Earthquake ground-motion duration estimation using general regression neural network

Document Type : Article

Author

Department of Civil Engineering, University of Tabriz, Tabriz, Iran

Abstract

Accurate prediction of earthquake duration could control seismic design of structures. In this paper, a new simple method was developed to estimate such important parameter by employing artificial neural networks (ANN) capability. A generalized regression neural network (GRNN) as a special class of RBF networks was implemented in this study to reduce the computation steps required for the searching process on sparse data sets. This network with quick-design capability does not need to impose a prescribed form for mapping of the observed data. The independent variables used in the predictive model of this study were earthquake magnitude, distance measure and site conditions. The designed models were trained using the 950 accelerograms recorded at Iranian plateau. The performance of proposed approach was compared with predicted results of feed forward back propagation networks. Analyses show that the designed GRNN performs well in estimating earthquake record duration and could be applied for prediction of common measures of earthquake ground-motion duration.

Keywords

Main Subjects


References
1. Bommer, J.J. and Martinez-Pereira, A. The e ective
duration of earthquake strong motion", Journal of
Earthquake Engineering, 3, pp. 127-172 (1999).
2. Bommer, J.J., Sta ord, P.J., and Alarcon, J.A. Empirical
equations for the prediction of the signi cant,
bracketed, and uniform duration of earthquake ground
motion", Bull Seismol Soc Am, 99(6), pp. 3217-3233
(2009).
3. Kempton, J.J. and Stewart, P.J. Prediction equations
for signi cant duration of earthquake ground motions
consideration site and near- source e ects", Earthquake
Spectra, 22, pp. 958-1013 (2006).
4. Reinoso, E. and Ordaz, M. Duration of strong ground
motion during Mexican earthquakes in terms of magnitude,
distance to the rupture area and dominant site
period", Earthquake Eng. Struct. Dyn., 3, pp. 653-673
(2001).
5. Iervolino, I., Manfredi, G., and Cosenza, E. Ground
motion duration e ect on nonlinear seismic response",
Earthquake Eng. Struct. Dyn., 35, pp. 21-38 (2006).
6. Nurtug, A. and Sucuoglu, H. Prediction of seismic
energy dissipation in SDOF systems", Earthquake Eng.
Struct. Dyn., 24, pp. 1215-1222 (1995).
7. Hancock, J. and Bommer, J.J. Using spectral
matched records to explore the in
uence of strongmotion
duration on inelastic structural response", Soil
Dyn. Earthquake Eng., 27(4), pp. 291-299 (2007).
8. Trifunac, M.D. Empirical criteria for liquefaction in
sands via standard penetration tests and seismic wave
energy", Soil Dyn. Earthquake Eng., 14(4), pp. 419-
426 (1995).
9. Youd, T.L. and Idriss, I.M. Liquefaction resistance of
soils: Summary report from the 1996 NCEER and 1998
NCEER/NSF workshops on evaluation of liquefaction
resistance of soils", Journal of Geotechnical and Geoenvironmental
Engineering, 127, pp. 297-313 (2001).
10. Rauch, A.F. and Martin, J.R. EPOLLS model for
predicting average displacements on lateral spreads",
J. Geotech. Engrg, 126, pp. 360-371 (2000).
11. FEMA 1999, HAZUS99 Earthquake Loss Estimation
Methodology: user's manual. Federal Emergency Management
Agency, Washington DC (2003).
12. Whitman, R.V., Anagnos, T., Kircher, C., Lagorio,
H.J., Lawson, R.S., and Schneider, P. Development
of a national earthquake loss estimation methodology",
Earthquake Spectra, 13(4), pp. 643-661 (1997).
13. Yaghmaei-Sabegh, S., Shoghian, Z., and Sheikh, M.N.
A new model for the prediction of earthquake groundmotion
duration in Iran", Nat. Hazards, 70, pp. 69-92
(2014).
14. Asencio-Cortes, G., Martnez- Alvarez, F., Morales-
Esteban, A., and Reyes, J. A sensitivity study of
seismicity indicators in supervised learning to improve
earthquake prediction", Knowledge-based Systems,
101, pp. 15-30 (2016).
15. Florido, E., Martnez- Alvarez, F., Morales-Esteban,
A., Reyes, J., and Aznarte-Mellado, L. Detecting
precursory patterns to enhance earthquake prediction
in Chile", Computers & Geosciences, 76, pp. 112-120
(2015).
16. Yaghmaei-Sabegh, S. A novel approach for classi -
cation of earthquake ground-motion records", Journal
of Seismology, published online, 21(4), pp. 885-907
(2017).
17. Zhang, G.P. Time series forecasting using a hybrid
ARIMA and neural network model", Neurocomputing,
50, pp. 159-175 (2003).
18. McCulloch, W.S. and Pitts, W. A logical calculation
of the ideas immanent in nervous activity", Bull
Mathematical Biophysics, 5, pp. 115-133 (1943).
19. Alippi, C., Polycarpou, M., Panayiotou, C., and
Ellinas, G. Arti cial neural networks lecture notes
in computer science", 19th International Conference,
Limassol, Cyprus, September 14-17, Proceedings, Part
I (2009).
20. Dysart, P.S. and Pulli, J.J. Regional seismic event
classi cation at the NORESS array: Seismological
measurements and the use of trained neural networks",
Bull. Seismol Soc. Am., 80(6B), pp. 1910-1933 (1990).
21. Dai, H. and MacBeth, C. Application of backpropagation
neural networks to identi cation of seismic
arrival types", Physics of the Earth and Planetary
Interiors, 101(3), pp. 177-188 (1997).
22. Xu, B., Wu, Z., Chen, G., and Yokoyama, K. Direct
identi cation of structural parameters from dynamics
responses with neural networks", Engineering Applications
of Arti cial Intelligence, 17, pp. 931-943 (2004).
23. Chakraverty, S., Gupta, P., and Sharma, S. Neural
network-based simulation for response identi cation
of two-storey shear building subject to earthquake
motion", Neural Comput & Applic, 19, pp. 367-375
(2010).
24. Kuzniar, K., Maciag, E., and Waszczyszyn, Z. Computation
of response spectra from mining tremors using
neural networks", Soil Dyn. Earthquake Eng., 25, pp.
331-339 (2005).
25. Gentili, S. and Bragato, P. A neural-tree-based system
for automatic location of earthquakes in Northeastern
Italy", Journal of Seismology, 10, pp. 73-89
(2006).
26. Asencio-Cortes, G. Martnez- Alvarez, F., Troncoso,
A., and Morales-Esteban, A. Medium-large earthquake
magnitude prediction in Tokyo with arti cial
2438 S. Yaghmaei-Sabegh/Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 2425{2439
neural networks", Neural Comput. & Applic., 28(5),
pp. 1043-1055 (2017).
27. Kern, T. and Ting, S.B. Neural network estimation
of ground peak acceleration at stations along Taiwan
high-speed rail system", Engineering Applications of
Arti cial Intelligence, 18, pp. 857-866 (2015).
28. Ahmad, I., El Naggar, M.H., and Naeem Khan, A.
Neural network based attenuation of strong motion
peaks in Europe", Journal of earthquake Engineering,
12(5), pp. 663-680 (2008).
29. Arjun, C.R. and Kumar, A. Neural network estimation
of duration of strong ground motion using
Japanese earthquake records", Soil Dyn. Earthquake
Eng., 31, pp. 866-872 (2001).
30. Liu, Y., Ju, Y., Duan, C., and Zhao, X, Structural
diagnosis using neural network and feature fusion",
Engineering Applications of Arti cial Intelligence, 24,
pp. 87-92 (2011).
31. Alari , A.S.N., Alari , N.S.N., and Al-Humidan, S.
Earthquakes magnitude predication using arti cial
neural network in northern Red Sea area", Journal of
King Saud University-Science, 24, pp. 301-313 (2012).
32. Reyes, J., Morales-Esteban, A., and Martnez- Alvarez,
F. Neural networks to predict earthquakes in Chile",
Applied Soft Computing, 13(2), pp. 1314-1328 (2013).
33. Martnez- Alvarez, F., Reyes, J., Morales-Esteban, A.,
and Rubio-Escudero, C. Determining the best set of
seismicity indicators to predict earthquakes, Two case
studies: Chile and the Iberian Peninsula", Knowledge-
Based Systems, 50, pp. 198-210 (2013).
34. Morales-Esteban, A., Martnez- Alvarez, F., and Reyes,
J. Earthquake prediction in seismogenic areas of
the Iberian Peninsula based on computational intelligence",
Tectonophysics, 59, pp. 121-134 (2013).
35. Panakkat, A. and Adeli, H. Recurrent neural network
for approximate earthquake time and location prediction
using multiple sesimicity indicators", Computer-
Aided Civil and Infrastructure Engineering, 24, pp.
280-292 (2009).
36. Larran, P., Calvo, B., Santana, R., Bielza, C., Galdiano,
J., Inza, I., Lozano, J.A., Armananzas, R.,
Santafe, G., Perez, A., and Robles, V. Machine
learning in bioinformatics", Brief Bioinform, 7(1), pp.
86-112 (2006).
37. Zamani, A., Sorbi, M., and Safavi, A. Application
of neural network and ANFIS model for earthquake
occurrence in Iran", Earth Science Informatics, 6(2),
pp. 71-85 (2013).
38. Hagan, M.T., Demuth, H.B., and Beale M., Neural
Network Design, PWS Publishing Company (1996).
39. Rajasekaran, S. and Amalraj, R. Predictions of design
parameters in civil engineering problems using SLNN
with a single hidden RBF neuron", Computers and
Structures, 80(31), pp. 2495-505 (2002).
40. Yen, G.G. Identi cation and control of large structures
using neural networks", Computers and Structures,
52(5), pp. 859-870 (1994).
41. Sunar, M., Gurain, A.M.A., and Mohande, M. Substructural
neural network controller", Computers and
Structures, 78(4), pp. 575-581 (2000).
42. Zhang, A.H. and Zhang, L. RBF neural networks for
the prediction of building interference e ects", Computers
and Structures, 82(27), pp. 2333-2339 (2004).
43. Panakkat, A. and Adeli, H. Neural network models
for earthquake magnitude prediction using multiple
seismicity indicators", International Journal of Neural
Systems, 17(1), pp. 13-33 (2007).
44. Baddari, K., Afa, T., Djarfour, D., and Ferahtia, J.
Application of a radial basis function arti cial neural
network to seismic data inversion", Computers and
Geosciences, 35, pp. 2338-2344 (2009).
45. Tang, C. Radial basis function neural network models
for peak stress and strain in plain concrete under triaxial
stress", Journal of Materials in Civil Engineering,
22(9), pp. 923-934 (2010).
46. Specht, D.F. A general regression neural network",
IEEE Trans Neural Networks, 2(6), pp. 568-576
(1991).
47. Yaghmaei-Sabegh, S. and Tsang, H-H. A new site
classi cation approach based on neural networks", Soil
Dyn. Earthquake Eng., 31, pp. 974-981 (2011).
48. Wasserman, P.D. Advanced methods in neural network",
Van Nostrand Reinhold, pp. 147-158 (1993).
49. Kim, B., Kim, S., and Kim, K. Modelling of plasma
etching using a generalized regression neural network",
Vacuum, 71(4), pp. 497-503 (2003).
50. Tsoukalas, L.H. and Uhrig, R.E., Fuzzy and Neural
Approached in Engineering, New York: Wiley (1997).
51. Kurup, P.U. and Grin, E.P. Prediction of soil
composition from CPT data using general regression
neural network", J. Computing in Civil Eng., 20(4),
pp. 281-289 (2006).
52. Hanna, A.M., Ural, D., and Saygili, G. Neural network
model for liquefaction potential in soil deposits
using Turkey and Taiwan earthquake data", Soil Dyn.
Earthquake Eng., 27(6), pp. 521-540 (2007).
53. Yaghmaei-Sabegh, S. and Tsang, H-H. Site class
mapping based on earthquake ground motion data
recorded by regional seismographic network", Nat.
Hazards, 73, pp. 2067-2087 (2014).
54. Ambraseys, N.N. and Sarma, S.K. Response of earth
dams to strong earthquakes", Geotechnique, 17, pp.
181-213 (1967).
55. Page, R.A., Boore, D.M., Joyner, W.B., and Coulter
H.W. Ground motion values for use in seismic design
of the trans-Alaska pipeline system", US Geological
Survey Circular, 672 (1972).
56. Yaghmaei-Sabegh, S. and Lam, N.T.K. Ground motion
modelling in Tehran based on the stochastic
method", Soil Dyn. Earthquake Eng., 30(7), pp. 525-
535 (2010).
S. Yaghmaei-Sabegh/Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 2425{2439 2439
57. Building Seismic Safety Council (BSSC) NEHRP
recommended provisions for seismic regulations for
new buildings and other structures developed for
the federal emergency management agency", FEMA
302/303, Washington, D.C. (1997).
58. Guclu, D. and Dursum, S. Arti cial neural network
modelling of a large-scale wastewater treatment plant
operation", Bioprocess and Biosystems Engineering,
33(9), pp. 1051-1058 (2010).
59. Kolmogorov, A.N. On the representation of continuous
functions of several variables by superposition
of continuous functions of one variable and addition",
Doklady Akademii Nauk SSSR, 114, pp. 359-373
(1957).
60. Hecht-Nielsen, R. Kolmogorov's mapping neural network
existence theorem", in IEEE International Conference
on Neural Networks, pp. 11-14 (1987).
61. Rummelhart, D.E., Hilton, G.E., and Williams, R.J.
Learning internal representations by error propagation",
Parallel Distributed Processing, 1, chapter 8,
D.E. Rummelhart, and J.L. McCleland, Eds., Cambridge,
MA (MIT Press) (1986).