Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

Authors

1 Department of civil engineering, Yazd University, Iran

2 Departme n t of Civil Engineering , Yazd University , Iran

Abstract

In recent years, controlled blasting has turned into an efficient method for  evaluation of soil liquefaction in real scale and evaluation of ground improvement techniques. Predicting blast-induced soil liquefaction by using collected information can be an effective step in the[a1]  study of blast-induced liquefaction. In this study, to estimate residual pore pressure ratio, first, multi- layer perceptron neural network is used in which error (RMS) for the network was calculated as 0.105. Next, neuro-fuzzy network, ANFIS was used for modeling. Different ANFIS models  are created using Grid  partitioning (GP), Subtractive Clustering (SCM), and Fuzzy C-means Clustering (FCM). Minimum error is obtained using by FCM at about 0.081. Finally, radial basis function (RBF) network is used. Error of this method was about 0.06. Accordingly, RBF network has better performance. Variables including fine-content, relative density, effective overburden pressure and SPT value  are considered as input components and the Ru, residual[a2]  pore pressure ratio was used as the only output component for designing prediction models. In the next stage the network output is compared with the results of a regression analysis. Finally, sensitivity analysis for RBF network is tested,  its results reveal that and SPT are the most effective factors in determining Ru.

Keywords

Main Subjects


References

1. Charlie, W.A., Bretz, T.E., Schure (White), L.A., and Doehring, D.O. \Blast-induced pore pressure and liquefaction of saturated sand", J. Geotech. Geoenviron. Eng., 139, pp. 1308-1319 (2013). DOI: 10.1061/(ASCE)GT.1943-5606.0000846
2. Wang, Z., Lu, Y., and Bai, C. \Numerical simulation of explosion-induced soil liquefaction and its e ffect on surface structures", Journal of the Finite Elements
in Analysis and Design., 47 pp. 1079-1090 (2011)
DOI:10.1016/j. nel.2011.04.001
3. Charlie, W.A., Hubert, M.E., Schure, L.A., Veyera,
G.E. et al. \Blast-induced soil liquefaction: Summary
of literature, Final Report to AFOSR, AD-A19995",
Department of Civil Engineering, Colorado State University
(1988).
4. Lyman, A.K.B. \Compaction of cohesionless foundation
soils by explosives", Transactions ASCE, 107, pp.
1330-1348 (1942).
5. Solymar, S.V. \Compaction of alluvial sands by deep
blasting", Can. Geotech. J., 21(2), pp. 305-321 (1984).
6. Kato, K., Mason, H.B., and Ashford, S.A. \Ground
vibration from blast-induced liquefaction testing in
Christchurch, New Zealand", 6th International ConF.
Asvar et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 617{631 629
ference on Earthquake Geotechnical Engineering.,
Christchurch, New Zealand (2015).
7. Hatzor, Y.H., Gvirtzman, H., Wainshtein, I.,
and Orian, I. \Induced liquefaction experiment
in relatively dense, clay-rich sand deposits", J.
Geophys. Res, B02311., 114, pp. 1-22 (2009).
DOI:10.1029/2008JB005943
8. Sugano, T., Kohama, E., Mitoh, M., and Shiozaki,
Y. \Seismic performance of urban, reclaimed and
port areas - full scale experiment at Tokachi port
by controlled blasting technique", The Earthquake
Engineering Symposium, 11, pp. 901-906 (2002).
9. Ashford, S.A., Rollins, K.M., and Lane, J.D. \Blastinduced
liquefaction for full-scale foundation testing",
J. Geotech. Geoenviron. Eng., 130(8), pp. 798-806
(2004).
10. Bolton, J.M., Durnfold, D.S. and Charlie, W.A. \ Onedimensional
shock and quasi-static liquefaction of silt
and sand", J. Geotech. Engrg., 120(10), pp. 1889-1974
(1994).
11. Gohl, W.B., Howie, J.A. and Rea, C.E. \Use of
controlled detonation of explosives for liquefaction
testing", Fourth Int. Conf. on Recent Advances in
Geotechnical Earthquake Engineering and Soil Dynamics,
San Diego, Calif., paper no. 913. USA (2001).
12. Rollins, K.M., Gerber, T.M., Lane, J.D., and Ashford,
S.A. \Lateral resistance of a full-scale pile group in
lique ed sand" J. Geotech. Geoenviron. Eng., 131(1),
pp. 115-125 (2005).
13. Hijikata, K., Ishida, T., Tanaka, H., Koyamada, K.,
Miyamoto, Y., Kontani, O., and Nigbor, R. \Experimental
study on soil-pile-structure interaction in lique-
able sand subjected to blast-induced ground motion",
13th World Conference on Earthquake Engineering,
Vancouver, B.C., Canada, Paper No. 190 (2004).
14. Byrne, P.M., Park, S.S., Beaty, M., Sharp, M.,
Gonzalez, L., and Abdoun, T. \Numerical modeling
of liquefaction and comparison with centrifuge tests",
Can. Geotech. J., 41, pp. 193-211 (2004).
15. Byrne, P.M., Park, S.S., Beaty, M., Sharp, M.,
Gonzalez, L., and Abdoun, T. \Numerical modeling
of dynamic centrifuge tests", 13th World Conference
on Earthquake Engineering, Vancouver, B.C., Canada,
Paper No. 3387 (2004).
16. Lee, W.Y. \Numerical modeling of blast-induced liquefaction",
PhD Thesis, Department of Civil and Environmental
Engineering, Brigham Young University,
67(06B):3305 (2006).
17. Taylor, P.A., Modeling the Response of Variably Saturated
Geomaterials to Large Strain Rate Loading,
Department of Computational Physics & Simulation
Frameworks Presentation, Sandia National Laboratories,
Albuquerque, NM, USA (2004).
18. Bell, R.L., Baer M.R., Brannon, R.M., Crawford,
D.A., Elrick, M.G., Hertel, E.S. Jr., Schmitt, R.G.,
Silling, S.A., and Taylor P.A., CTH User's Manual
and Input Instructions, Version 7.0. Sandia National
Laboratories, Albuquerque, NM, USA (2005).
19. Lewis, B.A. \Manual for LS-DYNA soil material model
147", Report FHWA-HRT-04-095, McLean, VA, Federal
Highway Administration (2004).
20. Wang, Z., Hao, H., and Lu, Y. \A three-phase soil
model for simulating stress wave propagation due to
blast loading", Int. J. Numer. Anal. Meth. Geomech.,
28, pp. 33-56 (2004). DOI: 10.1002/nag.325.
21. Wang, Z., Lu, Y., and Bai, C. \Numerical analysis
of blast-induced liquefaction of soil", Comput.
Geotech., 35(2), pp. 196-209 (2008). DOI:
10.1016/j.compgeo.2007.04.006
22. Charlie, W.A. and Doehring, D.O. \Groundwater
Table mounding, pore pressure, and liquefaction induced
by explosions: Energy-distance relations", Rev.
Geophys., 45, RG4006. pp. 1-9 (2007).
23. Kummeneje, O. and Eide, O. \Investigation of loose
sand deposits by blasting", 5th International Conf.
Soil Mechanics and Foundation Engineering, 1, pp.
491-497 (1961).
24. Studer, J. and Kok, L. \Blast-induced excess porewater
pressure and liquefaction experience and application",
International Symposium on Soils under
Cyclic and Transient Loading, Swansea, UK, pp. 581-
593 (1980).
25. Rollins, K.M., Lane, J.D., Nicholson, P.G., and
Rollins, R.E. \Liquefaction hazard assessment using
controlled-blasting techniques", 11th International
Conference on Soil Dynamics & Earthquake Engineering,
2, pp. 630-637 (2004).
26. Eller, J.M. \Predicting pore pressure response in insitu
liquefaction studies using controlled blasting",
Master's thesis, Oregon State University (2011).
27. Baziar, M.H. and Ghorbani, A. \Evaluation of lateral
spreading using arti cial neural networks", Soil Dynamics
and Earthquake Engineering, 25(1), pp. 1-9
(2005). DOI:10.1016/j.soildyn.2004.09.001
28. Hanna, A.M., Ural, D., and Saygili, G. \Neural
network model for liquefaction potential in soil deposits
using Turkey and Taiwan earthquake data",
Soil Dynamics and Earthquake Engineering, 27(6), pp.
521-540 (2007). DOI:10.1016/j.soildyn.2006.11.001
29. Juang, C.H., Chen, C.J., and Tien, Y-M. \Appraising
cone penetration test based liquefaction resistance
evaluation methods: arti cial neural network approach",
Can. Geotech. J., 36(3), pp. 443-454 (1999).
DOI: 10.1139/t99-011
30. Juang, C.H., Chen, C.J., Jiang, T., and Andrus, R.D.
\Risk-based liquefaction potential evaluation using
standard penetration tests", Can. Geotech. J., 37(6),
pp. 1195-208 (2000). DOI: 10.1139/t00-064
31. 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).
32. Jang, J.S.R., Sun, CT., and Mizutani, E. Neuro-fuzzy
and Soft Computing: A Computational Approach to
Learning and Machine Intelligence, Prentice Hall, New
Jersey, USA (1997).
630 F. Asvar et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 617{631
33. Kalkan, E., Akbulut, S., Tortum, A., and Celik, S.
\Prediction of the uncon ned compressive strength
of compacted granular soils by using inference systems",
Environ. Geol., 58, pp. 1429-1440 (2008). DOI:
10.1007/s00254-008-1645-x
34. Provenzano, P., Ferlisi, S., and Musso, A. \Interpretation
of a model footing response through an adaptive
neural fuzzy inference system", Comput. Geotech., 31,
pp. 251-66 (2004).
35. Kayadelen, C., Taskiran, T., Gunaydin, O., and Fener,
M. \Adaptive neuro-fuzzy modeling for the swelling
potential of compacted soils", Environ. Earth Sci., 59,
pp. 109-115 (2009).
36. Sezer, A., Goktepe, B.A., and Altun, S. \Adaptive
neuro-fuzzy approach for sand permeability estimation",
Environ. Eng. Manage. J., 9(2), pp. 231-238
(2010).
37. Cabalar, A.F., Cevik, A., and Gokceoglu, C.
\Some applications of Adaptive Neuro-Fuzzy Inference
System (ANFIS) in geotechnical engineering",
Comput. Geotech., 40, pp. 14-33 (2012).
DOI:10.1016/j.compgeo.2011.09.008
38. Nakazawa, H., and Sugano, T. \Full-scale eld test
on liquefaction-induced damage of runway pavement
by controlled blast technique", 9th U.S. National Conference
on Earthquake Engineering, Toronto, Ontario-
Canada (2010).
39. Port and Airport Research Institute (PARI), Japan.
\Full-scale eld experiment of airport facilities during
liquefaction induced by controlled blasting technique",
Technical Note of the Port and Airport Research Institute.
Independent Administrative Institution, Japan,
338 pp. (2009) (In Japanese).
40. Rollins, K.M. \Liquefaction mitigation using vertical
composite drains: full scale testing", Final Report for
Highway IDEA Project 94. Transportation Research
Board, 105 (2004).
41. Strand, S.R. \Liquefaction mitigation using vertical
composite drains and liquefaction-induced downdrag
on piles: implications for deep foundation design",
PhD thesis, Department of Civil and Environmental
Engineering, Brigham Young University (2008).
42. Ashford, S.A. and Juirnarongrit, T. \Performance
of lifelines subjected to lateral spreading", Report
SSRP-04/18, Department of Structural Engineering,
University of California, San Diego (2004).
43. Ashford, S.A., Juirnarongrit, T., Sugano, T., and
Hamada, M. \Soil-pile response to blast-induced lateral
spreading. 1: eld test", J. Geotech. Geoenviron.
Eng., 132(2), pp. 152-162 (2006). DOI:
10.1061/(ASCE)1090-0241(2006)132:2(152)
44. Ashford, S.A. and Rollins, K.M. \TILT: Treasure
island liquefaction test: nal report", Report SSRP-
2001/17, Department of Structural Engineering, University
of California, San Diego (2002).
45. Pathirage, K.S. \Critical assessment of the CANLEX
blast experiment to facilitate a development of an insitu
liquefaction methodology using explosives", Master's
Thesis, Department of Civil Engineering, The
University of British Columbia (2000).
46. Emami, M. \Application of arti cial neural networks
in pressuremeter test results", Master's Thesis, Tarbiat
Modares University, Tehran, Iran (2009).
47. Marquardt, D.W. \An algorithm for least squares
estimation of nonlinear parameters", Soc. Ind. Appl.,
11(2), pp. 431-441 (2006). DOI:10.1137/0111030
48. Zadeh, L.A. \Fuzzy sets", Inform. Control., 8, pp. 338-
353 (1965).
49. Chiu, S.L. \Fuzzy model identi cation based on cluster
estimation", Journal of Intelligent & Fuzzy Systems, 2,
pp. 267-278 (1994).
50. Bezdek, J.C. \Pattern recognition with fuzzy objective
function algorithms", Advanced Application in Pattern
Recognition, Plenum Press, New York and London
(1981). DOI: 10.1007/978-1-4757-0450-1
51. Behnia, D., Ahangari, K., Noorzad, A., and
Moeinossadat, S.R. \Predicting crest settlement in
concrete face rock ll dams using adaptive neurofuzzy
inference system and gene expression programming
intelligent methods", J. Zhejiang. Univ-Sci. A.
(Appl. Phys. & Eng.), 14(8), pp. 589-602 (2013)
DOI:10.1631/jzus.A1200301
52. Gupta, M.M., Jin, L., and Homma, N. \Static and
Dynamic Neural Network", From Fundamentals to Advanced
Theory, John Wiley & Sons, INC., Publication,
Hobokon, New Jersey (2003).
53. Fyfe, C., Arti cial Neural Networks, Department of
Computing and Information Systems, The University
of Paisley, Room, 1.1 Edn. (1996).
54. Seed, H.B. and Idriss, I.M. \Simpli ed procedure for
evaluating soil liquefaction potential", J. Soil. Mech.
Found. Div., ASCE, 97 (SM9), pp. 1249-1273 (1971).
55. Dobry, R., Ladd, R.S., Yokel, F.Y., Chung, R.M.
and Powell, D. \Prediction of pore water pressure
buildup and liquefaction of sands during earthquakes
by the cyclic strain method", Build. Sci. Series., 138,
National Bureau of Standards, US Department of
Commerce, US Governmental Printing Oce, Washington,
DC (1982).
56. Emamgholizadeh, S., Kashi, H., Marofpoor, I., and
Zalaghi, E. \Prediction of water quality parameters
of Karoon River (Iran) by arti cial intelligence-based
models", Int. J. Environ. Sci. Technol., 11, pp. 645-
656 (2014). DOI: 10.1007/s13762-013-0378-x