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

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.

[1]  Gayle S. Johnson,et al.  Preliminary report: Kocaeli (Izmit) earthquake of 17 August 1999 , 2000 .

[2]  Mario Ordaz,et al.  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 , 1988 .

[3]  W. B. Joyner,et al.  ESTIMATION OF RESPONSE SPECTRA AND PEAK ACCELERATIONS FROM WESTERN NORTH AMERICAN EARTHQUAKES: AN INTERIM REPORT PART 2 , 1993 .

[4]  Atilla Ansal,et al.  Microtremor Measurements for the Microzonation of Dinar , 2001 .

[5]  R. Dobry,et al.  Effect of Soil Plasticity on Cyclic Response , 1991 .

[6]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[7]  LiMin Fu,et al.  Neural networks in computer intelligence , 1994 .

[8]  Li Si-ming.  The Application of Neural Networks in Structural Optimization , 2003 .

[9]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[10]  M. Tehranizadeh,et al.  APPLICATION OF ARTIFICIAL INTELLIGENCE FOR CONSTRUCTION OF DESIGN SPECTRA , 2004 .

[11]  Mexico earthquake. , 1986, International nursing review.

[12]  Amir Hossein Gandomi,et al.  A hybrid computational approach to derive new ground-motion prediction equations , 2011, Eng. Appl. Artif. Intell..

[13]  Roger D. Borcherdt,et al.  On the characteristics of local geology and their influence on ground motions generated by the Loma Prieta earthquake in the San Francisco Bay region, California , 1992 .

[14]  Carl G. Looney,et al.  Advances in Feedforward Neural Networks: Demystifying Knowledge Acquiring Black Boxes , 1996, IEEE Trans. Knowl. Data Eng..

[15]  Amir Hossein Alavi,et al.  Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing , 2011 .

[16]  Hamza Güllü,et al.  Prediction of peak ground acceleration by genetic expression programming and regression: A comparison using likelihood-based measure , 2012 .

[17]  Francisco J. Chávez-García,et al.  Site effect evaluation at Mexico City: Dominant period and relative amplification from strong motion and microtremor records , 1994 .

[18]  Imad A. Basheer,et al.  Selection of Methodology for Neural Network Modeling of Constitutive Hystereses Behavior of Soils , 2000 .

[19]  Polat Gülkan,et al.  Attenuation modeling of recent earthquakes in Turkey , 2002 .

[20]  G. N. Smith.  Probability and statistics in civil engineering: An introduction , 1986 .

[21]  K. Tokimatsu,et al.  Two-Dimensional Shear Wave Structure and Ground Motion Characteristics in Kobe Based on Microtremor Measurements , 1998 .

[22]  D. R. Hush,et al.  Classification with neural networks: a performance analysis , 1989, IEEE 1989 International Conference on Systems Engineering.

[23]  Jamshid Ghaboussi,et al.  Generating multiple spectrum compatible accelerograms using stochastic neural networks , 2001 .

[24]  BaykasoğluAdil,et al.  Prediction of compressive and tensile strength of limestone via genetic programming , 2008 .

[25]  E. Şafak.  Local site effects and dynamic soil behavior , 2001 .

[26]  Abdulkadir Cevik,et al.  Genetic programming based formulation of rotation capacity of wide flange beams , 2007 .

[27]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[28]  William H. Press,et al.  Numerical recipes in Fortran 77 : the art of scientificcomputing. , 1992 .

[29]  Terrence L. Fine,et al.  Feedforward Neural Network Methodology , 1999, Information Science and Statistics.

[30]  Jonathan D. Bray,et al.  Engineering implications of ground motions from the Northridge earthquake , 1996, Bulletin of the Seismological Society of America.

[31]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[32]  Yaser Jafarian,et al.  Empirical predictive model for the vmax/amax ratio of strong ground motions using genetic programming , 2010, Comput. Geosci..

[33]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.

[34]  J. Douglas.  Earthquake ground motion estimation using strong-motion records: a review of equations for the estimation of peak ground acceleration and response spectral ordinates , 2003 .

[35]  Adil Baykasoglu,et al.  Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming , 2009, Neural Computing and Applications.

[36]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[37]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[38]  Ibrahim H. Guzelbey,et al.  A soft computing based approach for the prediction of ultimate strength of metal plates in compression , 2007 .

[39]  K. Campbell.  Campbell-Bozorgnia NGA Ground Motion Relations for the Geometric Mean Horizontal Component of Peak and Spectral Ground Motion Parameters , 2007 .

[40]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[41]  Mehmet Celebi,et al.  Engineering Aspects of the September 19, 1985 Mexico Earthquake , 1987 .

[42]  Mohammad Hassan Baziar,et al.  New Predictive Models for the v max/a max Ratio of Strong Ground Motions using Genetic Programming , 2009 .

[43]  Amir Hossein Alavi,et al.  New Ground-Motion Prediction Equations Using Multi Expression Programing , 2011 .

[44]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[45]  Pierre-Yves Bard,et al.  On the decoupling of surficial sediments from surrounding geology at Mexico City , 1993 .

[46]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[47]  R. D. Woods.  Measurement and use of shear wave velocity for evaluating dynamic soil properties. Proceedings Geotechnical Engineering Division of the ASCE, Denver, May, 1985. , 1985 .

[48]  Lale Özbakir,et al.  A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner , 2010, Appl. Soft Comput..

[49]  Holger R. Maier,et al.  The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study , 1998 .

[50]  J. Lawrence Von Thun,et al.  Earthquake engineering and soil dynamics II : recent advances in ground-motion evaluation : proceedings of the specialty conference , 1988 .

[51]  H. Bolton Seed,et al.  Site-dependent spectra for earthquake-resistant design , 1976, Bulletin of the Seismological Society of America.

[52]  I. Ahmad,et al.  Neural Network Based Attenuation of Strong Motion Peaks in Europe , 2008 .

[53]  W. T. Illingworth,et al.  Practical guide to neural nets , 1991 .

[54]  Hamza Güllü,et al.  A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey , 2007 .

[55]  I. Towhata.  Geotechnical Earthquake Engineering , 2008 .

[56]  Kenneth H. Stokoe,et al.  Use of Rayleigh Waves in Liquefaction Studies , 1985 .

[57]  Jamshid Ghaboussi,et al.  New method of generating spectrum compatible accelerograms using neural networks , 1998 .

[58]  J. Bommer,et al.  Style-of-Faulting in Ground-Motion Prediction Equations , 2003 .

[59]  Candan Gokceoglu,et al.  Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation , 2006 .

[60]  Robert E. Kayen,et al.  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 , 2005 .

[61]  Eser Durukal,et al.  Analysis of the strong motion data of the 1995 Dinar, Turkey earthquake , 1998 .

[62]  Chang-Guk Sun,et al.  Geologic site conditions and site coefficients for estimating earthquake ground motions in the inland areas of Korea , 2005 .

[63]  Lale Özbakir,et al.  Prediction of compressive and tensile strength of limestone via genetic programming , 2008, Expert Syst. Appl..