The second model predicts Peak Ground Acceleration (PGA) for a given Hypocentral Distance (HPD) and Moment Magnitude (MM). Performance analysis, Uncertainty analysis and analysis of interactive effects have been done to test the reliability of the generated models. Optimization analysis was also performed to predict possible inputs of the models for a given set of outputs. Models have performed reasonably well for the given amount of non-linearity in the data.
AB - Present study focuses on developing region-specific New Generation Ground Motion Prediction Models using Artificial intelligence technique for North India purely based on a measured ground motion data from specific region. Simple single hidden layered feed forward multilayer perceptron networks with back-propagation learning algorithm are used. A total of 280 data points of recorded strong motion data from the Kangra and Uttar Pradesh (UP) arrays, made available by the Program for Excellence in Strong Motion Studies (PESMOS), were used to train these networks. The first model predicts Moment Magnitude for a given Hypocentral Distance and Peak Ground Acceleration. The second model predicts Peak Ground Acceleration (PGA) for a given Hypocentral Distance (HPD) and Moment Magnitude (MM). Performance analysis, Uncertainty analysis and analysis of interactive effects have been done to test the reliability of the generated models. Optimization analysis was also performed to predict possible inputs of the models for a given set of outputs. Models have performed reasonably well for the given amount of non-linearity in the data.
KW - ANN
KW - Attenuation relationships
KW - GMPE
KW - Ground motion
KW - PGA
KW - Seismic hazard analysis
UR - http://www.scopus.com/inward/record.url?scp=85101419631&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4324-5_6
DO - 10.1007/978-981-33-4324-5_6
M3 - Conference Proceeding
AN - SCOPUS:85101419631
SN - 9789813343238
T3 - Lecture Notes in Civil Engineering
SP - 85
EP - 101
BT - Proceedings of the 1st Indo-China Research Series in Geotechnical and Geoenvironmental Engineering
A2 - Garg, Ankit
A2 - Solanki, C. H.
A2 - Bogireddy, Chandra
A2 - Liu, Junwei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st Indo-China Research Series in Geotechnical and Geoenvironmental Engineering, 2020
Y2 - 8 May 2020 through 19 May 2020
ER -