Discussion of “A Method for Earthquake Resistant Design of Earth Dams”

1966 ◽  
Vol 92 (6) ◽  
pp. 251-254
Author(s):  
Dalim K. Majumdar ◽  
Gary G. LaFrance
Keyword(s):  
Earth Dams ◽  
Earthquake Resistant Design ◽  
Earthquake Resistant

Earthquake-Resistant Design of Earth Dams

1967 ◽  
Vol 4 (1) ◽  
pp. 1-27 ◽  
Author(s):  
H Bolton Seed
Keyword(s):  
Earth Dams ◽  
Earthquake Resistant Design ◽  
Earthquake Resistant

scholarly journals FUNDAMENTAL STUDY ON EARTHQUAKE RESISTANT DESIGN OF FILL-TYPE DAMS

1983 ◽  
Vol 1983 (339) ◽  
pp. 127-136 ◽  
Author(s):  
Yoshio OHNE ◽  
Hidehiro TATEBE ◽  
Kunitomo NARITA ◽  
Tetsuo OKUMURA
Keyword(s):  
Fundamental Study ◽  
Earthquake Resistant Design ◽  
Earthquake Resistant

A NEURAL NETWORK-WAVELET MODEL FOR GENERATING ARTIFICIAL ACCELEROGRAMS

2004 ◽  
Vol 02 (03) ◽  
pp. 217-235 ◽  
Author(s):  
GENE F. SIRCA ◽  
HOJJAT ADELI
Keyword(s):  
Neural Network ◽  
Geographic Location ◽  
Training Data ◽  
Wavelet Network ◽  
Design Spectrum ◽  
Earthquake Resistant Design ◽  
Earthquake Resistant ◽  
Structural Configurations ◽  
Earthquake Accelerograms ◽  
Artificial Accelerograms

In earthquake-resistant design of structures, for certain structural configurations and conditions, it is necessary to use accelerograms for dynamic analysis. Accelerograms are also needed to simulate the effects of earthquakes on a building structure in the laboratory. A new method of generating artificial earthquake accelerograms is presented through adroit integration of neural networks and wavelets. A counterpropagation (CPN) neural network model is developed for generating artificial accelerograms from any given design spectrum such as the International Building Code (IBC) design spectrum. Using the IBC design spectrum as network input means an accelerogram may be generated for any geographic location regardless of whether earthquake records exist for that particular location or not. In order to improve the efficiency of the model, the CPN network is modified with the addition of the wavelet transform as a data compression tool to create a new CPN-wavelet network. The proposed CPN-wavelet model is trained using 20 sets of accelerograms and tested with additional five sets of accelerograms available from the U.S. Geological Survey. Given the limited set of training data, the result is quite remarkable.

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