Site effect assessment using microtremor measurement, equivalent linear method, and artificial neural network (case study: Babol, Iran)

Site effect assessment is an important procedure for a reliable site-specific hazard assessment. Fundamental frequency is a very important factor that must be considered in a construction site for examining the potential damage resulting from earthquake. In the two last decades, the microtremor H/V spectral ratio method has been widely used for site effect studies. Microtremor measurement is fast, applicable, and cost-effective. Microtremor measurement was undertaken at 60 stations in the Babol, north of Iran, during 2011 and 2012. Regarding Nakamura’s method, H/V spectral ratios, fundamental frequency, and amplification factor were calculated for all microtremor stations. The obtained results were controlled with SESAME guidelines. In order to assess the accuracy of the microtremor measurements and its application in site effect evaluation, a preliminary site response modeling was carried out using the equivalent linear methods at some stations. More than 18 boreholes in the study area were modeled as multilayer column, overlaid on bedrock. Also, the artificial neural networks (ANN) with different inputs including thickness, type of soil, unit weight, shear wave velocity, and max shear modulus of soil layer were trained. Then, the trained ANN predicts the fundamental frequency, and the output results were compared with microtremor measurement. The results showed that microtremor measurement approach provides an acceptable means of site effect evaluation in the study area.

[1]  L. Matias,et al.  Seismic behaviour estimation of thin alluvium layers using microtremor recordings , 1996 .

[2]  M. Fnais,et al.  Microtremor measurements in Yanbu city of Western Saudi Arabia: A tool for seismic microzonation , 2010 .

[3]  Lars Bo Ibsen,et al.  Neural network-based model for landslide susceptibility and soil longitudinal profile analyses: Two case studies , 2011 .

[4]  Miguel P. Romo,et al.  Modeling ground motion in Mexico City using artificial neural networks , 2003 .

[5]  Y Nakamura,et al.  A METHOD FOR DYNAMIC CHARACTERISTICS ESTIMATION OF SUBSURFACE USING MICROTREMOR ON THE GROUND SURFACE , 1989 .

[6]  A. Barari,et al.  Influence of Uniform Suction/Injection on Heat Transfer of MHD Hiemenz Flow in Porous Media , 2012 .

[7]  Roberto Paolucci,et al.  Assessment of Seismic Site Effects in 2-D Alluvial Valleys Using Neural Networks , 2000 .

[8]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[9]  Andrej Gosar,et al.  Microtremor HVSR study for assessing site effects in the Bovec basin (NW Slovenia) related to 1998 Mw5.6 and 2004 Mw5.2 earthquakes , 2007 .

[10]  H. Bararnia,et al.  Natural convection between a circular enclosure and an elliptic cylinder using Control Volume based Finite Element Method , 2012 .

[11]  Davood Domiri Ganji,et al.  ANALYTICAL SOLUTION OF THE MAGNETO-HYDRODYNAMIC FLOW OVER A NONLINEAR STRETCHING SHEET , 2009 .

[12]  Davood Domiri Ganji,et al.  Homotopy perturbation method for motion of a spherical solid particle in plane couette fluid flow , 2011, Comput. Math. Appl..

[13]  Michael Blumenstein,et al.  Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model , 2011 .

[14]  Davood Domiri Ganji,et al.  Numerical simulation of joule heating phenomenon using meshless RBF-DQ method , 2010 .

[15]  Sesame Team,et al.  Sesame project - Deliverable D23.12 - WP12 - GUIDELINES FOR THE IMPLEMENTATION OF THE H/V SPECTRAL RATIO TECHNIQUE ON AMBIENT VIBRATIONS MEASUREMENTS, PROCESSING AND INTERPRETATION , 2004 .

[16]  Leonard Ziemiański,et al.  Hybrid neural network/finite element modelling of wave propagation in infinite domains , 2003 .

[17]  Mohammad Hassan Baziar,et al.  Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity Energy , 2007 .

[18]  Davood Domiri Ganji,et al.  Optimal location of a pair heat source-sink in an enclosed square cavity with natural convection through PSO algorithm☆ , 2011 .

[19]  Davood Domiri Ganji,et al.  Local RBF-DQ method for two-dimensional transient heat conduction problems☆ , 2010 .

[20]  Efficiency of horizontal-to-vertical spectral ratio (HVSR) in defining the fundamental frequency in Muscat Region, Sultanate of Oman: a comparative study , 2014, Arabian Journal of Geosciences.

[21]  Mahdi Bayat,et al.  Visco-elastic MHD flow of Walters liquid B fluid and heat transfer over a non-isothermal stretching sheet , 2011 .

[22]  D. Ganji,et al.  Comparative study of large eddy simulation of film cooling using a dynamic global-coefficient subgrid scale eddy-viscosity model with RANS and Smagorinsky Modeling ☆ , 2011 .

[23]  Ju Jia Zou,et al.  Microtremor measurements of rolling compacted ground , 2012 .

[24]  Peter Bormann,et al.  New Relationships between Vs, Thickness of Sediments, and Resonance Frequency Calculated by the H/V Ratio of Seismic Noise for the Cologne Area (Germany) , 2002 .

[25]  A. Tokuhiro,et al.  Entropy generation in a transitional boundary layer region under the influence of freestream turbulence using transitional RANS models and DNS , 2013 .

[26]  S. Hosseini,et al.  Comparative study on the equivalent linear and the fully nonlinear site response analysis approaches , 2012 .

[27]  T. G. Sitharam,et al.  Seismic microzonation of Bangalore, India , 2008 .

[28]  Francisco J. Chávez-García,et al.  Site effect evaluation using spectral ratios with only one station , 1993, Bulletin of the Seismological Society of America.

[29]  Tienfuan Kerh,et al.  Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system , 2005, Eng. Appl. Artif. Intell..

[30]  P. Bormann,et al.  Low cost seismic microzonation using microtremor data: an example from Delhi, India , 2004 .

[31]  Abd el-aziz Khairy Abd el-aal,et al.  Fundamental site frequency estimation at New Domiat city, Egypt , 2012, Arabian Journal of Geosciences.

[32]  M. Baziar,et al.  Using Neural Network for Prediction of the Dynamic Period and Amplification Factor of Soil for Microzonation , 2009 .

[33]  Lars Bo Ibsen,et al.  Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran , 2011 .

[34]  Habibolla Latifizadeh Solution of the Falkner–Skan wedge flow by HPM–Pade’ method , 2011 .

[35]  Sadegh Rezaei,et al.  Evaluation of site response characteristics using microtremors , 2013 .

[36]  Awadhesh Kumar Shukla,et al.  Site amplification studies towards seismic microzonation in Jabalpur urban area, central India , 2011 .

[37]  金井 淸 19. Relation between the Nature of Surface Layer and the Amplitude of Earthquake Motions. II. , 1952 .

[38]  Jim Mori,et al.  Source parameters for small events associated with the 1986 North Palm Springs, California, earthquake determined using empirical Green functions , 1990 .

[39]  李幼升,et al.  Ph , 1989 .

[40]  P. Dominique,et al.  On the use of microtremor recordings in seismic microzonation , 1998 .

[41]  Davood Domiri Ganji,et al.  Solution of the Falkner-Skan wedge flow by HPM-Pade' method , 2012, Adv. Eng. Softw..

[42]  Qc site dependence in the Granada basin (southern Spain) , 1991, Bulletin of the Seismological Society of America.

[43]  S. Kramer Geotechnical Earthquake Engineering , 1996 .

[44]  Davood Domiri Ganji,et al.  Natural convection heat transfer in a nanofluid filled semi-annulus enclosure ☆ , 2012 .

[45]  Abbas Abbaszadeh Shahri,et al.  Evaluation of a nonlinear seismic geotechnical site response analysis method subjected to earthquake vibrations (case study: Kerman Province, Iran) , 2011 .

[46]  Julián M. Londoño,et al.  On the applicability of neural networks for soil dynamic amplification analysis , 2001 .