Options for sustainable earthquake-resistant design of concrete and steel buildings

2012 ◽  
Vol 3 (6) ◽  
pp. 783-804 ◽  
Author(s):  
Amador Teran Gilmore
1987 ◽  
Vol 3 (2) ◽  
pp. 329-346 ◽  
Author(s):  
R. D. Hanson ◽  
H. W. Martin

A description of the various types of steel structures built in Mexico City includes comparisons of the older types of steel construction with more modern buildings. Performance of steel buildings in the September 1985 earthquake was related to the local geotechnical conditions including foundation behavior. The collapses of the Edificio 21 Atlas and Conjunto Pino Suarez buildings raise important earthquake resistant design and research issues.


1983 ◽  
Vol 1983 (339) ◽  
pp. 127-136 ◽  
Author(s):  
Yoshio OHNE ◽  
Hidehiro TATEBE ◽  
Kunitomo NARITA ◽  
Tetsuo OKUMURA

Author(s):  
GENE F. SIRCA ◽  
HOJJAT ADELI

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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