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Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties

  • Research Article - Civil Engineering
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Abstract

Shear wave velocity (V S) is a basic engineering soil property implemented in evaluating the soil shear modulus. In many instances it may be preferable to determine V S indirectly by common in-situ tests, for instance the standard penetration test. In this paper, the relation between V S and geotechnical soil parameters such as standard penetration test blow counts (N160), effective stress and fines content, as well as overburden stress ratio \({(\sigma_{\rm vo}/ \sigma_{\rm vo}^\prime)}\) is investigated. A new polynomial model is proposed to correlate geotechnical parameters and V S, predicated on a total of 620 data sets, including field investigation records for the Kocaeli (Turkey, 1999) and Chi-Chi (Taiwan, 1999) earthquakes. This study addresses the question of whether group method of data handling (GMDH) type neural networks (NN) optimized using genetic algorithms could be used to (1) estimate V S based on specified geotechnical variables, (2) assess the influence of each variable on V S. Results suggest that GMDH-type NN, in comparison to previous statistical relations, provides an effective means of efficiently recognizing the patterns in data and accurately predicting the shear wave velocity. The sensitivity analysis reveals that \({{\sigma_{\rm vo}/ \sigma_{\rm vo}^\prime}}\) and fines content have significant influence on predicting V S.

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References

  1. Kramer, S.L.: Geotechnical Earthquake Engineering. Prentice Hall publishing, Upper Saddle Hill River, 653 p. (1996)

  2. Andrus R.D., Piratheepanthan P., Ellis B.S., Zhang J., Juang C.H.: Comparing liquefaction evaluation methods sing penetration—VS relationships. Soil Dyn. Earthq. Eng. 24, 713–721 (2004)

    Article  Google Scholar 

  3. Ardalan H., Eslami A., Nariman-Zadeh N.: Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput. Geotech. 36, 616–625 (2009)

    Article  Google Scholar 

  4. Kalantary F., Ardalan H., Nariman-Zadeh N.: An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms. Eng. Geol. 104, 144–155 (2009)

    Article  Google Scholar 

  5. Imai, T.; Yoshimura, Y.: The relation of mechanical properties of soils to P and S-wave velocities for ground in Japan Technical Note OYO Corporation, In: 4th Japan Earthquake Engineering Symposium, pp. 89–96 (in Japanese) (1975)

  6. Sykora, D.E.; Stokoe, K.H.: Correlations of in-situ measurements in sands of shear wave velocity. Soil Dyn. Earthq. Eng. 20, 125–36 (1983)

    Google Scholar 

  7. Jafari M.K., Shafiee A., Ramzkhah A.: Dynamic properties of the fine grained soils in south of Tehran. J. Seismol. Earthq. Eng. 4(1), 25–35 (2002)

    Google Scholar 

  8. Hasancebi, N.; Ulusay, R.: Empirical correlations between shear wave velocity and penetration resistance for ground shaking assessments. Bull. Eng. Geol. Environ. 66, 203–13 (2007)

    Google Scholar 

  9. Ulugergerli, U.E.; Uyanık, O.: Statistical correlations between seismic wave velocities and SPT blow counts and the relative density of soils. J. Test. Eval. 35, 1–5 (2007)

    Google Scholar 

  10. Dikmen, U.: Statistical correlations of shear wave velocity and penetration resistance for soils. J. Geophys. Eng. 6, 61–72 (2009)

    Article  Google Scholar 

  11. Pitilakis K., Raptakis D., Lontzetidis K.T., Vassilikou T., Jongmans D.: Geotechnical and geophysical description of Euro- Seistests, using field and laboratory tests, and moderate strong ground motions. J. Earthq. Eng. 3, 381–409 (1999)

    Google Scholar 

  12. Kiku, H.; Yoshida, N.; Yasuda, S.; Irisawa, T.; Nakazawa, H.; Shimizu, Y.; Ansal, A.; Erkan, A.: In-situ penetration tests and soi profiling in Adapazar  Turkey Proc. In: ICSMGE/TC4 Satellite Conference on Lessons Learned from Recent Strong Earthquakes, pp. 259–265 (2001)

  13. Tamura, I.; Yamazaki, F.: Estimation of S-wave velocity based on geological survey data for K-NET and Yokohama. J. Struct. Mech. Earthq. Eng. I, 237–248 (in Japanese) (2002)

  14. Ohta Y., Goto N.: Empirical shear wave velocity equations in terms of characteristic soil indexes. Earthq. Eng. Struct. Dyn. 6, 167–187 (1978)

    Article  Google Scholar 

  15. Mayne P.W., Martin G.K.: Commentary on Marchetti flat dilatometer correlations in soils. ASTM Geotech. Test. J. 21(3), 222–239 (1998)

    Article  Google Scholar 

  16. Mayne, P.W.: Stress-strain-strength-flow parameters from enhanced in-situ tests, Proceedings. In: International Conference on In-Situ Measurement of Soil Properties and Case Histories (In-Situ 2001), Bali, Indonesia, pp. 47–69 (2001)

  17. Hanna A., Ural D., Saygili G.: Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn. Earthq. Eng. 27, 521–540 (2007)

    Article  Google Scholar 

  18. Cetin, K.O.; Youd, T.L.; Seed, R.B.; Bray, J.D.; Stewart, J.P.; Durgunoglu, H.T.; Lettis, W.; Yilmaz, M.T.: Liquefaction-induced lateral spreading at izmit bay during the Kocaeli (Izmit)-Turkey Earthquake. J. Geotech. Geoenviron. Eng. 130, 12, December 1, 2004. © ASCE, ISSN 1090-0241

  19. Risk Management Solutions Inc. Event report: Chi-Chi, Taiwan earthquake. CA, USA (2000)

  20. Chua D.B., Stewarta J.P., Leeb S., Lind P.S., Chud B.L., Seed R.B., Hsuf S.C., Yug M.S., Wang C.H.: Documentation of soil conditions at liquefaction and non-liquefaction sites from 1999 Chi-Chi (Taiwan) earthquake. Soil Dyn. Earthq. Eng. 24, 647–657 (2004)

    Article  Google Scholar 

  21. Scawthorn, C.: Earthquakes of 1999. In: Euro conference on global change and catastrophe risk management: earthquake risks in Europe, Laxenburg, Austria (2000)

  22. Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man. Cybern.; SMC-1:364–378 (1971)

    Google Scholar 

  23. Farlow, S.J.: Self-Organizing Method in Modelling: GMDH Type Algorithm. Marcel Dekker Inc., New York (1984)

  24. Nariman-Zadeh N., Darvizeh A., Jamali A., Moeini A.: Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J. Mater. Process. Technol. 164-165, 1561–1571 (2005)

    Article  Google Scholar 

  25. Nariman-Zadeh N., Darvizeh A., Ahmad-Zadeh G.R.: Hybrid genetic design of GMDH type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. J. Eng. Manuf. 217, 779–790 (2003)

    Article  Google Scholar 

  26. Dimopoulos I., Chronopoulos J., Chronopoulos-Sereli A., Lek S.: Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecol. Model. 120, 157–165 (1999)

    Article  Google Scholar 

  27. Castillo D.E., Gutierrez J. M., Hadi A.L.: Sensitivity analysis in discrete Bayesian networks. IEEE Trans. Syst. Man. Cybern. 26, 412–423 (1996)

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Babol University of Technology, Babol, Iran

    H. Mola-Abasi

  2. Department of Civil and Environmental Engineering, Amirkabir University Technology (Tehran Polytechnic), Tehran, Iran

    A. Eslami & P. Tabatabaie Shourijeh

Authors
  1. H. Mola-Abasi
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  2. A. Eslami
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  3. P. Tabatabaie Shourijeh
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Correspondence to H. Mola-Abasi.

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Mola-Abasi, H., Eslami, A. & Shourijeh, P.T. Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties. Arab J Sci Eng 38, 829–838 (2013). https://doi.org/10.1007/s13369-012-0525-6

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  • DOI: https://doi.org/10.1007/s13369-012-0525-6

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