Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)

doi:10.1007/s11709-014-0256-8

Front. Struct. Civ. Eng.

2014, Vol. 8

Issue (3) : 292-307 https://doi.org/10.1007/s11709-014-0256-8

CASE STUDY

Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)

Sadegh REZAEI(

), Asskar Janalizadeh CHOOBBASTI

Department of Civil Engineering, Babol University of Technology, Babol P.O. Box 484, Iran

Download: PDF(3266 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

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 ( $K g$ ) 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 [1] method in 30 excavated boreholes within the study area. Also, the results obtained by the artificial neural network (ANN) were compared with microtremor measurement. Regarding the results of these three methods, it was concluded that the threshold value of liquefaction potential is $K g = 5$ . On the basis of the analysis performed in this research it is concluded that microtremors have the capability of assessing the liquefaction potential with desirable accuracy.

Keywords liquefaction microtremor vulnerability index artificial neural networks (ANN) microzonation

Corresponding Author(s): Sadegh REZAEI

Online First Date: 25 July 2014 Issue Date: 19 August 2014

Cite this article:

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI. Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)[J]. Front. Struct. Civ. Eng., 2014, 8(3): 292-307.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-014-0256-8
https://academic.hep.com.cn/fsce/EN/Y2014/V8/I3/292

Tab.1 The properties of available faults around the study area

Fig.1 Location and magnitude of earthquakes, Faults and their mechanism near the Babol

Fig.2 map of ground water distribution in Babol city

Fig.3 Simple model assumed by Nakamura

Fig.4 Location of microtremor recording stations and geotechnical boreholes

Fig.5 H/V spectral ratio

Fig.6 Fundamental frequency

Fig.7 Amplification factor

Fig.8 map of

K g

distribution in Babol city

station	liquefaction depths by using Seed & Idriss method/m	$K g$ value	station	liquefaction depths by using Seed & Idriss method/m	$K g$ value
B01	2–4.5 and 5.5–8 and 8.5–10.5	7.51	B28	5.5–8	5.51
B02	5-10	7.01	B30	–	1.25
B04	–	2.53	B32	–	4.74
B05	5.5–7	5.21	B35	3–6 and 6.5–10.5	14.6
B07	–	4.80	B37	6.5–9.5	6.61
B09	–	4.63	B39	–	4.7
B11	–	1.53	B42	5–12	8.14
B12	5–6.5& 8–9.5	6.15	B43	4.5–7 and 8–10	7.19
B15	–	4.63	B44	7–8	5.2
B18	–	1.10	B45	4–6.5	5.17
B19	6–7 and 9–10	5.93	B50	4–6 and 7–8 and 8.5–9.5	6.79
B22	–	3.65	B52	3.5-5 and 6.5-10.5	11.57
B23	8.5–10.5	5.42	B56	6.5–11	8.34
B25	3–4 and 4.5-5 and 5.5–8 and 8.5–10.5	10	B57	–	1.92
B27	–	4.12	B58	–	1.16

Tab.2 The liquefaction depths versus

K g

value

Fig.9 Evaluation of factor of safety using conventional and ANN method

Fig.10 The structure of a MLP-type network

Tab.3 data sets with inputs and output values with indicating the training and testing sets

Tab.4 Inputs and output ranges for training data sets used for construction of ANN model

Tab.5 Inputs and output ranges for testing data sets used for construction of ANN model

Fig.11 Maximum squared error versus number of hidden layer neurons

Fig.12 Effects of the number of hidden neurons on the network performance

Fig.13 Performance of ANN in term of regression value

Fig.14 Performance of ANN in term of regression value (normalized value of data)

Fig.15 ANN prediction for the FS for all boreholes

Fig.16 Regression value for train and test

Fig.17 Liquefaction microzonation of Babol by microtremor measurement

Fig.18 Liquefaction microzonation of Babol by ANN

1	H B Seed, I M Idriss. Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundations Division, 1971, SM9: 1249–1273
2	A J Choobbasti, S Rezaei, F Farrokhzad. Evaluation of site response characteristics using microtremors. Gradevinar, 2013, 65: 731–741
3	A J Choobbasti. Numerical simulation of liquefaction. Dissertation for the Doctoral Degree. Manchester: University of UMIST, 1997
4	Y Nakamura. A method for dynamic characteristics estimation of subsurface using microtremor on the ground surface. Quarterly Report of RTRI, 1989, 30: 25–33
5	S Rezaei, AJ Choobbasti, S Soleimani Kutanaei. 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	Y R Paudyal, R Yatabe, N P Bhandary, R K Dahal. Basement topography of the Kathmandu Basin using microtremor observation. Journal of Asian Earth Sciences, 2013, 62: 627–637
7	M S Fnais, K Abdelrahman, A M Al-Amri. Microtremor measurements in Yanbu city of Western Saudi Arabia: A tool for seismic microzonation. J King Saud Univ, 2010, 22(2): 97–110
8	MAA Beroya, A Aydin, R Tiglao, M Lasala. Use of microtremor in liquefaction hazard mapping. Eng Geo, 2009, 107: 140–153
9	G Saygili. Liquefaction potential assessment in soil deposits using artificial neural network. Master Thesis, Montreal: Concordia University, 2005
10	Y Nakamura. Real-time information systems for hazard mitigation. In: Proceedings of the 10th World Conference in Earthquake Engineering. Spain, Madrid, 1996
11	H C Huang, Y S Tseng. Characteristics of soil liquefaction using H/V of microtremors in Yuan-Lin area, Taiwan. TAO, 2002, 13(3): 325–338
12	F Farrokhzad, A J Choobbasti, A Barari. Liquefaction microzonation of Babol city using artificial neural network. J King Saud Univ Sci, 2012, 24(1): 89–100
13	AJ Choobbasti, S Rezaei, F Farrokhzad, P Azar. Evaluation of site response characteristics using nonlinear method (Case study: Babol, Iran), Front Struct Civ Eng. 2014, 8(1): 69–82 https://doi.org/10.1007/s11709-014-0231-4
14	K Kanai, T Takana. On microtremors. VIII. Bull Earth Research Int, 1961, 39: 97–114
15	U Dikmen, M Mizaoglu. 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	S Rezaei. 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	T Toshinawa, M Inoue, N Yoneyama, Y Hoshino, K Mimura, Y Yokoi. Geologic-profile estimates of Kofu Basin, Japan, by making use of microtremor observations. Geophysical Research Abstracts, 2003, 5: 02079
18	Y Maruyama, F Yamazaki, T Hamada. 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	Y Nakamura. 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	M Bour, D Fouissac, P Dominique, C Martin. On the use of microtremor rcordings in seismic microzonation. Soil Dynamics and Earthquake Engineering, 1998, 17(7–8): 465–474
21	P Teves-Costa, L Matias, P Y Bard. Seismic behavior estimation of thin alluvium layers using microtremor recordings. Soil Dynamics and Earthquake Engineering, 1996, 15(3): 201–209
22	E H Field, S H Hough, K Jacob. 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	P Harutoonian, C J Leo, T Doanh, S Castellaro, J J Zou, D S Liyanapathirana, H Wong, K Tokeshi. Microtremor measurements of rolling compacted ground. Soil Dynamics and Earthquake Engineering, 2012, 41: 23–31
24	A Gosar. 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	B Zhao, X Xie, C Chai, H Ma, X Xu, D Peng, W Yin, J Tao. 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	F J Chávez-García, T S Kang. Lateral heterogeneities and microtremors: Limitations of HVSR and SPAC based studies for site response. 2014, 174: 1–10
27	A Deif, S El-Hadidy, S R M Sayed, A El Werr. 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	Y Nakamura. Seismic vulnerability indices for ground and structures using Microtremor. World Congress on Railway Research, Italy, Florence, 1997
29	F Uehan, Y Nakamura. Ground motion characteristics around Kobe City detected by microtremor measurement. In: Proceedings of the 11th World Conference on Earthquake Engineering. Acapulco. Mexico, 1996
30	J Saita, Y Nakamura, T Sato. 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	M Y Walling, W K Mohanty, S K Nath, S Mitra, A John. 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	H Bolton Seed, K Tokimatsu, L F Harder, R M Chung. Influence of SPT procedures in soil liquefaction resistance evaluations. Journal of Geotechnical Engineering, 1985, 111(12): 1425–1445
33	S K Jha, K Suzuki. Reliability analysis of soil liquefaction based on standard penetration test. Computers and Geotechnics, 2009, 36(4): 589–596
34	T L Youd, I M Idriss, R D Andrus, I Arango, G Castro, J T Christian, R Dobry, W D L Finn, L F Harder Jr, M E Hynes, K Ishihara, J P Koester, S S C Liao, W F Marcuson III, G R Martin, J K Mitchell, Y Moriwaki, M S Power, P K Robertson, R B Seed, K H Stokoe 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	A J Choobbasti, F Farrokhzad, A Barari. Predicting Subsurface Soil Layering and Landslide risk with Artifical Neural Network a Case Study from Iran. Geologica Carpath, 2011, 5: 1–16
36	N Vu-Bac, T Lahmer, H Keitel, J Zhao, X Zhuang, T Rabczuk. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84
37	N Vu-Bac, T Lahmer, Y Zhang, X Zhuang, T Rabczuk. Stochastic predictions of interfacial characteristic of carbon nanotube polyethylene composites. Composites. Part B, Engineering, 2014, 59: 80–95
38	A T C Goh. Empirical design in geotechnics using neural networks. Geotechnique, 1995, 45(4): 709–714
39	A T C Goh. Neural network modeling of CPT seismic liquefaction data. J Geotech Geoenviron Eng Div, 1996, 122(1): 70–73.
40	A T C Goh. Probabilistic neural network for evaluating seismic liquefaction potential. Canadian Geotechnical Journal, 2002, 39(1): 219–232
41	D N Ural, H Saka. Liquefaction assessment by neural networks. Elect J Geotech Eng, 1989
42	C Hsein Juang, C J Chen, Y M Tien. Appraising cone penetration test based liquefaction resistance evaluation methods: Artificial neural network approach. Canadian Geotechnical Journal, 1999, 36(3): 443–454
43	S Barai, G Agarwal. Studies on isnstance based learning models for liquefaction potential assessment, Elect J Geotech Eng, 2002
44	A M Hanna, G Morcous, M Helmy. Efficiency of pile groups installed in cohesionless soil using artificial neural networks. Canadian Geotechnical Journal, 2004, 41(6): 1241–1249
45	D Cha, H Zhang, M Blumenstein. Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model. Ocean Engineering, 2011, 38: 878–887
46	H Tavakoli, O L Omran, S S Kutanaei, M S Shiade. 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	A J Choobbasti, H Tavakoli, S S Kutanaei. 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	P J Werbos. Beyond regression: New Tools for Prediction and Analysis in the Behavioural Sciences, Dissertation for the Doctoral Degree. Canbridge: Harvard University, 1974

[1]	Hanjiang LAI, Shifan WU, Mingjuan CUI, Jian CHU. Recent development in biogeotechnology and its engineering applications[J]. Front. Struct. Civ. Eng., 2021, 15(5): 1073-1096.
[2]	Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU. Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential[J]. Front. Struct. Civ. Eng., 2021, 15(2): 490-505.
[3]	Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD. Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks[J]. Front. Struct. Civ. Eng., 2021, 15(1): 80-98.
[4]	Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU. A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data[J]. Front. Struct. Civ. Eng., 2020, 14(6): 1476-1491.
[5]	Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG. A constrained neural network model for soil liquefaction assessment with global applicability[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1066-1082.
[6]	Mengfei QU,Qiang XIE,Xinwen CAO,Wen ZHAO,Jianjun HE,Jiang JIN. Model test of stone columns as liquefaction countermeasure in sandy soils[J]. Front. Struct. Civ. Eng., 2016, 10(4): 481-487.
[7]	Ikram GUETTAYA,Mohamed Ridha EL OUNI. In situ-based assessment of soil liquefaction potential–Case study of an earth dam in Tunisia[J]. Front. Struct. Civ. Eng., 2014, 8(4): 456-461.
[8]	Mingjing JIANG, Haijun HU, Jianbing PENG, Serge LEROUEIL. Experimental study of two saturated natural soils and their saturated remoulded soils under three consolidated undrained stress paths[J]. Front Arch Civil Eng Chin, 2011, 5(2): 225-238.
[9]	Lianyang ZHANG, . A simple method for evaluating liquefaction potential from shear wave velocity[J]. Front. Struct. Civ. Eng., 2010, 4(2): 178-195.
[10]	JIANG Guanlu, LIU Xianfeng, ZHANG Jianwen, ZHAO Ruyi. Shaking table test of composite foundation reinforcement of saturated silty soil for high speed railway[J]. Front. Struct. Civ. Eng., 2007, 1(3): 353-360.

Viewed

Full text

Abstract

Cited

Shared

Discussed