Predicting shear wave velocity of soil using multiple linear regression analysis and artificial neural networks

Document Type : Article


1 Dept. of geology, Ferdowsi University of Mashhad, Mashhad, Iran

2 Dept. of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran


In this paper, the correlation between shear wave velocity and some of the index parameters of soils including standard penetration test blow counts (SPT), fine-content (FC), soil moisture (W), liquid limit (LL) and depth (D) is investigated. The study attempts to show the application of artificial neural networks and multiple regression analysis in the prediction of the shear wave velocity (VS) value of soils.
 New predicting equations are suggested to correlate VS and mentioned parameters based on a dataset collected from Mashhad city in the north east of Iran. The results suggest that better and more exact correlations in the estimation of VS are acquired when ANN method is used. The predicted values using ANN method are checked against the real values of VS to evaluate the performance of this method. The minimum correlation coefficient obtained in ANN method is higher than the maximum correlation coefficient obtained from the MLR. In addition, the value of estimation error in the ANN method is much less than the MLR method indicating the higher confidence coefficient of the ANN in estimating the VS of soil.


Main Subjects


1. Akin, M.K., Kramer, S.L., and Topal, T. Empirical
correlations of shear wave velocity (VS) and
penetration resistance (SPT-N) for di erent soils in
an earthquake-prone area (Erbaa-Turkey)", EngGeol.,
119, pp. 1-17 (2011).
2. Fabbrocino, S., Lanzano, G., Forte, G., Santucci de
Magistris, F., and Fabbroccini, G. SPT blow count
vs. shear wave velocity relationship in the structurally
complex formations of the Molise Region (Italy)",
Engineering Geology, 187, pp. 84-97 (2015).
3. Sil, A. and Sitharam, T.G. Dynamic site characterization
and correlation of shear wave velocity with
standard penetration test 'N' values for the city of
Agartala, Tripura state, India", Pure and Applied
Geophysics, 171(8), pp. 1859-1876 (2014).
4. Chatterjee, K. and Choudhury, D. Variations in shear
wave velocity and soil site class in Kolkata city using
regression and sensitivity analysis", Nat. Hazards, 69,
pp. 2057-2082 (2013).
O. Ataee et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 1943{1955 1953
5. Hasancebi, N. and Ulusay, R. Empirical correlations
between shear wave velocity and penetration resistance
for ground shaking assessments", Bulletin of Engineering
Geology and the Environment, 66, pp. 203-213
6. Dikmen, U. Statistical correlations of shear wave
velocity and penetration resistance for soils", Journal
of Geophysics and Engineering, 6, pp. 61-72 (2009).
7. Maheswari, R.U., Boominathan, A., and Dodagoudar,
G.R. Use of surface waves in statistical correlations
of shear wave velocity and penetration resistance of
Chennai soils", Geotechnical and Geological Engineering,
28, pp. 119-137 (2010).
8. Ghazi, A., Hafezi Moghadas, N., Sadeghi, H.,
Ghafoori, M., and Lashkaripour, G.L. Empirical
relationships of shear wave velocity, SPT-N value and
vertical e ective stress for di erent soils in Mashhad,
Iran", Annals of Geophysics, 58(3), S0325 (2015).
9. Brandenberg, S.J., Bellana, N., and Shantz, T. Shear
wave velocity as a statistical function of standard penetration
test resistance and vertical e ective stress at
Caltrans bridge sites", Soil Dynamics and Earthquake
Engineering, 30, pp. 1026-1035 (2010).
10. Shooshpasha, I., Mola-Abasi, H., Jamalian, A., Dikmen,
U., and Salahi, M. Validation and application
of empirical shear wave velocity models based on
standard penetration test", Computational Methods in
Civil Engineering, 4(1), pp. 25-41 (2013).
11. Imai, T. P- and S-wave velocities of the ground
in Japan", In Proceedings of the IX, International
Conference on Soil Mechanics and Foundation Engineering,
pp. 127-132 (1977).
12. Imai, T. and Yoshimura, Y. Elastic wave velocity and
soil properties in soft soil (in Japanese)", Tsuchito-
Kiso., 18(1), pp. 17-22 (1970).
13. Jafari, M.K., Sha ee, A., and Razmkhah, A. Dynamic
properties of ne grained soils in south of Tehran", Soil
Dynamics and Earthquake Engineering, 4, pp. 25-35
14. Kiku, H., Yoshida, N., Yasuda, S., Irisawa, T.,
Nakazawa. H., Shimizu. Y., Ansal, A., and Erkan, A.
In situ penetration tests and soil pro ling in Adapazari,
Turkey", In Proceedings of the ICSMGE/TC4
Satellite Conference on Lessons Learned from Recent
Strong Earthquakes, pp. 259-265 (2001).
15. Lee, SHH. Regression models of shear wave velocities",
Journal of the Chinese Institute of Engineers,
13, pp. 519-532 (1990).
16. Ohsaki, Y. and Iwasaki, R. On dynamic shear moduli
and Poisson's ratio of soil deposits", Soils and Foundations,
13(4), pp. 61-73 (1973).
17. Ohta, Y. and Goto, N. Empirical shear wave velocity
equations in terms of characteristics soil indexes",
Earthquake Engineering and Structural Dynamics, 6,
pp. 167-187 (1978).
18. Pitilakis, K.D., Anastasiadis, A., and Raptakis, D.
Field and laboratory determination of dynamic properties
of natural soil deposits", In Proceedings of the
10th World Conference on Earthquake Engineering,
Rotherdam, pp. 1275-1280 (1992).
19. Seed, H.B. and Idriss, I.M. Evaluation of liquefaction
potential sand deposits based on observation of performance
in previous earthquakes", Preprint 81-544,
In Situ Testing to Evaluate Liquefaction Susceptibility,
ASCE National Convention, Missouri, pp. 81-544
20. Sykora, D.E. and Stokoe, K.H. Correlations of in-situ
measurements in sands of shear wave velocity", Soil
Dynamics and Earthquake Engineering, 20, pp. 125-
136 (1983).
21. Dehghan Nayeri, G., Dehghan Nayeri, D., and
Barkhordari, K. A new statistical correlation between
shear wave velocity and penetration resistance of soils
using genetic programming", Electronic Journal of
Geotechnical Engineering, 18, pp. 2071-2078 (2013).
22. Sivrikaya, O. Comparison of arti cial neural networks
models with correlative works on undrained shear
strength", Eurasian Soil Science, 42(13), pp. 1487-
1496, Pleiades Publishing, Ltd (2009).
23. Dehghan, S., Sattari, Gh., Chehreh chelghani, S., and
Aliabadi, M.A. Prediction of uniaxial compressive
strength and modulus of elasticity for Travertine samples
using regression and arti cial neural networks",
Mining Science and Technology, 20, pp. 0041-0046
24. Sarmadian, F. and Keshavarzi, A. Developing pedotransfer
functions for estimating some soil properties
using arti cial neural network and multivariate regression
approaches", International Journal of Environmental
and Earth Sciences, 1(1), pp. 31-37 (2010).
25. Schaap, M.G., Leij, F.J., and Van Genuchten, M.Th.
Neural network analysis for hierarchical prediction
of soil hydraulic properties", Soil Science Society of
America Journal, 62, pp. 847-855 (1998).
26. Maleki, S., Moradzadeh, A., Ghavami Riabi, R.,
Gholami, R., and Sadeghzadeh, F. Prediction of shear
wave velocity using empirical correlations and arti cial
intelligence methods", NRIAG Journal of Astronomy
and Geophysics, 3, pp. 70-81 (2014).
27. Mohammadi, H. and Rahmannejad, R. The estimation
of rock mass deformation modulus using regression
and arti cial neural network analysis", Arabian
Journal for Science and Engineering, 35(1A), pp. 67-
77 (2009).
28. Gunaydm, O. Estimation of soil compaction parameters
by using statistical analyses and arti cial neural
networks", Environ. Geol., 57, pp. 203-215 (2009).
29. Sudha Rani, Ch. Arti cial neural networks (ANNs)
for prediction of engineering properties of soils", International
Journal of Innovative Technology and Exploring
Engineering (IJITEE), 3(1), pp. 123-130 (2013).
1954 O. Ataee et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 1943{1955
30. Khanlari, G.R., Heidari, M., Momeni, A.A., and
Abdilor, Y. Prediction of shear strength parameters of
soils using arti cial neural networks and multivariate
regression methods", Engineering Geology, 131-132,
pp. 11-18 (2012).
31. Mola-Abasi, H. and Shooshpasha, I. Prediction of
zeolite-cement-sand uncon ned compressive strength
using polynomial neural network", The European
Physical Journal Plus, 131(4), pp. 1-12 (2016).
32. Park, H.I. Development of neural network model to
estimate the permeability coecient of soils", Marine
Geosources and Geotechnology, 29(4), pp. 267-278
33. Harini, H.N. and Naagesh, S. Predicting CBR of
ne grained soils by arti cial neural network and
multiple linear regression", International Journal of
Civil Engineering and Technology (IJCIET), 5(2), pp.
119-126 (2014).
34. Moayed, R.Z., Kordnaeij, A., and Mola-Abasi, H.
Compressibility indices of saturated clays by group
method of data handling and genetic algorithms",
Neural Computing and Applications, pp. 1-14 (2016).
35. Mola-Abasi, H. and Shooshpasha, I. Prediction of
compression index of saturated clays (Cc) using polynomial
models", Scientia Iranica, 23(2), pp. 500-507
36. Kordnaeij, A., Kalantary, F., Kordtabar, B., and
Mola-Abasi, H. Prediction of recompression index
using GMDH-type neural network based on geotechnical
soil properties", Soils and Foundations, 55(6),
pp. 1335-1345 (2015).
37. Das, S.K. and Basudhar, P.K. Undrained lateral load
capacity of piles in clay using arti cial neural network",
Computers and Geotechnics, 33, pp. 454-459 (2006).
38. Teh, C.I., Wong, K.S., Goh, A.T.C., and Jaritngam,
S. Prediction of pile capacity using neural networks",
J. Computing in Civil Engineering, ASCE, 11(2), pp.
129-138 (1997).
39. Sivakugan, N., Eckersley, J.D., and Li, H. Settlement
predictions using neural networks", Australian Civil
Engineering Transactions, CE40, pp. 49-52 (1998).
40. Mola-Abasi, H., Shooshpasha, I., and Amiri, I. Prediction
of liquefaction induced lateral displacements
using GMDH type neural networks", Global Journal
of Scienti c Researches, 2(1), pp. 21-26 (2014).
41. Goh, A.T.C. Probabilistic neural network for evaluating
seismic liquefaction potential", Canadian Geotechnical
Journal, 39, pp. 219-232 (2002).
42. Kim, Y.S. and Kim, B.K. Use of arti cial neural
networks in the prediction of liquefaction resistance of
sands", Journal of Geotechnical and Geoenvironmental
Engineering, 132(11), pp. 1502-1504 (2006).
43. Ural, D.N. and Saka, H. Liquefaction assessment
by neural networks", Electronic Journal
of Geotechnical Engineering, 3, pp. 1-27 (1998).
( 1998/JourTOC3.htm)
44. Cho, S.E. Probabilistic stability analyses of slopes
using the ANN-based response surface", Computers
and Geotechnics, 36, pp. 787-797 (2009).
45. Ferentinou, M.D. and Sakellariou, M.G. Computational
intelligence tools for the prediction of slope
performance", Computers and Geotechnics, 34(5), pp.
362-384 (2007).
46. Wong, F.S. Time series forecasting using backpropagation
neural networks", Neurocomputing, 2, pp.
147-259 (1991).
47. Eskandari, H., Rezaee, M.R., and Mohammadnia, M.
Application of multiple regression and arti cial neural
network techniques to predict shear wave velocity from
well log data for a carbonate reservoir, South-West
Iran", Cseg Recorder, pp. 42-48 (2004).
48. Moatazedian, I., Rahimpour-Bonab, H., Kadkhodaie-
Ilkhchi, A., and Rajoli, M.R. Prediction of shear and
compressional wave velocities from petrophysical data
utilizing genetic algorithms technique: A case study in
Hendijan and Abuzar elds located in Persian Gulf",
Geopersia, 1, pp. 1-17 (2011).
49. Akhundi, H., Ghafoori, M., and Lashkaripour, G.R.
Prediction of shear wave velocity using arti cial
neural network technique, multiple regression and
petrophysical data: A case study in Asmari reservoir
(SW Iran)", Open Journal of Geology, 4, pp. 303-313
50. Rezaee, M.R., Kadkhodaie Ilkhchi, A., and Barabadi,
A. Prediction of shear wave velocity from petrophysical
data utilizing intelligent systems: An example from
a sandstone reservoir of Carnarvon Basin, Australia",
Journal of Petroleum Science and Engineering, 55, pp.
201-212 (2007).
51. Mola-Abasi, H., Dikmen, U., and Shooshpasha, I.
Prediction of shear-wave velocity from CPT data at
Eskisehir (Turkey) using a polynomial model", Near
Surface Geophysics, 13(2), pp. 155-167 (2015).
52. Mola-Abasi, H., Eslami, A., and Tabatabaie Shourijeh,
P. Shear wave velocity by polynomial neural networks
and genetic algorithms based on geotechnical soil properties",
Arabian Journal for Science and Engineering,
38(4), pp. 829-838 (2013).
53. Shooshpasha, I., Kordnaeij, A., Dikmen, U., MolaAbasi,
H., and Amir, I. Shear wave velocity by
support vector machine based on geotechnical soil
properties", Natural Hazards and Earth System Sciences
Discussions, 2(4), pp. 2443-2461 (2014).
54. Berberian, M. and Ghoreshi, M., Seismic-Fault Hazard
and Project Engineering of Thermal Power Plant
of Nishapur, Seismotectonical Survey, Ministry of
Energy, Power Engineering Corporation (Moshanir),
Tehran (1989) (in Persian).
55. Azadi, A., Javan-Doloei, G.H., Hafezi Moghadas, N.,
and Hessami-Azar, K. Geological, geotechnical and
geophysical characteristics of the Toos fault located
north of Mashhad, north-eastern Iran ", Journal of the
Earth and Space Physics, 35(4), pp. 17-34 (2010) (in
O. Ataee et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 1943{1955 1955
56. Building and Housing Research Center, Iranian Code
of Practice for Seismic Resistant Design of Buildings,
Standard No. 2800, 3rd Edn., Tehran, Iran (2007).
57. James, G., Witten, D., Hastie, T., and Tibshirani, R.,
An Introduction to Statistical Learning with Applications
in R, Springer, New York, Heidelberg Dordrecht,
London (2013).
58. Haykin, S., Neural Networks: A Comprehensive Foundation,
2nd. Ed. Prentice-Hall, Upper Saddle River,
New Jersey, pp. 26-32 (1999).
59. Shahin, M.A., Jaksa, M.B., and Maier, H.R. Arti
cial neural network applications in geotechnical
engineering", Australian Geomechanics, 36(1), pp. 49-
62 (2001).
60. Isik, F. and Ozden, G. Estimating compaction parameters
of ne and coarse grained soils by means
of arti cial neural networks", Environmental Earth
Sciences, 69, pp. 2287-2297 (2013).
61. Kisi, O. Stream
ow forecasting using di erent arti -
cial neural network algorithms", Journal of Hydrologic
Engineering. ASCE, 12(5), pp. 532-539 (2007).
62. Kanungo, D.P., Arora, M.K., Sarkar, S., and Gupta,
R.P. A comparative study of conventional, ANN black
box, fuzzy and combined neural and fuzzy weighting
procedures for landslide susceptibility zonation in Darjeeling
Himalayas", Engineering Geology, 85, pp. 347-
366 (2006).
63. Kartam, N., Flood, I., and Garrett, J.H. Arti cial
neural networks for civil engineers", Fundamentals and
Applications, ASCE, New York (1997).
64. Kayadelen, C. Estimation of e ective stress parameter
of unsaturated soils by using arti cial neural
networks", International Journal for Numerical and
Analytical Methods in Geomechanics, 32(9), pp. 1087-
1106 (2008).
65. Zhang, G., Patuwo, E.B., and Hu, M.Y. Forecasting
with arti cial neural network: The state of the art",
International Journal of Forecasting, 14, pp. 35-62
66. Wang, H.B., Xu, W.Y., and Xu, R.C. Slope stability
evaluation using back propagation neural networks",
Engineering Geology, 80, pp. 302-315 (2005).
67. Rivals, I. and Personnaz, L. Neural-network construction
and selection in nonlinear modeling", IEEE
Transaction on Neural Networks, 14(4), pp. 804-819
68. Ghiassi, M. and Nangoy, S. A dynamic arti cial neural
network model for forecasting nonlinear processes",
Computers & Industrial Engineering, 57(1), pp. 287-
297 (2009).