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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2014, Vol. 8 Issue (3) : 292-307
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
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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 (Kg) 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 Kg=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 Authors: 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.
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Asskar Janalizadeh CHOOBBASTI
fault namedistance to study area/kmfault length/kmfault mechanism
Firooz Abad85112thrust fault
Alborz44300thrust fault
Khazar16550thrust fault
Attari9185thrust fault
Astane9375thrust fault
Garmsar13670thrust fault
Kandovan10064thrust fault
Mosha91400thrust fault
North of Tehran115108thrust fault
Ivanaki14375thrust fault
Firoozkooh8440thrust fault
Basham9671thrust fault
Ourim7244thrust fault
Damghan136100thrust fault
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 Kg distribution in Babol city
stationliquefaction depths by using Seed & Idriss method/mKg valuestationliquefaction depths by using Seed & Idriss method/mKg value
B012–4.5 and 5.5–8 and 8.5–10.57.51B285.5–85.51
B055.5–75.21B353–6 and 6.5–10.514.6
B125–6.5& 8–9.56.15B434.5–7 and 8–107.19
B196–7 and 9–105.93B504–6 and 7–8 and 8.5–9.56.79
B223.65B523.5-5 and 6.5-10.511.57
B253–4 and 4.5-5 and 5.5–8 and 8.5–10.510B571.92
Tab.2  The liquefaction depths versus Kg value
Fig.9  Evaluation of factor of safety using conventional and ANN method
Fig.10  The structure of a MLP-type network
borehole numbersoil typetotal stresseffective stressN-SPTFS
Tab.3  data sets with inputs and output values with indicating the training and testing sets
soil typetotal stresseffective stressN-SPTFS
Tab.4  Inputs and output ranges for training data sets used for construction of ANN model
soil typetotal stresseffective stressN-SPTFS
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
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