Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research Paper
  • Published:
Bulletin of Earthquake Engineering Aims and scope Submit manuscript

Abstract

Since the determination from experimental tests are expensive and time consuming, the site conditions in strong ground motion equations are mostly expressed by geologically qualitative descriptions of soils at the recording stations. The analytical solution for the site description has not been sufficiently studied due to highly nonlinear behavior of soil. Advances in field of artificial intelligence (AI) offer new insights to solve the problems in the most complex systems utilizing different algorithms and models. This paper primarily aims to predict average shear wave velocity (\(\text{ V}_\mathrm{S30}\)) as a soil property at the earthquake recording stations by applying AI methods, which are composed of artificial neural network (ANN) and genetic expression programming (GEP). The application is performed for the 60-accelerograph station sites located in California, USA. The predictor variables of \(\text{ V}_\mathrm{S30}\) in AI models, which are properly organized from strong ground motion data, are magnitude, site-to-source distance, peak ground acceleration and spectral accelerations at different site periods. \(\text{ V}_\mathrm{S30}\) values as output variable are collected from the surface wave testings conducted in the sites. The results indicates that for the considered highly nonlinear problem in this paper, the developed ANN and GEP models perform good predictions in terms of error and correlation. It can be concluded that the AI methods are relatively promising for prediction of \(\text{ V}_\mathrm{S30}\). The findings from this paper can be helpful to improve the site descriptions at the current database of the study region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Alavi AH, Gandomi AH, Modaresnezhad M, Mousavi M (2011) New ground-motion prediction equations using multi expression programing. J Earthq Eng 15:511–536

    Article  Google Scholar 

  • Ansal AM, Iyisan R, Güllü H (2001) Microtremor measurements for the microzonation of Dinar. Pure Appl Geophys 158(12):2525–2541

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Google Scholar 

  • Baykasoglu A, Güllü H, Canakci H, Özbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123

    Article  Google Scholar 

  • Berke L, Hajela P (1991) Application of neural networks in structural optimization. NATO/AGARD Adv Study Inst 23(I–II):731–745

    Google Scholar 

  • Bommer JJ, Douglas J, Strasser FO (2003) Style-of-faulting in ground-motion prediction equations. B Earthq Eng 1(2):171–203

    Article  Google Scholar 

  • 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

  • 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

  • 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

  • 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

    Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Cevik A (2007) Genetic programming based formulation of rotation capacity of wide flange beams. J Constr Steel Res 63:884–893

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Chang SW, Bray JD, Seed RB (1996) Engineering implications of ground motions from the Northridge earthquake. B Seismol Soc Am 86:270–288

    Google Scholar 

  • Chester DL (1990) Why two hidden layers are better than one? Int Joint Conf Neural Netw 1:265–268

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    Google Scholar 

  • Fine TL (1999) Feedforward neural network methodology. Springer, New York

    Google Scholar 

  • Fu L (1995) Neural networks in computer intelligence. McGraw-Hill, New York

    Google Scholar 

  • 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

    Google Scholar 

  • Ghaboussi J, Lin C-CJ (1998) New method of generating spectrum compatible accelerograms using neural networks. Earthq Eng Struct D 27(4):377–396

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Gulkan P, Kalkan E (2002) Attenuation modeling of recent earthquakes in Turkey. J Seismol 6:397–409

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge

    Google Scholar 

  • 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

  • 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

  • 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

  • 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

  • Jafarian Y, Kermani E, Baziar MH (2010) Empirical predictive model for the \(\text{ v}_{\text{ max}}/\text{ a}_{\text{ max}}\) ratio of strong ground motions using genetic programming. Comput Geosci 36:1523–1531

    Google Scholar 

  • 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

  • Kermani E, Jafarian Y, Baziar MH (2009) New predictive models for the \(\text{ v}_{\text{ max}}/\text{ a}_{\text{ max}}\) ratio of strong ground motions using genetic programming. Int J Civ Eng 7(4):236–247

    Google Scholar 

  • Kramer SL (1996) Geotechnical earthquake engineering. Prentice Hall, NJ

    Google Scholar 

  • Kudrycki TP (1988) Neural network implementation of a medical diagnosis expert system. MS thesis ,College of Engineering, University of Cincinnati

  • 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

    Google Scholar 

  • Lin C-CJ, Ghaboussi J (2001) Generating multiple spectrum compatible accelerograms using stochastic neural networks. Earthq Eng Struct D 30(7):1021–1042

    Article  Google Scholar 

  • Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Masters T (1993) Practical neural network recipes in C++. Academic Press, San Diego

    Google Scholar 

  • Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addisin-Wesley, Reading

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Pacific Earthquake Engineering Research Center (PEER)–NGA database. http://peer.berkeley.edu/nga/flatfile.html. Last accessed on 04 Nov 2011

  • 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

    Google Scholar 

  • Principe JC, Euliano NR, Lefebvre WC (2000) Neural and adaptive systems, fundamentals through simulations. Wiley, New York

    Google Scholar 

  • Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge

    Google Scholar 

  • Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol I. MIT Press, Cambridge

    Google Scholar 

  • Safak E (2001) Local site effects and dynamic soil behavior. Soil Dyn Earthq Eng 21:453–458

    Article  Google Scholar 

  • Salchenberger LM, Cinar EM, Lash NA (1992) Neural networks: a new tool for predicting thrift failures. Decis Sci 23:899–916

    Article  Google Scholar 

  • Scawthorn C, Johnson GS (2000) Preliminary report Kocaeli (Izmit) earthquake of 17 August 1999. Eng Struct 22:727–745

    Article  Google Scholar 

  • Seed HB, Ugas C, Lysmer J (1976) Site-dependent spectra for earthquake-resistant design. B Seismol Soc Am 66:221–243

    Google Scholar 

  • 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

  • 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

  • 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

    Article  Google Scholar 

  • Smith GN (1986) Probability and statistics in civil engineering: an introduction. Collins, London

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  • Tehranizadeh M, Safi M (2004) Application of artificial intelligence for construction of design spectra. Eng Struct 26:707–720

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

  • Vucetic MV, Dobry R (1991) Effect of soil plasticity on cyclic response. J Geotech Eng 117:89–107

    Article  Google Scholar 

  • Zurada JM (1992) Introduction to artificial neural systems. West, St. Paul

    Google Scholar 

Download references

Acknowledgments

This study is supported by The Scientific Research Project Unit of University of Gaziantep. The author would like to express his thanks to Kayen et al. (2005) (USGS Open File Report 2005-1366) and PEER Center for the data used in this paper. The anonymous reviewers are gratefully acknowledged for their constructive and critical reviews in improving the quality of the manuscript.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Gaziantep, 27310 , Gaziantep, Turkey

    Hamza Güllü

Authors
  1. Hamza Güllü
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamza Güllü.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Güllü, H. On the prediction of shear wave velocity at local site of strong ground motion stations: an application using artificial intelligence. Bull Earthquake Eng 11, 969–997 (2013). https://doi.org/10.1007/s10518-013-9425-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10518-013-9425-8

Keywords

Search

Navigation