Assessment of Seismic Site Effects in 2-D Alluvial Valleys Using Neural Networks

Earthquake Spectra

Volumn 16, Issue 3, 2000, Pages 661-680

  Paolucci, Roberto ^a   Colli, Pietro ^c   Giacinto, Giorgio ^b  

^a POLITECNICO DI MILANO   (Italy)

^b UNIVERSITY OF CAGLIARI   (Italy)

^c NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; PATTERN RECOGNITION; SEISMIC RESPONSE; SITE EFFECT; VALLEY;

EID: 0003518187     PISSN: 87552930     EISSN: None     Source Type: Journal    
DOI: 10.1193/1.1586133     Document Type: Article

References (28)

1. Baum, E. B. and Haussler, D., 1989, What size network gives valid generalization, Neural Computation, 1, 151-160.
2. Boissonnade, A. C., Dong, W. M., Liu, S. C., and Shah, H. C., 1984, Use of pattern recognition and Bayesian classification for earthquake intensity and damage estimation, Soil Dynamics and Earthq. Engng, 3, 145-149.
3. Bard, P.-Y. and Bouchon, M., 1985, The two-dimensional resonance of sediment filled valleys, Bull. Seism. Soc. Am., 75, 519-541.
4. Casadei, F. and Gabellini, E., 1997, Implementation of a 3-D coupled spectral element/finite element solver for wave propagation and soil-structure interaction simulations. Part I - Models. Tech. Rep. of the Joint Research Centre for the EC Environment and Climate Project TRISEE (3D Site Effects and Soil-Foundation Interaction in Earthquake and Vibration Risk Evaluation), EC EUR 17730 EN, Ispra, Italy.
5. Casadei, F. and Gabellini, E., 1998, Implementation of a 3D coupled spectral element/finite element solver for wave propagation and soil-structure interaction simulations. Part II - Numerical examples. Tech. Rep. of the Joint Research Centre for the EC Environment and Climate Project TRISEE (3D Site Effects and Soil-Foundation Interaction in Earthquake and Vibration Risk Evaluation), EC EUR 18051 EN, Ispra, Italy.
6. Chandrasekaran, B. and Goel, A., 1988., From numbers to symbols to knowledge structures: Artificial intelligence perspectives on the classification task, IEEE Transactions on Systems Man and Cybernetics, 18, 415-424.
7. Chávez-García F. J. and E. Faccioli, 2000, Complex site effects and building codes: making the leap., Journal of Seismology, 4, 23-40.
8. Colli, P., 1999, Caratterizzazione di progetto della risposta sismica di valli alluvionali mediante reti neurali, Thesis for the degree of Civil and Environmental Engineer at Politecnico di Milano, Italy, 190 pp. (in Italian).
9. Duda, R. O. and Hart, P. E., 1973, Pattern Classification and Scene Analysis, John Wiley & Sons, Inc., New York, 482 pp.
10. Faccioli, E., Maggio, F., Paolucci, R., and Quarteroni, A., 1997, 2-D and 3-D elastic wave propagation by a pseudo-spectral domain decomposition method, Journal of Seismology, 1, 237-251.
11. nd Int. Sym. on the Effects of Surface Geology on Seismic Motion, I, 147-160, Yokohama, Japan.
12. nd Ed., Academic Press, New York, NY.
13. Ghaboussi, J., 1993, An overview of the potential applications of neural networks in civil engineering, Proc. ASCE Congress on Structural Engineering in Natural Hazards Mitigation, New York, 2, 1324-1330.
14. Ghaboussi, J. and Lin, C. J., 1998, New method of generating spectrum compatible accelerograms using neural networks, Earthquake Engineering & Structural Dynamics, 27, 377-396.
15. Giacinto, G., Paolucci, R., and Roli, F., 1997, Application of Neural Networks and Statistical Pattern Recognition Algorithms to Earthquake Risk Evaluations, Pattern Recognition Letters, 18, 1353-1362.
16. Goh, A.T.C., 1994, Seismic liquefaction potential assessed by neural networks, ASCE Journal of Geotechnical Engineering, 120, 1467-1480.
17. Graves, R., 1998, Three-dimensional finite-difference modeling of the San Andreas Fault: source, parameterization and ground-motion levels, Bull. Seism. Soc. Am., 88, 881-897.
18. Haykin, S., 1999, Neural Networks - A comprehensive foundation (2e). Prentice Hall, 842 pp.
19. Olsen, K., Archuleta, R., and Matarese, J., 1995, Three-dimensional simulation of a magnitude 7.75 earthquake on the San Andreas Fault, Science, 270, 1628-1632.
20. Pao, Y., 1989, Adaptive Pattern Recognition and Neural Networks. Addison-Wesley Publishing Company, Inc., 309 pp.
21. Paolucci, R., Suárez, M., and Sánchez-Sesma, F. J., 1992, Fast computation of SH seismic response for a class of alluvial valleys, Bull. Seism. Soc. Am., 82, 2075-2086.
22. Raptakis, D., Theodulidis, N., and Pitilakis, K., 1998, Data analysis of the Euroseistest strong motion array in Volvi (Greece): standard and horizontal-to-vertical spectral ratio techniques, Earthquake Spectra, 14, 203-224.
23. Romo, M. P., 1999, Earthquake geotechnical engineering and artificial neural networks, IV Arthur Casagrande Lecture, Proc. XI Panamerican Conf. on Soil Mechanics and Geotechnical Engineering, Iguazu, Brazil.
24. Rumhelart, D. E. and McClelland, J. L. eds., 1986, Parallel Distributed Processing. Vol. 1: Foundations, MIT Press, Cambridge, MA.
25. Serpico, S. B. and Roli, F., 1995, Classification of multisensor remote-sensing images by structured neural networks, IEEE Transactions on Geoscience and Remote Sensing, 33, 562-578.
26. Tickle, A. B., Andrews, R., Golea, M., and Diederich, J., 1998, The truth will come to light: direction and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Transactions on Neural Networks, 9, 1057-1068.
27. Trifunac, M. D. and Brady, A. G., 1975, A study on the duration of strong earthquake ground motion, Bulletin of the Seismological Society of America, 65, 581-626.
28. nd Int. Sym. on the Effects of Surface Geology on Seismic Motion, 1, 263-270, Yokohama, Japan.