Reliability Evaluation of Slopes Considering Spatial Variability of Soil Parameters Based on Efficient Surrogate Model
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 1
Abstract
The conventional surrogate model for slope reliability assessment often is faced with the issues of high dimensionality and sample selection disorder, which are caused by the spatial variability of soil parameters and which compromise the precision and efficiency of slope reliability assessment. Previous studies focused on solving this problem mainly by choosing more-accurate models; studies of optimizing the training samples for constructing surrogate models are relatively scarce. This paper proposes a multivariate adaptive regression spline model based on active learning (AMARS) for slope reliability analysis in spatially variable soils, combined with the sliced inverse regression (SIR) method. The active learning includes self-supervised learning methods that optimize the sample set for constructing surrogate models. The training samples are processed using the SIR method to prevent the model from falling into dimensionality disaster. The proposed method was validated using two slope cases with spatial variation. Comparison of computational efficiency and accuracy in estimating slope failure probability revealed that the method suggested here outperforms others. Moreover, for both single-layer simple and multilayer complex spatially varying slopes, the proposed method not only reduces computational costs effectively but can also be used to evaluate the reliability of slopes with small failure probabilities.
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Data Availability Statement
Data generated or analysed during this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Natural Science Foundation of Jiangxi Province (Project No. 20224BAB204076), Young Scientific and Technological Talents Sponsorship Project in Ganpo Juncai Support Program (2023QT08), and the National Natural Science Foundation of China (Project Nos. 52378344, 52222905, and 51969018). The financial support is gratefully acknowledged.
References
Asare, E. N., M. Affam, and Y. Y. Ziggah. 2023. “A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks.” Model. Earth Syst. Environ. 2023 (Jan): 1–17. https://doi.org/10.1007/s40808-023-01717-2.
Cho, S. E. 2009. “Probabilistic stability analyses of slopes using the ANN-based response surface.” Comput. Geotech. 36 (5): 787–797. https://doi.org/10.1016/j.compgeo.2009.01.003.
Cho, S. E. 2010. “Probabilistic assessment of slope stability that considers the spatial variability of soil properties.” J. Geotech. Geoenviron. 136 (7): 975–984. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000309.
Constantine, P. G., E. Dow, and Q. Q. Wang. 2014. “Active subspace methods in theory and practice: Applications to kriging surfaces.” Siam J. Sci. Comput. 36 (4): 1500–1524. https://doi.org/10.1137/130916138.
Deng, Z. P., D. Q. Li, X. H. Qi, Z. J. Cao, and K. K. Phoon. 2017. “Reliability evaluation of slope considering geological uncertainty and inherent variability of soil parameters.” Comput. Geotech. 92 (Sep): 121–131. https://doi.org/10.1016/j.compgeo.2017.07.020.
Deng, Z. P., M. Pan, J. T. Niu, S. H. Jiang, and W. W. Qian. 2021. “Slope reliability analysis in spatially variable soils using sliced inverse regression-based multivariate adaptive regression spline.” Bull. Eng. Geol. Environ. 80 (Sep): 7213–7226. https://doi.org/10.1007/s10064-021-02353-9.
Duncan, J. M., S. G. Wright, and T. L. Brandon. 2014. Soil strength and slope stability. New York: Wiley.
Echard, B., N. Gayton, and M. Lemaire. 2011. “AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation.” Struct. Saf. 33 (2): 145–154. https://doi.org/10.1016/j.strusafe.2011.01.002.
Friedman, J. H. 1991. “Multivariate adaptive regression splines.” Ann. Stat. 19 (1): 1–67. https://doi.org/10.1214/aos/1176347963.
Griffiths, D. V., and G. A. Fenton. 2004. “Probabilistic slope stability analysis by finite elements.” J. Geotech. Geoenviron. 130 (5): 507–518. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:5(507).
Huang, S. Y., S. H. Zhang, and L. L. Liu. 2022a. “A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes.” Reliab. Eng. Syst. Saf. 228 (Jun): 108761. https://doi.org/10.1016/j.ress.2022.108761.
Huang, S. Y., S. H. Zhang, L. L. Liu, W. Q. Zhu, and Y. M. Cheng. 2021. “Efficient slope reliability analysis and risk assessment based on multiple Kriging metamodels.” Comput. Geotech. 137 (Sep): 104277. https://doi.org/10.1016/j.compgeo.2021.104277.
Huang, Y., K. Yu, N. Wu, and J. Chang. 2022b. “Slope shape and edge intelligent recognition technology based on deep neural sensing network.” J. Sensors 2022 (Jul): 4. https://doi.org/10.1155/2022/5901803.
Jiang, S. H., D. Q. Li, L. M. Zhang, and C. B. Zhou. 2014. “Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method.” Eng. Geol. 168 (Jan): 120–128. https://doi.org/10.1016/j.enggeo.2013.11.006.
Jiang, S. H., X. Liu, and J. Huang. 2020. “Non-intrusive reliability analysis of unsaturated embankment slopes accounting for spatial variabilities of soil hydraulic and shear strength parameters.” Eng. Comput. Germany 2020 (1): 1–14. https://doi.org/10.1007/s00366-020-01108-6.
Jiang, S. H., I. Papaioannou, and D. Straub. 2018. “Bayesian updating of slope reliability in spatially variable soils with in-situ measurements.” Eng. Geol. 239 (May): 310–320. https://doi.org/10.1016/j.enggeo.2018.03.021.
Jolliffe, I. T. 2002. Principal component analysis for special types of data. New York: Springer.
Li, D. Q., S. H. Jiang, Z. J. Cao, W. Zhou, C. B. Zhou, and L. M. Zhang. 2015. “A multiple response-surface method for slope reliability analysis considering spatial variability of soil properties.” Eng. Geol. 187 (Mar): 60–72. https://doi.org/10.1016/j.enggeo.2014.12.003.
Li, D. Q., D. Zheng, Z. J. Cao, X. S. Tang, and X. H. Qi. 2019. “Two-stage dimension reduction method for metamodel based slope reliability analysis in spatially variable soils.” Struct. Saf. 81 (Nov): 101872. https://doi.org/10.1016/j.strusafe.2019.101872.
Li, K. C. 1991. “Sliced inverse regression for dimension reduction.” J. Am. Stat. Assoc. 86 (414): 316–327. https://doi.org/10.1080/01621459.1991.10475035.
Li, P., and Y. Wang. 2022. “An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS).” Reliab. Eng. Syst. Saf. 221 (May): 108377. https://doi.org/10.1016/j.ress.2022.108377.
Li, T. Z., Q. Pan, and D. Dias. 2021a. “Active learning relevant vector machine for reliability analysis.” Appl. Math. Model. 89 (Jan): 381–399. https://doi.org/10.1016/j.apm.2020.07.034.
Li, X., C. L. Gong, L. X. Gu, W. K. Gao, J. Zhao, and S. Hua. 2018. “A sequential surrogate method for reliability analysis based on radial basis function.” Struct. Saf. 73 (Jul): 42–53. https://doi.org/10.1016/j.strusafe.2018.02.005.
Li, X., Y. Liu, Z. Yang, Z. Meng, and L. Zhang. 2021b. “Efficient slope reliability analysis using adaptive classification-based sampling method.” Bull. Eng. Geol. Environ. 80 (Dec): 8977–8993. https://doi.org/10.1007/s10064-021-02476-z.
Li, X. Y., L. M. Zhang, L. Gao, and H. Zhu. 2017. “Simplified slope reliability analysis considering spatial soil variability.” Eng. Geol. 216 (Jan): 90–97. https://doi.org/10.1016/j.enggeo.2016.11.013.
Liu, L., S. Zhang, Y. M. Cheng, and L. Liang. 2019. “Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines.” Geosci. Front. 10 (2): 671–682. https://doi.org/10.1016/j.gsf.2018.03.013.
Liu, L. L., and Y. M. Cheng. 2016. “Efficient system reliability analysis of soil slopes using multivariate adaptive regression splines-based Monte Carlo simulation.” Comput. Geotech. 79 (Oct): 41–54. https://doi.org/10.1016/j.compgeo.2016.05.001.
Liu, Y., Z. Yang, and X. Li. 2022. “Adaptive ensemble learning of radial basis functions for efficient geotechnical reliability analysis.” Comput. Geotech. 146 (Jun): 104753. https://doi.org/10.1016/j.compgeo.2022.104753.
Lu, Y. Z., Y. Z. Yang, and H. L. Zhang. 2005. “Reliability evaluation of slope engineering by support vector machine.” J. Rock Mech. Geotech. 24 (1): 149–153. https://doi.org/10.3321/j.issn:1000-6915.2005.01.025.
Ma, J. Z., J. Zhang, H. W. Huang, L. L. Zhang, and J. S. Huang. 2017. “Identification of representative slip surfaces for reliability analysis of soil slopes based on shear strength reduction.” Comput. Geotech. 85 (May): 199–206. https://doi.org/10.1016/j.compgeo.2016.12.033.
Phoon, K. K., S. P. Huang, and S. T. Quek. 2002. “Implementation of Karhunen–Loeve expansion for simulation using a wavelet-Galerkin scheme.” Probab. Eng. Mech. 17 (3): 293–303. https://doi.org/10.1016/S0266-8920(02)00013-9.
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.
Shiau, J., and S. Keawsawasvong. 2023. “Multivariate adaptive regression splines analysis for 3D slope stability in anisotropic and heterogenous clay.” J. Rock Mech. Geotech. 15 (4): 1052–1064. https://doi.org/10.1016/j.jrmge.2022.05.016.
Tan, X. H., W. H. Bi, X. L. Hou, and W. Wang. 2011. “Reliability analysis using radial basis function networks and support vector machines.” Comput. Geotech. 38 (2): 178–186. https://doi.org/10.1016/j.compgeo.2010.11.002.
Wang, Y., Z. Cao, and S. K. Au. 2011. “Practical reliability analysis of slope stability by advanced Monte Carlo simulations in a spreadsheet.” Can. Geotech. J. 48 (1): 162–172. https://doi.org/10.1139/T10-044.
Xiao, N. C., M. J. Zuo, and C. Zhou. 2018. “A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis.” Reliab. Eng. Syst. Saf. 169 (Jan): 330–338. https://doi.org/10.1016/j.ress.2017.09.008.
Xie, W., W. Nie, P. Saffari, L. F. Robledo, P.-Y. Descote, and W. Jian. 2021. “Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China.” Nat. Hazards 109 (1): 931–948. https://doi.org/10.1007/s11069-021-04862-y.
Xue, X., X. Yang, and X. Chen. 2014. “Application of a support vector machine for prediction of slope stability.” Sci. China Technol. Sci. 57 (5): 2379–2386. https://doi.org/10.1007/s11431-014-5699-6.
Zhang, J., H. W. Huang, and K. K. Phoon. 2013. “Application of the Kriging-based response surface method to the system reliability of soil slopes.” J. Geotech. Geoenviron. 139 (4): 651–655. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000801.
Zhang, T., P. Zeng, R. Jimenez, T. Li, X. Feng, and X. Sun. 2022. “System reliability analysis of soil slopes using shear strength reduction and active-learning surrogate models.” Arab. J. Geosci. 15 (6): 470. https://doi.org/10.1007/s12517-022-09718-8.
Zhang, W. G., X. Gu, L. Hong, L. Han, and L. Wang. 2023. “Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges.” Appl. Soft Comput. 2023 (Feb): 110066. https://doi.org/10.1016/j.asoc.2023.110066.
Zhao, H. B. 2008. “Slope reliability analysis using a support vector machine.” Comput. Geotech. 35 (3): 459–467. https://doi.org/10.1016/j.compgeo.2007.08.002.
Zhou, Z., D. Q. Li, T. Xiao, Z. J. Cao, and W. Du. 2021. “Response surface guided adaptive slope reliability analysis in spatially varying soils.” Comput. Geotech. 132 (Aug): 103966. https://doi.org/10.1016/j.compgeo.2020.103966.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 1 • March 2024
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© 2023 American Society of Civil Engineers.
History
Received: Jul 4, 2023
Accepted: Oct 10, 2023
Published online: Dec 9, 2023
Published in print: Mar 1, 2024
Discussion open until: May 9, 2024
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