Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)
Sadegh REZAEI, Asskar Janalizadeh CHOOBBASTI
Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)
Recent researchers have discovered microtremor applications for evaluating the liquefaction potential. Microtremor measurement is a fast, applicable and cost-effective method with extensive applications. In the present research the liquefaction potential has been reviewed by utilization of microtremor measurement results in Babol city. For this purpose microtremor measurements were performed at 60 measurement stations and the data were analyzed by suing Nakmaura’s method. By using the fundamental frequency and amplification factor, the value of vulnerability index () was calculated and the liquefaction potential has been evaluated. To control the accuracy of this method, its output has been compared with the results of Seed and Idriss [
liquefaction / microtremor / vulnerability index / artificial neural networks (ANN) / microzonation
[1] |
Seed H B, Idriss I M. Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundations Division, 1971, SM9: 1249–1273
|
[2] |
Choobbasti A J, Rezaei S, Farrokhzad F. Evaluation of site response characteristics using microtremors. Gradevinar, 2013, 65: 731–741
|
[3] |
Choobbasti A J. Numerical simulation of liquefaction. Dissertation for the Doctoral Degree. Manchester: University of UMIST, 1997
|
[4] |
Nakamura Y. A method for dynamic characteristics estimation of subsurface using microtremor on the ground surface. Quarterly Report of RTRI, 1989, 30: 25–33
|
[5] |
Rezaei S, Choobbasti AJ, Soleimani Kutanaei S. Site effect assessment using microtremor measurement, equivalent linear method and artificial neural network (Case study: Babol, Iran), Arab J of Geosci, 2013 (DOI: 10.1007/s12517-013-1201-1)
|
[6] |
Paudyal Y R, Yatabe R, Bhandary N P, Dahal R K. Basement topography of the Kathmandu Basin using microtremor observation. Journal of Asian Earth Sciences, 2013, 62: 627–637
|
[7] |
Fnais M S, Abdelrahman K, Al-Amri A M. Microtremor measurements in Yanbu city of Western Saudi Arabia: A tool for seismic microzonation. J King Saud Univ, 2010, 22(2): 97–110
|
[8] |
Beroya MAA, Aydin A, Tiglao R, Lasala M. Use of microtremor in liquefaction hazard mapping. Eng Geo, 2009, 107: 140–153
|
[9] |
Saygili G. Liquefaction potential assessment in soil deposits using artificial neural network. Master Thesis, Montreal: Concordia University, 2005
|
[10] |
Nakamura Y. Real-time information systems for hazard mitigation. In: Proceedings of the 10th World Conference in Earthquake Engineering. Spain, Madrid, 1996
|
[11] |
Huang H C, Tseng Y S. Characteristics of soil liquefaction using H/V of microtremors in Yuan-Lin area, Taiwan. TAO, 2002, 13(3): 325–338
|
[12] |
Farrokhzad F, Choobbasti A J, Barari A. Liquefaction microzonation of Babol city using artificial neural network. J King Saud Univ Sci, 2012, 24(1): 89–100
|
[13] |
Choobbasti AJ, Rezaei S, Farrokhzad F, Azar P. Evaluation of site response characteristics using nonlinear method (Case study: Babol, Iran), Front Struct Civ Eng. 2014, 8(1): 69–82
CrossRef
Google scholar
|
[14] |
Kanai K, Takana T. On microtremors. VIII. Bull Earth Research Int, 1961, 39: 97–114
|
[15] |
Dikmen U, Mizaoglu M. The sesimic microzonation map of Yenisehir-Bursa, NW of Turket by means of ambient noise measurements. J Balkan Geoph Soc, 2005, 8: 53–62
|
[16] |
Rezaei S. Assessing the site effects and estimation of strong ground motion specification by using microtremor data and compare its results with simulation of soil profile (Case study: western part of Babol city). Master Thesis, Babol: Babol Noshiravani University of Technology, 2014
|
[17] |
Toshinawa T, Inoue M, Yoneyama N, Hoshino Y, Mimura K, Yokoi Y. Geologic-profile estimates of Kofu Basin, Japan, by making use of microtremor observations. Geophysical Research Abstracts, 2003, 5: 02079
|
[18] |
Maruyama Y, Yamazaki F, Hamada T. Microtremor measurements for the estimation of seismic motion along expressway. In: Proceedings of the 6th International Conference of Seismic Zonation. Palm Springs, USA, California, 2000
|
[19] |
Nakamura Y. Clear identification of fundamental idea of Nakamura’s technique and its applications. In: Proceedings of the 12th World Conference on Earthquake Engineering. Auckland, New Zealand, 2000
|
[20] |
Bour M, Fouissac D, Dominique P, Martin C. On the use of microtremor rcordings in seismic microzonation. Soil Dynamics and Earthquake Engineering, 1998, 17(7–8): 465–474
|
[21] |
Teves-Costa P, Matias L, Bard P Y. Seismic behavior estimation of thin alluvium layers using microtremor recordings. Soil Dynamics and Earthquake Engineering, 1996, 15(3): 201–209
|
[22] |
Field E H, Hough S H, Jacob K. Using microtremors to assess potential 16-earthquake site response: A case study in Flushing Meadows, New York City. Bulletin of the Seismological Society of America, 1990, 80: 1456–1480
|
[23] |
Harutoonian P, Leo C J, Doanh T, Castellaro S, Zou J J, Liyanapathirana D S, Wong H, Tokeshi K. Microtremor measurements of rolling compacted ground. Soil Dynamics and Earthquake Engineering, 2012, 41: 23–31
|
[24] |
Gosar A. Microtremor HVSR study for assessing site effects in the Bovec basin (NW Slovenia) related to 1998 Mw5.6 and 2004 Mw5.2 earthquakes. Engineering Geology, 2007, 91(2–4): 178–193
|
[25] |
Zhao B, Xie X, Chai C, Ma H, Xu X, Peng D, Yin W, Tao J. Imaging the garben structure in the deep basin with a microtremor profile crossing the Yinchuan City. Journal of Geophysics and Engineering, 2007, 4(3): 293–300
|
[26] |
Chávez-García F J, Kang T S. Lateral heterogeneities and microtremors: Limitations of HVSR and SPAC based studies for site response. 2014, 174: 1–10
|
[27] |
Deif A, El-Hadidy S, Sayed S R M, El Werr A. Definition soil characteristics and ground response at the northewerstern part of Gulf of Suez, Egypt. Journal of Geophysics and Engineering, 2008, 5(4): 420–437
|
[28] |
Nakamura Y. Seismic vulnerability indices for ground and structures using Microtremor. World Congress on Railway Research, Italy, Florence, 1997
|
[29] |
Uehan F, Nakamura Y. Ground motion characteristics around Kobe City detected by microtremor measurement. In: Proceedings of the 11th World Conference on Earthquake Engineering. Acapulco. Mexico, 1996
|
[30] |
Saita J, Nakamura Y, Sato T. Liquefaction caused by the 2011 off the Pacific coast of Tohoku earthquake and the result of the prior microtremor measurement. In: Proceedings of the 9th International Conference on urban earthquake engineering. Tokyo, Japan, 2012
|
[31] |
Walling M Y, Mohanty W K, Nath S K, Mitra S, John A. Microtremor survey in Talchir, India to ascertain its basin characteristics in terms of predominant frequency by Nakmaura’s ratio technique. Engineering Geology, 2009, 106(3–4): 123–132
|
[32] |
Bolton Seed H, Tokimatsu K, Harder L F, Chung R M. Influence of SPT procedures in soil liquefaction resistance evaluations. Journal of Geotechnical Engineering, 1985, 111(12): 1425–1445
|
[33] |
Jha S K, Suzuki K. Reliability analysis of soil liquefaction based on standard penetration test. Computers and Geotechnics, 2009, 36(4): 589–596
|
[34] |
Youd T L, Idriss I M, Andrus R D, Arango I, Castro G, Christian J T, Dobry R, Finn W D L, Harder L F Jr, Hynes M E, Ishihara K, Koester J P, Liao S S C, Marcuson W F III, Martin G R, Mitchell J K, Moriwaki Y, Power M S, Robertson P K, Seed R B, Stokoe K H II. Liquefaction resistance of soils; summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. Journal of Geotechnical and Geoenvironmental Engineering, 2001, 127(10): 817–833
|
[35] |
Choobbasti A J, Farrokhzad F, Barari A. Predicting Subsurface Soil Layering and Landslide risk with Artifical Neural Network a Case Study from Iran. Geologica Carpath, 2011, 5: 1–16
|
[36] |
Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84
|
[37] |
Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of carbon nanotube polyethylene composites. Composites. Part B, Engineering, 2014, 59: 80–95
|
[38] |
Goh A T C. Empirical design in geotechnics using neural networks. Geotechnique, 1995, 45(4): 709–714
|
[39] |
Goh A T C. Neural network modeling of CPT seismic liquefaction data. J Geotech Geoenviron Eng Div, 1996, 122(1): 70–73.
|
[40] |
Goh A T C. Probabilistic neural network for evaluating seismic liquefaction potential. Canadian Geotechnical Journal, 2002, 39(1): 219–232
|
[41] |
Ural D N, Saka H. Liquefaction assessment by neural networks. Elect J Geotech Eng, 1989
|
[42] |
Hsein Juang C, Chen C J, Tien Y M. Appraising cone penetration test based liquefaction resistance evaluation methods: Artificial neural network approach. Canadian Geotechnical Journal, 1999, 36(3): 443–454
|
[43] |
Barai S, Agarwal G. Studies on isnstance based learning models for liquefaction potential assessment, Elect J Geotech Eng, 2002
|
[44] |
Hanna A M, Morcous G, Helmy M. Efficiency of pile groups installed in cohesionless soil using artificial neural networks. Canadian Geotechnical Journal, 2004, 41(6): 1241–1249
|
[45] |
Cha D, Zhang H, Blumenstein M. Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model. Ocean Engineering, 2011, 38: 878–887
|
[46] |
Tavakoli H, Omran O L, Kutanaei S S, Shiade M S. Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network. Latin American Journal of Solids and Structures, 2014, 11(6): 966–979
|
[47] |
Choobbasti A J, Tavakoli H, Kutanaei S S. Modeling and optimization of a trench layer location around a pipeline using artificial neural networks and particle swarm optimization algorithm. Tunnelling and Underground Space Technology, 2014, 40: 192–202
|
[48] |
Werbos P J. Beyond regression: New Tools for Prediction and Analysis in the Behavioural Sciences, Dissertation for the Doctoral Degree. Canbridge: Harvard University, 1974
|
/
〈 | 〉 |