Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories
Publication: Journal of Performance of Constructed Facilities
Volume 33, Issue 3
Abstract
Earthquakes have always attracted civil and geotechnical engineers’ attention, especially when it comes to the liquefaction potential of soil. This paper investigates the feasibility of classifier based on stochastic gradient boosting (SGB) to explore the liquefaction potential from actual cone penetration test (CPT) and standard penetration test (SPT) field data. SGB is composed of many classification and regression trees which meet the mechanism of ensemble learning and show strong predictive power compared with conventional statistical learning models in several engineering applications. The binary classifier was built by the database gathered from CPT and SPT filed data for predicting the non-liquefaction or liquefaction of soil, the SGB hyperparameters are optimized by grid search method with tenfolds cross validation methods. Three performance metric, namely Cohen’s Kappa coefficient, classification accuracy rate and receiver operating characteristic curve, are used to evaluate the predictive performance of SGB approaches. With CPT and SPT test sets, highest classification accuracy rate of 88.62% and 95.45%, respectively, are achieved with SGB. It is confirmed that the SGB can be applied to characterize the complex relationship between the liquefaction potential and different soil and seismic parameters with great efficiency. Further, relative importance of influencing variables for each model are investigated and demonstrated that the SGB predictor is more sensitive to the indicators of initial soil friction angle for SPT data whereas cone tip resistance for CPT data.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors appreciate the support of the National Natural Science Foundation Project of China (Grant No. 41807259), the State Key Laboratory Breeding Base for Mining Disaster Prevention and Control (Grant No. MDPC201608), the Natural Science Foundation of Hunan Province (Grant No. 2018JJ3693), the China Postdoctoral Science Foundation funded project (Grant No. 2017M622610) and the Sheng Hua Lie Ying Program of Central South University (Principle Investigator: Dr. Jian Zhou).
References
Bradley, A. P. 1997. “The use of the area under the ROC curve in the evaluation of machine learning algorithms.” Pattern Recognit. 30 (7): 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2.
Bray, J. D., et al. 2004. “Subsurface characterization of ground failure sites in Adapazari Turkey.” J. Geotech. Geoenviron. Eng. 130 (7): 673–685. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:7(673).
Chen, T., and C. Guestrin. 2016. “Xgboost: A scalable tree boosting system.” Accessed March 15, 2018. http://arxiv.org/abs/1603.02754.
Cox, B. R., et al. 2013. “Liquefaction at strong motion stations and in Urayasu City during the 2011 Tohoku-Oki earthquake.” Supplement, Earthquake Spectra EERI 29 (S1): S55–S80. https://doi.org/10.1193/1.4000110.
Friedman, J. H. 2001. “Greedy function approximation: A gradient boosting machine.” Ann. Stat. 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
Friedman, J. H. 2002. “Stochastic gradient boosting.” Comput. Stat. Data Anal. 38 (4): 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2.
Goh, A. T. 1996. “Neural-network modeling of CPT seismic liquefaction data.” J. Geotech. Eng. 122 (1): 70–73. https://doi.org/10.1061/(ASCE)0733-9410(1996)122:1(70).
Goh, A. T., and S. Goh. 2007. “Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data.” Comput. Geotech. 34 (5): 410–421. https://doi.org/10.1016/j.compgeo.2007.06.001.
Hanna, A. M., D. Ural, and G. Saygili. 2007. “Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data.” Soil Dyn. Earthquake Eng. 27 (6): 521–540. https://doi.org/10.1016/j.soildyn.2006.11.001.
Hastie, T., R. Tibshirani, and J. H. Friedman. 2001. Vol. 1 of The elements of statistical learning, 339. New York: Springer.
Hoang, N. D., and D. T. Bui. 2018. “Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: A multi-dataset study.” Bull. Eng. Geol. Environ. 77 (1): 191–204. https://doi.org/10.1007/s10064-016-0924-0.
Hu, J. L., X. W. Tang, and J. N. Qiu. 2016. “Analysis of the influences of sampling bias and class imbalance on performances of probabilistic liquefaction models.” Int. J. Geomech. 17 (6): 04016134. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000808.
Idriss, I. M., and R. W. Boulanger. 2006. “Semi-empirical procedures for evaluating liquefaction potential during earthquakes.” Soil Dyn. Earthquake Eng. 26 (2): 115–130. https://doi.org/10.1016/j.soildyn.2004.11.023.
Idriss, I. M., and R. W. Boulanger. 2014. CPT and SPT based liquefaction triggering procedures. London: Dept. of Civil and Environmental Engineering.
Jiang, L., P. Kong, J. Shu, and K. Fan. 2019a. “Numerical analysis of support designs based on a case study of a longwall entry.” Rock Mech. Rock Eng. https://doi.org/10.1007/s00603-018-1728-2.
Jiang, L., Q. S. Wu, Q. L. Wu, P. Wang, Y. Xue, P. Kong, and B. Gong. 2019b. “Fracture failure analysis of hard and thick key layer and its dynamic response characteristics.” Eng. Fail. Anal. 98: 118–130. https://doi.org/10.1016/j.engfailanal.2019.01.008.
Juang, C. H., H. Yuan, D. H. Lee, and P. S. Lin. 2003. “Simplified cone penetration test-based method for evaluating liquefaction resistance of soils.” J. Geotech. Geoenviron. Eng. 129 (1): 66–80. https://doi.org/10.1061/(ASCE)1090-0241(2003)129:1(66).
Kaveh, A., S. M. Hamze-Ziabari, and T. Bakhshpoori. 2018. “Patient rule-induction method for liquefaction potential assessment based on CPT data.” Bull. Eng. Geol. Environ. 77 (2): 849–865. https://doi.org/10.1007/s10064-016-0990-3.
Kohavi, R. 1995. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” In Vol. 2 of Proc., 14th Int. Joint Conf. on Artificial Intelligence IJCAI’ 95, 1137–1143. San Francisco: Morgan Kaufmann.
Kohestani, V. R., M. Hassanlourad, and A. Ardakani. 2015. “Evaluation of liquefaction potential based on CPT data using random forest.” Nat. Hazards 79 (2): 1079–1089. https://doi.org/10.1007/s11069-015-1893-5.
Kuhn, M. 2008. “Building predictive models in R using the caret package.” J. Stat. Software 28 (5): 1–26. https://doi.org/10.18637/jss.v028.i05.
Kuhn, M., and K. Johnson. 2013. Applied predictive modeling. New York: Springer.
Lai, S. Y., W. J. Chang, and P. S. Lin. 2006. “Logistic regression model for evaluating soil liquefaction probability using CPT data.” J. Geotech. Geoenviron. Eng. 132 (6): 694–704. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:6(694).
Lai, S. Y., S. C. Hsu, and M. J. Hsieh. 2004. “Discriminant model for evaluating soil liquefaction potential using cone penetration test data.” J. Geotech. Geoenviron. Eng. 130 (12): 1271–1282. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:12(1271).
Li, X. B., J. Zhou, S. F. Wang, and B. Liu. 2017. “Review and practice of deep mining for solid mineral resources.” Chin. J. Nonferrous Metals 27 (6): 1236–1262. https://doi.org/10.19476/j.ysxb.1004.0609.2017.06.021.
Lu, J., D. Lu, X. Zhang, Y. Bi, K. Cheng, M. Zheng, and X. Luo. 2016. “Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.” Biochimica et Biophysica Acta (BBA)-General Subjects 1860 (11): 2664–2671. https://doi.org/10.1016/j.bbagen.2016.05.019.
Moss, R. E., R. B. Seed, R. E. Kayen, J. P. Stewart, A. Der Kiureghian, and K. O. Cetin. 2006. “CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential.” J. Geotech. Geoenviron. Eng. 132 (8): 1032–1051. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:8(1032).
Oommen, T., L. G. Baise, and R. Vogel. 2010. “Validation and application of empirical liquefaction models.” J. Geotech. Geoenviron. Eng. 136 (12): 1618–1633. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000395.
Pal, M. 2006. “Support vector machines-based modelling of seismic liquefaction potential.” Int. J. Numer. Anal. Methods Geomech. 30 (10): 983–996. https://doi.org/10.1002/nag.509.
Qi, C., A. Fourie, G. Ma, X. Tang, and X. Du. 2017. “Comparative study of hybrid artificial intelligence approaches for predicting hangingwall stability.” J. Comput. Civ. Eng. 32 (2): 04017086. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000737.
R Core Team. 2017. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Ridgeway, G. 2007. “Generalized boosted models: A guide to the gbm package.” Accessed June 10, 2017. http://cran.r-project.org/web/packages/gbm/index.html.
Samui, P. 2007. “Seismic liquefaction potential assessment by using relevance vector machine.” Earthuqake. Eng. Eng. Vibr. 6 (4): 331–336. https://doi.org/10.1007/s11803-007-0766-7.
Seed, H. B., and I. M. Idriss. 1971. “Simplified procedure for evaluating soil liquefaction potential.” J. Soil Mech. Found. Div. 97 (9): 1249–1273.
Snoek, J., H. Larochelle, and R. P. Adams. 2012. “Practical Bayesian optimization of machine learning algorithms.” In Proc., 26th Int. Conf. on Advances in Neural Information Processing Systems (NIPS’12). Edited by P. Bartlett, F. Pereira, C. Burges, L. Bottou, and K. Weinberger, 2960–2968. Cambridge, MA: MIT Press.
Touzani, S., J. Granderson, and S. Fernandes. 2018. “Gradient boosting machine for modeling the energy consumption of commercial buildings.” Energy Build. 158: 1533–1543. https://doi.org/10.1016/j.enbuild.2017.11.039.
Wang, Z. W., D. P. Zhao, X. Liu, C. Chen, and X. B. Li. 2017. “P and S wave attenuation tomography of the Japan subduction zone.” Geochem. Geophys. Geosyst. 18 (4): 1688–1710. https://doi.org/10.1002/2017GC006800.
Xue, X., and X. Yang. 2013. “Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction.” Nat. Hazards 67 (2): 901–917. https://doi.org/10.1007/s11069-013-0615-0.
Yazdi, J. S., F. Kalantary, and H. S. Yazdi. 2012. “Investigation on the effect of data imbalance on prediction of liquefaction.” Int. J. Geomech. 13 (4): 463–466. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000217.
Zhou, J., X. B. Li, and X. Z. Shi. 2012. “Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines.” Saf. Sci. 50 (4): 629–644. https://doi.org/10.1016/j.ssci.2011.08.065.
Zhou, J., X. B. Li, and H. S. Mitri. 2015. “Comparative performance of six supervised learning methods for the development of models of pillar stability.” Nat. Hazards 79 (1): 291–316. https://doi.org/10.1007/s11069-015-1842-3.
Zhou, J., X. B. Li, and H. S. Mitri. 2016a. “Classification of rockburst in underground projects: comparison of ten supervised learning methods.” J. Comput. Civ. Eng. 50 (4): 629–644. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553.
Zhou, J., X. Chen, M. Z. Wang, E. M. Li, H. Chen, and X. Z. Shi. 2019. “Classification of seismic-liquefaction potential using Friedman’s stochastic gradient boosting based on the cone penetration test data.” In Transportation and geotechniques: Materials, sustainability and climate, edited by M. Barman, M. Zaman, and J. R. Chang, 67–78. Cham, Switzerland: Springer.
Zhou, J., X. Z. Shi, K. Du, X. Y. Qiu, X. B. Li, and H. S. Mitri. 2017. “Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel.” Int. J. Geomech. 79 (1): 291–316. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000817.
Zhou, J., X. Z. Shi, R. D. Huang, X. Y. Qiu, and C. Chen. 2016b. “Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines.” Trans. Nonferrous Met. Soc. China 26 (7): 1938–1945. https://doi.org/10.1016/S1003-6326(16)64312-1.
Zhou, J., X. Z. Shi, and X. B. Li. 2016c. “Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining.” J. Vibr. Control 22 (19): 3986–3997. https://doi.org/10.1177/1077546314568172.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Mar 30, 2018
Accepted: Oct 17, 2018
Published online: Feb 23, 2019
Published in print: Jun 1, 2019
Discussion open until: Jul 23, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.