Abstract
Liquefaction has caused many catastrophes during earthquakes in the past . The strain energy-based method is one of the modern methods used to estimate liquefaction potential. In this study, wide-ranging experimental data were gathered from cyclic tests and centrifuge modeling of liquefaction. A model was then developed based on the strain energy needed for liquefaction to occur using the group method of data handling and the gravitational search algorithm. Contributions of the effective variables were evaluated through a sensitivity analysis. To check the accuracy of the developed strain energy model, cyclic triaxial tests were conducted on sandy soil and silty sand specimens. Comparison of the energy required to initiate liquefaction in the tested soil specimens with values predicted by the developed model indicated high accuracy of the energy-based model. Subsequently, the accuracy of the energy model was assessed in field conditions using the amount of strain energy released by real earthquakes in various sites. The ability of the model to distinguish liquefied areas from non-liquefied ones confirms its accuracy in field conditions. Finally, the developed model was compared with some available relationships to estimate the strain energy required for liquefaction to occur.
Similar content being viewed by others
References
Alavi AH, Gandomi AH (2012) Energy-based numerical models for assessment of soil liquefaction. Geosci Front 3(4):541–555
Arulmoli K, Muraleetharan KK, Hosain MM, Fruth LS (1992) VELACS laboratory testing program. Soil Data Report, The Earth Technology Corporation, Irvine, Calif. Report to the National Science Foundation, Washington, DC
Baziar MH, Dobry R (1995) Residual strength and large-deformation potential of loose silty sands. J Geotech Eng ASCE 121(12):896–906
Baziar MH, Jafarian Y (2007) Assessment of liquefaction triggering using strain energy concept and ANN model: capacity energy. Soil Dyn Earthq Eng 27(12):1056–1072
Baziar MH, Sharafi H (2011) Assessment of silty sand liquefaction potential using hollow torsional tests—an energy approach. Soil Dyn Earthq Eng 31(7):857–865
Baziar MH, Jafarian Y, Shahnazari H, Movahed V, Tutunchian MA (2011) Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: an evolutionary approach. Comput Geosci 37(11):1883–1893
Butterfield KJ (2004) Seismic liquefaction trigger mechanisms. PhD Dissertation, Department of Civil Engineering, University of Canterbury
Caglar N, Arman H (2007) The applicability of neural networks in the determination of soil profiles. Bull Eng Geol Environ 66(3):295–301
Carraro JAH, Bandini P, Salgado R (2003) Liquefaction resistance of clean and nonplastic silty sands based on cone penetration resistance. J Geotech Geoenviron Eng ASCE 129(11):965–976
Chien LK, Oh YN, Chang CH (2002) Effects of fines content on liquefaction strength and dynamic settlement of reclaimed soil. Can Geotech J 39:254–265
Davis RO, Berrill JB (1998) Rational approximation of shear stress and strain based on downhole acceleration measurements. Int J Numer Anal Meth Geomech 22:603–619
Dief HM (2000) Evaluating the liquefaction potential of soils by the energy method in the centrifuge. PhD Dissertation, Department of Civil Engineering, Case Western Reserve University, Cleveland
Dief HM, Figueroa JL (2001) Liquefaction assessment by the energy method through centrifuge modeling. In: Zeng XW (ed) Proceedings of the NSF international workshop on earthquake simulation in geotechnical engineering. CWRU, Cleveland
Dobry R, Ladd RS, Yokel FY, Chung RM, Powell D (1982) Prediction of pore water pressure build-up and liquefaction of sands during earthquakes by the cyclic strain method. National Bureau of Standards, US Department of Commerce, US Governmental Printing Office, Building Science Series, Washington, DC
Figueroa JL, Saada AS, Liang L, Dahisaria NM (1994) Evaluation of soil liquefaction by energy principles. J Geotech Eng ASCE 120(9):1554–1569
Gandomi AH, Babanajad SK, Alavi AH, Farnam Y (2012) Novel approach to strength modeling of concrete under triaxial compression. J Mater Civ Eng 24(9):1132–1143
Goh AT, Zhang WG (2014) An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines. Eng Geol 170:1–10
Green RA (2001) Energy-based evaluation and remediation of liquefiable soils. PhD dissertation, Virginia Polytechnic Institute and State University, Blacksburg
Hazirbaba K, Rathje EM (2009) Pore pressure generation of silty sands due to induced cyclic shear strains. J Geotech Geoenviron Eng ASCE 135(12):1892–1905
Hwang HS (2006) Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication. Comput Ind Eng 50(4):450–457
Ishihara K (1996) Soil behavior in earthquake geotechnics. Oxford Science Publications
Ishihara K, Muroi T, Towhata I (1989) In-situ pore water pressures and ground motions during the 1987 Chiba-Toho-Oki earthquake. Soils Found 29(4):75–90
Jafarian Y, Javdanian H (2017) Dynamic behavior of calcareous sands. Bull Earthq Sci Eng 4(1):27–36
Jafarian Y, Sadeghi Abdollahi A, Vakili R, Baziar MH (2010) Probabilistic correlation between laboratory and field liquefaction potentials using relative state parameter index. Soil Dyn Earthq Eng 30:1061–1072
Jafarian Y, Sadeghi Abdollahi A, Vakili R, Baziar MH, Noorzad A (2011) On the efficiency and predictability of strain energy for the evaluation of liquefaction potential: a numerical study. Comput Geotech 38(6):800–808
Jafarian Y, Towhata I, Baziar MH, Noorzad A, Bahmanpour A (2012) Strain energy based evaluation of liquefaction and residual pore water pressure in sands using cyclic torsional shear experiments. Soil Dyn Earthq Eng 35:13–28
Jafarian Y, Haddad A, Javdanian H (2014) Predictive model for normalized shear modulus of cohesive soils. Acta Geodyn Geomater 11(1):89–100
Jafarian Y, Haddad A, Javdanian H (2015) Comparing the shear stiffness of calcareous and silicate sands under dynamic and cyclic straining. 7th Int Conf Seismol Earthq Eng (SEE7), 18 May, Tehran
Jafarian Y, Javdanian H, Haddad A (2016a) Comparing dynamic behavior of Hormuz calcareous and Babolsar siliceous sands under identical conditions. Bull Earthq Sci Eng 3(3):1–10
Jafarian Y, Haddad A, Javdanian H (2016b) Estimating the shearing modulus of Boushehr calcareous sand using resonant column and cyclic triaxial experiments. Modares Civil Eng J 15(4):9–19
Javdanian H (2017) Assessment of shear stiffness ratio of cohesionless soils using neural modeling. Model Earth Syst Environ 3(3):1045–1053
Javdanian H (2017) The effect of geopolymerization on the UCS of stabilized fine-grained soils. Int J Eng Trans B Appl 30(11):1508–1517
Javdanian H, Hoseini O (2016) Evaluating performance of the existing relationships and models to predict liquefaction-induced lateral spreading. 5th Int Conf Geotech Eng Soil Mech, 15 November, Tehran
Javdanian H, Seidali M (2016) Evaluating liquefaction induced lateral spreading. 5th Int Conf Geotech Eng Soil Mech, 15 November, Tehran
Javdanian H, Haddad A, Mehrzad B (2012) Experimental and numerical investigation of the bearing capacity of adjacent footings on reinforced soil. Electronic J Geotech Eng 17(R):2597–2617
Javdanian H, Haddad A, Jafarian A (2015a) Evaluation of dynamic behavior of fine-grained soils using group method of data handling. Transp Infrastruct Eng 1(3):77–92. http://jtie.journals.semnan.ac.ir/article_318_en.html
Javdanian H, Jafarian Y, Haddad A (2015b) Predicting damping ratio of fine-grained soils using soft computing methodology. Arab J Geosci 8(6):3959–3969
Javdanian H, Heidari A, Kamgar R (2017) Energy-based estimation of soil liquefaction potential using GMDH algorithm. Iran J Sci Technol Trans Civ Eng 41(3):283–295
Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the Su-NSPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104:144–155
Kanagalingam T (2006) Liquefaction Resistance of Granular Mixes Based on Contact Density and Energy Considerations. PhD Dissertation, The State University of New York at Buffalo, Buffalo
Kaveh A, Hamze-Ziabari SM, Bakhshpoori T (2016) Patient rule-induction method for liquefaction potential assessment based on CPT data. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-016-0990-3
Kokusho T, Mimori Y (2015) Liquefaction potential evaluations by energy-based method and stress-based method for various ground motions. Soil Dyn Earthq Eng 75:130–146
Ladd RS (1978) Preparing test specimens using undercompaction. Geotech Test J 1(1):16–23
Lee KL, Fitton JA (1968) Factors affecting the cyclic loading strength of soil. Vibration Effects of Earthquakes on Soils and Foundation, ASTM STP 450, American Society for Testing and Materials. 71–95
Lee KL, Seed HB (1967) Cyclic stress conditions causing liquefaction of sand. J Soil Mech Found Div ASCE 93(SM1):47–70
Li X, Zhong D, Ren B, Fan G, Cui B (2017) Prediction of curtain grouting efficiency based on ANFIS. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1039-y
Liang L (1995) Development of an energy method for evaluating the liquefaction potential of a soil deposit. PhD Dissertation, Department of Civil Engineering, Case Western Reserve University, Cleveland
Madala HR, Ivakhnenko AG (1994) Inductive learning algorithms for complex systems modeling. CRC Press, Boca Raton
Marandi SM, Javdanian H (2012) Laboratory studies on bearing capacity of strip interfering shallow foundations supported by geogrid-reinforced sand. Adv Mater Res 472:1856–1869
Mehrzad B, Haddad A, Jafarian Y (2016) Centrifuge and numerical models to investigate liquefaction-induced response of shallow foundations with different contact pressures. Int J Civ Eng 14(2):117–131
Mohammadi SD, Naseri F, Alipoor S (2015) Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran. Bull Eng Geol Environ 74(3):827–843
Naeini SA, Baziar MH (2004) Effect of fines content on steady-state strength of mixed and layered samples of a sand. Soil Dyn Earthq Eng 24:181–187
Najafzadeh M, Azamathulla HM (2013) Neuro-fuzzy GMDH systems to predict the scour pile groups due to waves. J Comput Civ Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000376
Najafzadeh M, Lim SY (2014) Application of improved neuro-fuzzy GMDH to predict scour downstream of sluice gates. Earth Sci Inform 8(1):187–196
Najafzadeh M, Tafarojnoruz A (2016) Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environ Earth Sci 75(2):157
Najafzadeh M, Barani GA, Hessami Kermani MR (2013) GMDH network based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106
Papathanassiou G, Seggis K, Pavlides S (2011) Evaluating earthquake-induced liquefaction in the urban area of Larissa, Greece. Bull Eng Geol Environ 70(1):79–88
Polito CP, Martin JR (2001) Effects of nonplastic fines on the liquefaction resistance of sands. J Geotech Geoenviron Eng ASCE 127(5):408–415
Rahman MZ, Siddiqua S (2017) Evaluation of liquefaction-resistance of soils using standard penetration test, cone penetration test, and shear-wave velocity data for Dhaka, Chittagong, and Sylhet cities in Bangladesh. Environ Earth Sci 76(5):207
Rashedi E, Nezamabadipour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248
Rokoff MD (1999) The influence of grain-size characteristics in determining the liquefaction potential of a soil deposit by the energy method. MSc Dissertation, Department of Civil Engineering, Case Western Reserve University, Cleveland
Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 97:1249–1273
Seed HB, Lee KL (1966) Liquefaction of saturated sands during cyclic loading. J soil Mech found div ASCE 92(SM2);105–134
Shahin MA, Maier HB, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civ Eng ASCE 18(2):105–114
Sonmez B, Ulusay R (2008) Liquefaction potential at Izmit Bay: comparison of predicted and observed soil liquefaction during the Kocaeli earthquake. Bull Eng Geol Environ 67(1):1–9
Takashi O, Hidetomo I, Tetsuya M, Kazunori N (1998) Orthogonal and successive projection methods for the learning of neurofuzzy GMDH. Inf Sci 110:5–24
Tao M (2003). Case history verification of the energy method to determine the liquefaction potential of soil deposits. PhD Dissertation, Department of Civil Engineering, Case Western Reserve University, Cleveland
Thevanayagam S (1998) Effect of fines and confining stress on undrained shear strength of silty sands. J Geotech Geoenviron Eng ASCE 124(6):479–491
Towhata I (1986) Discussion to “energy dissipation and seismic liquefaction of sands: revised model” by Berrill JB, Davis RO. Soils Found 26(1):134–135
Towhata I, Ishihara K (1985) Shear work and pore water pressure in undrained shear. Soils Found 25(3):73–84
Whitman RV (1971) Resistance of soil to liquefaction and settlement. Soils Found 11(4):59–68
Xue X, Yang X (2016) Seismic liquefaction potential assessed by support vector machines approaches. Bull Eng Geol Environ 75(1):153–162
Youd T, Idriss I, Andrus R, Arango I, Castro G, Christian J, Dobry R, Finn W, Harder LJ, Hynes M, Ishihara K, Koester J, Liao S, Marcuson W, Martin G, Mitchell J, Moriwaki Y, Power M, Robertson P, Seed R, Stokoe K II (2001) Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J Geotech Geoenviron Eng 127(10):817–833
Zeghal M, Elgamal AW, Tang HT, Stepp JC (1995) Lotung downhole array. II: evaluation of soil nonlinear properties. J Geotech Eng ASCE 121(4):363–378
Zhang WG, Goh ATC (2013) Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Comput Geotech 48:82–95
Zhang W, Goh AT (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7(1):45–52
Zhuang H, Chen G, Hu Z, Qi C (2016) Influence of soil liquefaction on the seismic response of a subway station in model tests. Bull Eng Geol Environ 75(3):1169–1182
Acknowledgements
This work has been financially supported by the research deputy of Shahrekord University (grant number 95GRN1M39422). This support is gratefully acknowledged. Special thanks are also extended to engineers from the Dez Shaloudeh Azma Co., Iran, for providing laboratory facilities.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Javdanian, H. Evaluation of soil liquefaction potential using energy approach: experimental and statistical investigation. Bull Eng Geol Environ 78, 1697–1708 (2019). https://doi.org/10.1007/s10064-017-1201-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10064-017-1201-6