Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines

Abstract Soil liquefaction is one of the most complicated phenomena to assess in geotechnical earthquake engineering. The procedures that have been developed to determine the liquefaction potential of sandy soil deposits can be categorized into three main groups: stress-based, strain-based, and energy-based procedures. The main advantage of the energy-based approach over the other two methods is the fact that it considers the effects of strain and stress concurrently unlike the stress or strain-based methods. Several liquefaction evaluation procedures have been developed, relating the capacity energy to initial soil parameters such as the relative density, initial effective confining pressure, fine contents and soil textural properties. Analyses have been carried out on a total of 302 previously published tests using a nonparametric regression procedure known as multivariate adaptive regression splines (MARS), to assess the capacity energy required to trigger liquefaction in sand and silty sands. The capacity energies estimated by this proposed model compare favorably with the centrifuge test data sets used for validation purpose.

[1]  Anthony T. C. Goh,et al.  An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines , 2014 .

[2]  Liqun Liang,et al.  EVALUATION OF SOIL LIQUEFACTION BY ENERGY PRINCIPLES , 1994 .

[3]  Robert V. Whitman,et al.  Resistance of Soil to Liquefaction and Settlement , 1971 .

[4]  Amir Hossein Alavi,et al.  Formulation of flow number of asphalt mixes using a hybrid computational method , 2011 .

[5]  Mohammad Hassan Baziar,et al.  EFFECT OF FINES CONTENT ON STEADY-STATE STRENGTH OF MIXED AND LAYERED SAMPLES OF A SAND , 2004 .

[6]  Russell A. Green,et al.  Energy-Based Evaluation and Remediation of Liquefiable Soils , 2004 .

[7]  Nii O. Attoh-Okine,et al.  Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling , 2009 .

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

[9]  Richard J. Bathurst,et al.  Comparison of numerical and analytical solutions for reinforced soil wall shaking table tests , 2011 .

[10]  Wengang Zhang,et al.  Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns , 2014 .

[11]  James R. Martin,et al.  EFFECTS OF NONPLASTIC FINES ON THE LIQUEFACTION RESISTANCE OF SANDS , 2001 .

[12]  Habib Shahnazari,et al.  Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach , 2011, Comput. Geosci..

[13]  A. Papadopoulou,et al.  The effect of non-plastic fines on the liquefaction resistance of sands , 2013 .

[14]  A. Gandomi,et al.  Energy-based numerical models for assessment of soil liquefaction , 2012 .

[15]  Ali Lashkari,et al.  Prediction of the shaft resistance of nondisplacement piles in sand , 2013 .

[16]  Shun-Chieh Hsieh,et al.  Energy-based probabilistic evaluation of soil liquefaction , 2005 .

[17]  Sia Nemat-Nasser,et al.  A unified approach to densification and liquefaction of cohesionless sand in cyclic shearing , 1979 .

[18]  I. M. Idriss,et al.  SIMPLIFIED PROCEDURE FOR EVALUATING SOIL LIQUEFACTION POTENTIAL , 1971 .

[19]  Ali Firat Cabalar,et al.  Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering , 2012 .

[20]  Amir Hossein Gandomi,et al.  Permanent deformation analysis of asphalt mixtures using soft computing techniques , 2011, Expert Syst. Appl..

[21]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines for analysis of geotechnical engineering systems , 2013 .

[22]  V. C. Xenaki,et al.  Liquefaction resistance of sand-silt mixtures: an experimental investigation of the effect of fines , 2003 .

[23]  Liqun Liang.  Development of an energy method for evaluating the liquefaction potential of a soil deposit , 1995 .

[24]  Zhe Luo,et al.  New models for probability of liquefaction using standard penetration tests based on an updated database of case histories , 2012 .

[25]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[26]  C. Hsein Juang,et al.  Probabilistic Framework for Liquefaction Potential by Shear Wave Velocity , 2001 .

[27]  Jonathan P. Stewart,et al.  CPT-Based Probabilistic and Deterministic Assessment of In Situ Seismic Soil Liquefaction Potential - eScholarship , 2006 .

[28]  C. H. Juang,et al.  Reliability-based method for assessing liquefaction potential of soils , 1999 .

[29]  Arthur C. Heidebrecht,et al.  Energy dissipation and seismic liquefaction in sands , 1982 .

[30]  Susumu Yasuda,et al.  Sand Liquefaction In Hollow Cylinder Torsion Under Irregular Excitation , 1975 .

[31]  Pijush Samui,et al.  Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach , 2012 .

[32]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[33]  Ross W. Boulanger,et al.  Probabilistic Standard Penetration Test-Based Liquefaction-Triggering Procedure , 2012 .

[34]  Pijush Samui,et al.  Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay , 2012, Int. J. Appl. Metaheuristic Comput..