SS-XGBoost: A Machine Learning Framework for Predicting Newmark Sliding Displacements of Slopes
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 146, Issue 9
Abstract
Estimation of Newmark sliding displacement plays an important role for evaluating seismic stability of slopes. Current empirical models generally utilize predefined functional forms and relatively large model uncertainty is involved. On the other hand, machine learning method typically has superior capacity in processing comprehensive data sets in a nonparametric way. In this study, a machine learning framework is proposed to predict Newmark sliding displacements using the extreme gradient boosting model (XGBoost) and the Next Generation Attenuation (NGA)-West2 database, where the subset simulation (SS) is coupled with the -fold cross validation (CV) technique for the first time to tune hyperparameters of the XGBoost model. The framework can achieve excellent generalization capability in predicting displacements and prevent data overfitting by using optimized hyperparameters. The developed data-driven Newmark displacement models can better satisfy both sufficiency and efficiency criteria, and produce considerably smaller standard deviations compared with traditional empirical models. Application of the models in probabilistic seismic slope displacement hazard analysis is also demonstrated. The proposed SS-XGBoost framework has great potential in developing data-driven prediction models for a wide range of engineering applications.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The developed executable file for XGBoost Newmark displacement models is available at http://gwang.people.ust.hk/XGB-Newmark.html.
Acknowledgments
The authors acknowledge support from Hong Kong Research Grants Council (Grant No. 16214118), the National Natural Science Foundation of China (Grant No. 51779189), and Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao (Grant No. 51828902) from National Natural Science Foundation of China. The first author wishes to thank the Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, for hosting his visit as an exchange Ph.D. student.
References
Alavi, A. H., and A. H. Gandomi. 2011. “Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing.” Comput. Struct. 89 (23–24): 2176–2194. https://doi.org/10.1016/j.compstruc.2011.08.019.
Alimoradi, A., and J. L. Beck. 2014. “Machine-learning methods for earthquake ground motion analysis and simulation.” J. Eng. Mech. 141 (4): 04014147. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000869.
Ancheta, T. D., et al. 2014. “NGA-West2 database.” Earthquake Spectra 30 (3): 989–1005. https://doi.org/10.1193/070913EQS197M.
Au, S. K., and J. L. Beck. 2001. “Estimation of small failure probabilities in high dimensions by subset simulation.” Probab. Eng. Mech. 16 (4): 263–277. https://doi.org/10.1016/S0266-8920(01)00019-4.
Bray, J. D., and T. Travasarou. 2007. “Simplified procedure for estimating earthquake-induced deviatoric slope displacements.” J. Geotech. Geoenviron. Eng. 133 (4): 381–392. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:4(381).
Campbell, K. W., and Y. Bozorgnia. 2012. “A comparison of ground motion prediction equations for Arias intensity and cumulative absolute velocity developed using a consistent database and functional form.” Earthquake Spectra 28 (3): 931–941. https://doi.org/10.1193/1.4000067.
Campbell, K. W., and Y. Bozorgnia. 2014. “NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra.” Earthquake Spectra 30 (3): 1087–1115. https://doi.org/10.1193/062913EQS175M.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: ACM.
Derras, B., P. Y. Bard, F. Cotton, and A. Bekkouche. 2012. “Adapting the neural network approach to PGA prediction: An example based on the KiK-net data.” Bull. Seismol. Soc. Am. 102 (4): 1446–1461. https://doi.org/10.1785/0120110088.
Du, W., D. Huang, and G. Wang. 2018a. “Quantification of model uncertainty and variability in Newmark displacement analysis.” Soil Dyn. Earthquake Eng. 109 (Jun): 286–298. https://doi.org/10.1016/j.soildyn.2018.02.037.
Du, W., and G. Wang. 2014. “Fully probabilistic seismic displacement analysis of spatially distributed slopes using spatially correlated vector intensity measures.” Earthquake Eng. Struct. Dyn. 43 (5): 661–679. https://doi.org/10.1002/eqe.2365.
Du, W., and G. Wang. 2016. “A one-step Newmark displacement model for probabilistic seismic slope displacement hazard analysis.” Eng. Geol. 205 (Apr): 12–23. https://doi.org/10.1016/j.enggeo.2016.02.011.
Du, W., G. Wang, and D. Huang. 2018b. “Evaluation of seismic slope displacements based on fully coupled sliding mass analysis and NGA-West2 database.” J. Geotech. Geoenviron. Eng. 144 (8): 06018006. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001923.
Friedman, J. H. 2001. “Greedy function approximation: A gradient boosting machine.” Ann. Stat. 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
Golbraikh, A., and A. Tropsha. 2002. “Beware of q2!.” J. Mol. Graphics Modell. 20 (4): 269–276. https://doi.org/10.1016/S1093-3263(01)00123-1.
Goodfellow, I., Y. Bengio, and A. Courville. 2016. “Machine learning basics.” In Deep learning, 98–164. Cambridge, MA: MIT Press.
Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning. 2nd ed. New York: Springer.
Hinton, G. E. 2012. “Neural networks: Tricks of the trade.” In A practical guide to training restricted Boltzmann machines, 599–619. Berlin: Springer.
Jibson, R. W. 2007. “Regression models for estimating coseismic landslide displacement.” Eng. Geol. 91 (2–4): 209–218. https://doi.org/10.1016/j.enggeo.2007.01.013.
Jibson, R. W. 2011. “Methods for assessing the stability of slopes during earthquakes: A retrospective.” Eng. Geol. 122 (1–2): 43–50. https://doi.org/10.1016/j.enggeo.2010.09.017.
Jibson, R. W., E. L. Harp, and J. A. Michael. 2000. “A method for producing digital probabilistic seismic landslide hazard maps.” Eng. Geol. 58 (3–4): 271–289. https://doi.org/10.1016/S0013-7952(00)00039-9.
Jones, R. E., J. A. Templeton, C. M. Sanders, and J. T. Ostien. 2018. “Machine learning models of plastic flow based on representation theory.” Comput. Modell. Eng. Sci. 117 (3): 309–342. https://doi.org/10.31614/cmes.2018.04285.
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).
Khosravikia, F., Y. Zeinali, Z. Nagy, P. Clayton, and E. Rathje. 2018. “Neural network-based equations for predicting PGA and PGV in Texas, Oklahoma, and Kansas.” In Proc., 5th Geotechnical Earthquake Engineering Soil Dynamics Conf., 538–549. Reston, VA: ASCE.
Kong, Q., D. T. Trugman, Z. E. Ross, M. J. Bianco, B. J. Meade, and P. Gerstoft. 2018. “Machine learning in seismology: Turning data into insights.” Seismol. Res. Lett. 90 (1): 3–14. https://doi.org/10.1785/0220180259.
Lee, J., and R. A. Green. 2015. “Empirical predictive relationship for seismic lateral displacement of slopes.” Géotechnique 65 (5): 374–390. https://doi.org/10.1680/geot.SIP.15.P.011.
Li, D. Q., T. Xiao, Z. J. Cao, C. B. Zhou, and L. M. Zhang. 2016. “Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using subset simulation.” Landslides 13 (2): 293–303. https://doi.org/10.1007/s10346-015-0569-2.
Li, H. S. 2011. “Subset simulation for unconstrained global optimization.” Appl. Math. Model. 35 (10): 5108–5120. https://doi.org/10.1016/j.apm.2011.04.023.
Li, H. S., and S. K. Au. 2010. “Design optimization using subset simulation algorithm.” Struct. Saf. 32 (6): 384–392. https://doi.org/10.1016/j.strusafe.2010.03.001.
Newmark, N. M. 1965. “Effects of earthquakes on dams and embankments.” Géotechnique 15 (2): 139–160. https://doi.org/10.1680/geot.1965.15.2.139.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Oct): 2825–2830.
Qi, X. H., and D. Q. Li. 2018. “Effect of spatial variability of shear strength parameters on critical slip surfaces of slopes.” Eng. Geol. 239 (May): 41–49. https://doi.org/10.1016/j.enggeo.2018.03.007.
Rathje, E. M., and J. D. Bray. 2000. “Nonlinear coupled seismic sliding analysis of earth structures.” J. Geotech. Geoenviron. Eng. 126 (11): 1002–1014. https://doi.org/10.1061/(ASCE)1090-0241(2000)126:11(1002).
Rathje, E. M., and G. Saygili. 2008. “Probabilistic seismic hazard analysis for the sliding displacement of slopes: Scalar and vector approaches.” J. Geotech. Geoenviron. Eng. 134 (6): 804–814. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:6(804).
Rathje, E. M., and G. Saygili. 2009. “Probabilistic assessment of earthquake-induced sliding displacements of natural slopes.” Bull. N. Z. Soc. Earthquake Eng. 42 (1): 18–27. https://doi.org/10.5459/bnzsee.42.1.18-27.
Ren, S., G. Chen, T. Li, Q. Chen, and S. Li. 2018. “A deep learning-based computational algorithm for identifying damage load condition: An artificial intelligence inverse problem solution for failure analysis.” Comput. Modell. Eng. Sci. 117 (3): 287–307. https://doi.org/10.31614/cmes.2018.04697.
Saygili, G., and E. M. Rathje. 2008. “Empirical predictive models for earthquake-induced sliding displacements of slopes.” J. Geotech. Geoenviron. Eng. 134 (6): 790–803. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:6(790).
Shao, H., and X. Deng. 2018. “AdaBoosting neural network for short-term wind speed forecasting based on seasonal characteristics analysis and lag space estimation.” Comput. Modell. Eng. Sci. 114 (3): 277–293. https://doi.org/10.3970/cmes.2018.114.277.
Song, J., G. Y. Gao, A. Rodriguez-Marek, and E. M. Rathje. 2016. “Seismic assessment of the rigid sliding displacements caused by pulse motions.” Soil Dyn. Earthquake Eng. 82 (Mar): 1–10. https://doi.org/10.1016/j.soildyn.2015.11.014.
Song, J., and A. Rodriguez-Marek. 2015. “Sliding displacement of flexible earth slopes subject to near-fault ground motions.” J. Geotech. Geoenviron. Eng. 141 (3): 04014110. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001233.
Wang, G. 2012. “Efficiency of scalar and vector intensity measures for seismic slope displacements.” Front. Struct. Civ. Eng. 6 (1): 44–52. https://doi.org/10.1007/s11709-012-0138-x.
Wang, G., and W. Du. 2013. “Spatial cross-correlation models for vector intensity measures (PGA, , PGV, and SAs) considering regional site conditions.” Bull. Seismol. Soc. Am. 103 (6): 3189–3204. https://doi.org/10.1785/0120130061.
Wang, M. X., X. S. Tang, D. Q. Li, and X. H. Qi. 2020. “Subset simulation for efficient slope reliability analysis involving copula-based cross-correlated random fields.” Comput. Geotech. 118 (Feb): 103326. https://doi.org/10.1016/j.compgeo.2019.103326.
Wang, Y., and E. M. Rathje. 2018. “Application of a probabilistic assessment of the permanent seismic displacement of a slope.” J. Geotech. Geoenviron. Eng. 144 (6): 04018034. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001886.
XGBoost. 2018. “XGBoost documentation—XGBoost parameters.” Accessed September 30, 2019. https://xgboost.readthedocs.io/en/release_0.80/parameter.html.
Xiao, T., D. Q. Li, Z. J. Cao, S. K. Au, and K. K. Phoon. 2016. “Three-dimensional slope reliability and risk assessment using auxiliary random finite element method.” Comput. Geotech. 79 (Oct): 146–158. https://doi.org/10.1016/j.compgeo.2016.05.024.
Xiao, T., D. Q. Li, Z. J. Cao, and L. M. Zhang. 2018. “CPT-based probabilistic characterization of three-dimensional spatial variability using MLE.” J. Geotech. Geoenviron. Eng. 144 (5): 04018023. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001875.
Yin, Y., F. Wang, and P. Sun. 2009. “Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China.” Landslides 6 (2): 139–152. https://doi.org/10.1007/s10346-009-0148-5.
Youssef, A. M., H. R. Pourghasemi, Z. S. Pourtaghi, and M. M. Al-Katheeri. 2016. “Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia.” Landslides 13 (5): 839–856. https://doi.org/10.1007/s10346-015-0614-1.
Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 146 • Issue 9 • September 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Aug 7, 2019
Accepted: Mar 3, 2020
Published online: Jun 18, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 18, 2020
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.