On the prediction of shear wave velocity at local site of strong ground motion stations: An application using artificial intelligence

Bulletin of Earthquake Engineering

Volumn 11, Issue 4, 2013, Pages 969-997

  Güllü, Hamza ^a  

^a GAZIANTEP UNIVERSITY   (Turkey)

Author keywords

Genetic expression programming; Local site conditions; Neural network; Shear wave velocity; Strong ground motion

Indexed keywords

EARTHQUAKE RECORDING; GENETIC EXPRESSION PROGRAMMING; LOCAL SITE CONDITION; PEAK GROUND ACCELERATION; PREDICTOR VARIABLES; SHEAR WAVE VELOCITY; SPECTRAL ACCELERATION; STRONG GROUND MOTION;

ACOUSTIC WAVE VELOCITY; EQUATIONS OF MOTION; FORECASTING; GENETIC PROGRAMMING; NEURAL NETWORKS; SHEAR WAVES; WAVE PROPAGATION;

EARTHQUAKE EFFECTS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; DATABASE; EARTHQUAKE PREDICTION; GROUND MOTION; MAGNITUDE; PEAK ACCELERATION; S-WAVE; SOIL PROPERTY; SURFACE WAVE; WAVE VELOCITY;

CALIFORNIA; UNITED STATES;

EID: 84880618785     PISSN: 1570761X     EISSN: 15731456     Source Type: Journal    
DOI: 10.1007/s10518-013-9425-8     Document Type: Article

References (72)

1. Ahmad I, El Naggar MH, Naeem Khan A (2008) Neural network based attenuation of strong motion peaks in Europe. J Earthq Eng 12: 663-680.
2. Aki K (1988) Local site effect on ground motion. In: von Thun JL (ed) Earthquake engineering and soil dynamics. II: recent advances in ground-motion evaluation. Am Soc Civil Eng Geotech Spec Publ 20.
3. Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput Struct 89: 2176-2194.
4. Alavi AH, Gandomi AH, Modaresnezhad M, Mousavi M (2011) New ground-motion prediction equations using multi expression programing. J Earthq Eng 15: 511-536.
5. Ansal AM, Iyisan R, Güllü H (2001) Microtremor measurements for the microzonation of Dinar. Pure Appl Geophys 158(12): 2525-2541.
6. Baheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aided Civil Infrastruct Eng. 5(6): 445-63.
7. Bard PY (1997) Local effects on strong ground motion: basic physical phenomena and estimation methods for microzoning studies. In: Notes of the advanced study course SERINA (seismic risk: an integrated seismological, geotechnical and structural approach), Thessaloniki, Greece.
8. Bard PY, Chavez-Garciaz FJ (1993) On the decoupling of surficial sediments from surrounding geology at Mexico-City. B Seismol Soc Am 83(6): 1979-1991.
9. Baykasoglu A, Güllü H, Canakci H, Özbaki{dotless}r L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35: 111-123.
10. Berke L, Hajela P (1991) Application of neural networks in structural optimization. NATO/AGARD Adv Study Inst 23(I-II): 731-745.
11. Bommer JJ, Douglas J, Strasser FO (2003) Style-of-faulting in ground-motion prediction equations. B Earthq Eng 1(2): 171-203.
12. Boore DM, Atkinson GM (2007) Boore-Atkinson NGA ground motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters. Report No: PEER 2007/01. Pacific Earthquake Engineering Research Center, College of Engineering, University of California, Berkeley, p 110.
13. Boore DM, Joyner WB, Fumal TE (1993) Estimation of response spectra and peak accelerations from western North American earthquakes: an interim report. Open-File Report 93-509. US Geological Survey, p 70.
14. Boore DM, Joyner WB, Fumal TE (1994) Estimation of response spectra and peak accelerations from western North American earthquakes: an interim report. Part 2. Open-File Report 94-127. US Geological Survey.
15. Borcherdt RD, Glassmoyer G (1992) On the characteristics of local geology and their influence on ground motions generated by the Loma-Prieata earthquake in the San-Francisco bay region, California. B Seismol Soc Am 82(2): 603-641.
16. Building Seismic Safety Council (BSSC) (2003) NEHRP (National Earthquake Hazards Reduction Program) Recommended provisions for seismic regulations for new buildings and other Structures (FEMA 450), part 1: provisions. Federal Emergency Management Agency, National Institute of Building Sciences, Washington, DC. http://www. nehrp. gov/pdf/fema450provisions. pdf. Last accessed on 18 Nov 2011.
17. Canakci H, Baykasoglu A, Güllü H (2009) Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput Appl 18: 1031-1041.
18. Cevik A (2007) Genetic programming based formulation of rotation capacity of wide flange beams. J Constr Steel Res 63: 884-893.
19. Cevik A, Guzelbey IH (2007) A soft computing based approach for the prediction of ultimate strength of metal plates in compression. Eng Struct 29: 383-394.
20. Chang SW, Bray JD, Seed RB (1996) Engineering implications of ground motions from the Northridge earthquake. B Seismol Soc Am 86: 270-288.
21. Chester DL (1990) Why two hidden layers are better than one? Int Joint Conf Neural Netw 1: 265-268.
22. Douglas J (2003) Earthquake ground motion estimation using strong motion records: a review of equations for the estimation of peak ground acceleration and response spectral ordinates. Earth Sci Rev 61: 43-104.
23. Durukal E, Erdik M, Avci J, Yuzugulu O, Alpay Y, Avar B, Zulfikar C, Biro T, Mert A (1998) Analysis of the strong motion data of the 1995 Dinar, Turkey earthquake. Soil Dyn Earthq Eng 17: 557-578.
24. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2): 87-129.
25. Fine TL (1999) Feedforward neural network methodology. Springer, New York.
26. Fu L (1995) Neural networks in computer intelligence. McGraw-Hill, New York.
27. Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion prediction equations. Eng Appl Artif Intell 24: 717-732.
28. Ghaboussi J, Lin C-CJ (1998) New method of generating spectrum compatible accelerograms using neural networks. Earthq Eng Struct D 27(4): 377-396.
29. Güllü H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141-142: 92-113.
30. Gulkan P, Kalkan E (2002) Attenuation modeling of recent earthquakes in Turkey. J Seismol 6: 397-409.
31. Güllü H, Erçelebi E (2007) A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 93: 65-81.
32. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge.
33. Hayashi K, Kayen R (2003) Comparative test of three surface wave methods at Williams Street Park in San Jose, USA. Paper S051-009. In: Joint meeting of Japan Earth and Planetary Science, University of Tokyo, Tokyo, Japan, May 26-29.
34. Hecht-Nielsen R (1987) Kolmogorov's mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego CA, USA, pp 11-4.
35. Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, Dayton, OH, USA, pp 277-80.
36. Idriss IM (1990) Response of soft soil sites during earthquakes. In: Duncan JM (ed) Proceedings of the seed memorial symposium on H. Bolton, vol 3. BiTech, Vancouver, BC, pp 2265-2271.
37. max ratio of strong ground motions using genetic programming. Comput Geosci 36: 1523-1531.
38. Kayen R, Thompson E, Minasian D, Carkin B (2005) Shear-wave velocity of the ground near sixty California strong motion recording sites by the spectral analysis of surface waves (SASW) method and harmonic-wave sources. US Geological Survey, Open-File Report 2005-1366. http://pubs. usgs. gov/of/2005/1366/. Last accessed on 02 Nov 2011.
39. max ratio of strong ground motions using genetic programming. Int J Civ Eng 7(4): 236-247.
40. Kramer SL (1996) Geotechnical earthquake engineering. Prentice Hall, NJ.
41. Kudrycki TP (1988) Neural network implementation of a medical diagnosis expert system. MS thesis, College of Engineering, University of Cincinnati.
42. Lermo J, Chavez-Garcia FJ (1994) Site effect evaluation at Mexico City: dominant period and relative amplification from strong motion and microtremor records. Soil Dyn Earthq Eng 13: 413-423.
43. Lin C-CJ, Ghaboussi J (2001) Generating multiple spectrum compatible accelerograms using stochastic neural networks. Earthq Eng Struct D 30(7): 1021-1042.
44. Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2): 211-226.
45. Maier HR, Dandy GC (1998) The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ Model Softw 15: 193-209.
46. Masters T (1993) Practical neural network recipes in C++. Academic Press, San Diego.
47. Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addisin-Wesley, Reading.
48. Ozbakir L, Baykasoglu A, Kulluk S (2010) A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner. Appl Soft Comput 10: 304-317.
49. Pacific Earthquake Engineering Research Center (PEER)-NGA database. http://peer. berkeley. edu/nga/flatfile. html. Last accessed on 04 Nov 2011.
50. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2001) Numerical recipes in Fortran 77: the art of scientific computing, 2nd edn. Cambridge University Press, Cambridge.
51. Principe JC, Euliano NR, Lefebvre WC (2000) Neural and adaptive systems, fundamentals through simulations. Wiley, New York.
52. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge.
53. Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol I. MIT Press, Cambridge.
54. Safak E (2001) Local site effects and dynamic soil behavior. Soil Dyn Earthq Eng 21: 453-458.
55. Salchenberger LM, Cinar EM, Lash NA (1992) Neural networks: a new tool for predicting thrift failures. Decis Sci 23: 899-916.
56. Scawthorn C, Johnson GS (2000) Preliminary report Kocaeli (Izmit) earthquake of 17 August 1999. Eng Struct 22: 727-745.
57. Seed HB, Ugas C, Lysmer J (1976) Site-dependent spectra for earthquake-resistant design. B Seismol Soc Am 66: 221-243.
58. Seed HB, Romo MP, Sun J, Jaime A, Lysmer J (1987) Relationships between soil conditions and earthquake ground motions in Mexico City in the earthquake September 19, 1985. Report UCB/EERC-87/15. Earthquake Engineering Research Center, University of California, Berkeley, p 112.
59. Seed RB, Dickenson SE, Riemer MF, Bray JD, Sitar N, Mitchell JK, Idriss IM, Kayen RE, Kropp A, Harder LF, Power MS (1990) Preliminary report on the principal geotechnical aspects of the October 17, 1989 Loma Prieta earthquake. Report No. UCB/EERC-90/05. Earthquake Engineering Research Center.
60. Singh SK, Lermo JF, Dominguez T, Ordaz M, Espinosa JM, Mena E, Quaas R (1988) The Mexico earthquake of September 19, 1985-a study of amplification of seismic waves in the valley of Mexico with respect to a hill zone site. Earthq Spectra 4: 653-673.
61. Smith GN (1986) Probability and statistics in civil engineering: an introduction. Collins, London.
62. Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43: 224-235.
63. Stokoe K, Nazarian S (1985) Use of Rayleigh waves in liquefaction studies. In: Woods RD (ed) Measurement and use of shear-wave Velocity for evaluating dynamic soil properties. ASCE, NY, pp 1-17.
64. Stone WC, Yokel FY, Celebi M, Hanks T, Leyendecker EV (1987) Engineering aspects of the September 19, 1985 Mexico Earthquake. NBS Building Service Series 165. National Bureau of Standards, Washington, DC, p 207.
65. Sun CG, Kim DS, Chung CK (2005) Geologic site conditions and site coefficients for estimating earthquake ground motions in the inland areas of Korea. Eng Geol 81: 446-469.
66. Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York.
67. Tehranizadeh M, Safi M (2004) Application of artificial intelligence for construction of design spectra. Eng Struct 26: 707-720.
68. Tokimatsu K, Arai H, Asaka Y (1998) Two-dimensional shear wave structure and ground motion characteristics in Kobe based on microtremor measurements. Geotech Earthq Eng Soil Dyn III(1): 703-713.
69. Tsai Y-B, Huang M-W (2000) Strong ground motion characteristics of the Chi-Chi, Taiwan earthquake of September 21, 1999. Earthq Eng Eng Seismol 2: 1-21.
70. Turkish Seismic Code (TSC) (2007) Specification for structures to be built in disaster areas. http://www. koeri. boun. edu. tr/depremmuh/eski/FINAL999. pdf. Last accessed on Nov 2011.
71. Vucetic MV, Dobry R (1991) Effect of soil plasticity on cyclic response. J Geotech Eng 117: 89-107.
72. Zurada JM (1992) Introduction to artificial neural systems. West, St. Paul.