Generating multiple spectrum compatible accelerograms using stochastic neural networks

Earthquake Engineering and Structural Dynamics

Volumn 30, Issue 7, 2001, Pages 1021-1042

  Lin, Chu Chieh J ^a,b   Ghaboussi, Jamshid ^a  

^a University of Illinois at Urbana Champaign   (United States)

^b Taiwan Construction Research Institute   (Taiwan)

Author keywords

Accelerograms; Earthquake engineering; Ground motions; Neural networks; Stochastic

Indexed keywords

DATA COMPRESSION; NEURAL NETWORKS; RANDOM PROCESSES; SPECTRUM ANALYSIS;

EARTHQUAKE ACCELEROGRAMS;

EARTHQUAKES;

ARTIFICIAL NEURAL NETWORK; EARTHQUAKE ENGINEERING; SEISMIC RESPONSE; STRUCTURAL RESPONSE;

EID: 0035397410     PISSN: 00988847     EISSN: None     Source Type: Journal    
DOI: 10.1002/eqe.50     Document Type: Article

References (33)

1. Earthquake spectra and design
2. Generation of artificial earthquakes
3. Simulation of nonstationary random process
4. Spectrum-compatible motions for design purposes
5. Simulation of artificial earthquakes
6. Spectrum-consistent time-history generation
7. Generation of artificial time histories, rich in all frequencies, from given response spectra
8. Generation of spectrum compatible accelerograms
9. Generation of artificial strong motion accelerograms
10. Simulation of earthquake ground motions using autoregressive moving average (ARMA) model
11. Improved method of generation of artificial time-history, rich in all frequencies, from floor spectra
12. A method of artificial generation of earthquake ground motion
13. Simulation of stationary random processes: Two-stage MA to ARMA approach
14. Use of empirical green's function, spectral ratios, and kinematic source models for simulating strong ground motion
15. Estimation of response spectra and simulation of nonstationary earthquake ground motions
16. Biologically inspired soft computing methods in structural mechanics and engineering
17. Replicator neural network in generating artificial earthquake accelerograms
18. New method of generating spectrum compatible accelerograms using neural networks
19. Faster-learning variations on back-propagation: An empirical study
20. New method of material modeling using neural networks
21. Autoprogressive training of neural network constitutive models
22. A learning algorithm for boltzmann machines
23. Combinatorial optimization with Gaussian machines
24. Probabilistic bit stream neural chip: Theory
25. Learning in stochastic bit stream neural networks
26. Probability neural network
27. Analytical and simulation results for stochastic fitzhugh-nagumo neurons and neural network
28. Random neural network with negative and positive signals and product form solution
29. Data manifolds, natural coordinates, replicator neural networks, and optimal source coding
30. Learning and architecture determination through automatic node generation