Abstract
Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this chapter, a modification MARS approach based on logistic regression (LR) MARS_LR is used to evaluate seismic liquefaction potential based on actual field records. Three different MARS_LR models were used to analyze three different field liquefaction databases, and the results are compared with BPNN.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andrus RD, Stokoe KH (2000) Liquefaction resistance of soils from shear-wave velocity. J Geotech Geoenviron Eng 126(11):1015–1025
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks, Monterey, CA
Cetin KO, Seed RB, Der Kiureghian AK, Tokimatsu K, Harder LF Jr, Kayen RE, Moss RES (2004) Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential. J Geotech Geoenviron Eng ASCE 130(12):1314–1340
Chern SG, Lee CY, Wang CC (2008) CPT-based liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol 16(2):139–148
Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84(405):165–175
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141
Goh ATC (2002) Probabilistic neural network for evaluating seismic liquefaction potential. Can Geotech J 39:219–232
Juang CH, Chen CJ (1999) CPT-based liquefaction evaluation using artificial neural networks. Comput Aided Civ Infrastruct Eng 14(3):221–229
Juang CH, Yuan H, Lee DH, Lin PS (2003) Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. J Geotech Geoenviron Eng ASCE 129(1):66–80
Lai SY, Hsu SC, Hsieh MJ (2004) Discriminant model for evaluating soil liquefaction potential using cone penetration test data. J Geotech Geoenviron Eng ASCE 130(12):1271–1282
Law KT, Cao YL, He GN (1990) An energy approach for assessing seismic liquefaction potential. Can Geotech J 27:320–329
Liao SC, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng ASCE 114(4):389–411
Marchetti S (1982) Detection of liquefiable sand layers by means of quasi-static penetration tests. In: Proceedings of the 2nd European symposium on penetration testing, vol 2, Amsterdam, pp 458–482
Moss RES, Seed RB, Kayen RE, Stewart JP, Der Kiureghian AK, Cetin KO (2006) CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential. J Geotech Geoenviron Eng ASCE 132(8):1032–1051
Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459
Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 97(9):1249–1273
Seed HB, Tokimatsu K, Harder LF, Chung R (1985) Influence of SPT procedures in soil liquefaction resistance evaluations. J Geotech Eng ASCE 111(12):861–878
Specht D (1990) Porbabilistic neural networks. Neural Netw 3:109–118
Stark TD, Olson SM (1995) Liquefaction resistance using CPT and field case histories. J Geotech Eng ASCE 121(12):856–869
Tosun H, Seyrek E, Orhan A, Savas H, Turkoz M (2011) Soil liquefaction potential in Eskisehir, NW Turkey. Nat Hazards Earth Syst Sci 11:1071–1082
Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems, vol 9. MIT Press, Cambridge, MA, pp 281–287
Youd TL et al (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 ASCE 127(10):817–833
Zhang GQ (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern. Part C Appl Rev 30(4):451–462
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Science Press and Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zhang, W. (2020). MARS_LR Use in Assessment of Soil Liquefaction. In: MARS Applications in Geotechnical Engineering Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7422-7_12
Download citation
DOI: https://doi.org/10.1007/978-981-13-7422-7_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7421-0
Online ISBN: 978-981-13-7422-7
eBook Packages: EngineeringEngineering (R0)