Liquefaction prediction using support vector machine model based on cone penetration data
Pijush SAMUI
Liquefaction prediction using support vector machine model based on cone penetration data
A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance (qc) and cyclic stress ratio (CSR), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters (qc and maximum horizontal acceleration amax), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor (C) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.
earthquake / cone penetration test / liquefaction / support vector machine (SVM) / prediction
[1] |
Seed H B, Idriss I M. Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundation Divisions, 1971, 97(9): 1249–1273
|
[2] |
Seed H B, Tokimatsu K, Harder L F, Chung R. Influence of SPT procedures in soil liquefaction resistance evaluations. Journal of the Geotechnical Engineering Dividions, 1985, 111(12): 1425–1445
CrossRef
Google scholar
|
[3] |
Robertson P K, Campanella R G. Liquefaction potential of sands using the cone penetration test. Journal of the Geotechnical Engineering Dividions, 1985, 111(3): 384–403
CrossRef
Google scholar
|
[4] |
Skempton A W. Standard penetration test procedures and the effects in sands of overburden pressure, relative density, particle size, aging and overconsolidation. Geotechnique, 1986, 36(3): 425–447
CrossRef
Google scholar
|
[5] |
Seed H B, De Alba P M. Use of SPT and CPT for evaluating the liquefaction resistance of sands. In: Proceedings of In Situ’86, a Specialty Conference on Use of In Situ Tests in Geotechnical Engineering. ASCE, New York, 1986, 281–302
|
[6] |
Shibata T, Teparaksa W. Evaluation of liquefaction potentials of soils using cone penetration tests. Soil and Foundation, 1988, 28(2): 49–60
CrossRef
Google scholar
|
[7] |
Christian J T, Swiger W F. Statistics of liquefaction and SPT results. Journal of Geotechnical Engineering, 1975, 101(11): 1135–1150
|
[8] |
Haldar A, Tang W H. Probabilistic evaluation of liquefaction potential. Journal of Geotechnical Engineering, 1979, 104(2): 145–162
|
[9] |
Liao S S C, Veneziano D, Whitman R V. Regression models for evaluating liquefaction probability. Journal of Geotechnical Engineering, 1988, 114(4): 389–411
CrossRef
Google scholar
|
[10] |
Hsein Juang C, Yuan H, Lee D H, Ku C S. Assessing CPT-based methods for liquefaction evaluation with emphasis on the cases from the Chi-Chi, Taiwan, earthquake. Soil Dynamics and Earthquake Engineering, 2002, 22(3): 241–258
CrossRef
Google scholar
|
[11] |
Goh A T C. Seismic liquefaction potential assessed by neural networks. Journal of Geotechnical Engineering, 1994, 120(9): 1467–1480
CrossRef
Google scholar
|
[12] |
Goh A T C. Neural-netwok modeling of CPT seismic liquefaction data. Journal of Geotechnical Engineering, 1996, 122(1): 70–73
CrossRef
Google scholar
|
[13] |
Saka H, Ural D N. Liquefaction assessment by Artificial Neural Networks. Electronics journal of geotechnical engineering, 2002, 3
|
[14] |
Goh A T C. Probablistic neural network for evaluating seismic liquefaction potential. Canadian Geotechnical Journal, 2002, 39(1): 219–232
CrossRef
Google scholar
|
[15] |
Park D, Rilett, L R. Forecasting freeway link ravel times with a multi-layer feed forward neural network. Computer Aided Civil And Znfa Structure Engineering, 1999, 14: 358–367
|
[16] |
Kecman V. Learning and Soft Computing—Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Massachusetts, Cambridge, London, England: the MIT press, 2001
|
[17] |
Vapnik V. The Nature of Statistical Learning Theory. New York: Springer, 1995
|
[18] |
Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers. In: Hussler D, ed. 5th Annual ACM Workshop on COLT. Pittsburgh: ACM Press, 1992, 144–152
|
[19] |
Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273–297
CrossRef
Google scholar
|
[20] |
Gualtieri J A, Chettri S R, Cromp R F, Johnson L F. Support vector machine classifiers as applied to AVIRIS data. In: Proceedings of the Summaries of the 8th JPL Airbrone Earth Science Workshop. Nevada, February 8–11, 1999
|
[21] |
Vapnik V N. Statistical Learning Theory. New York: Wiley, 1998
|
[22] |
Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing 7. Institute of Electrical and Electronics Engineers, New York, 1997, 276–285
|
[23] |
Fletcher R. Practical Methods of Optimization. 2nd ed. Chichester, Newyork: Wiley, 1987
|
[24] |
Cristianini N, Shawe-Taylor J. An introduction to Support vector machine. London: Cambridge University press, 2000
|
[25] |
Sincero A P. Predicting Mixing Power Using Artificial Neural Network. EWRI World Water and Environmental, 2003
|
[26] |
Gunn R. Support vector machines for classification and regression. http://www.ecs.soton.ac.uk/ ~srg/ publications/ pdf/SVM.pdf, 2003
|
[27] |
MathWork, Inc. Matlab user’s manual, Version 5.3. Natick, 1999
|
/
〈 | 〉 |