Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 148, Issue 4
Abstract
Apart from spatial variability of soil properties, a geotechnical system can have many other sources of uncertainties. To efficiently analyze such a system in a probabilistic manner, many strategies have been proposed in the literature. This paper presents a deep learning technique for an efficient geotechnical reliability analysis with multiple uncertainties. The proposed method involves using convolutional neural networks (CNNs) as metamodels of the physics-based simulation model of a geotechnical system. In the present study, the spatially variable soil properties and the external loads are simultaneously considered in the analysis of a geotechnical system. The proposed neural network method configures these uncertainties to form a multi-channel “image.” CNNs can then simultaneously learn high-level features that contain information about the multiple uncertainties. With an appropriate architecture and adequate training, the trained CNNs can replace the computationally demanding physics-based simulation model for Monte Carlo simulations. Application of the neural network method is illustrated using a synthetic geotechnical example. The results reveal that the proposed neural network method effectively handles multiple uncertainties and efficiently predicts a failure probability value that is in good agreement with the benchmark result obtained using direct Monte Carlo simulations.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National University of Singapore and was conducted at the Centre for Protective Technology.
References
Ang, A. H.-S., and W. H. Tang. 2007. Probability concepts in engineering planning and design: Emphasis on application to civil and environmental engineering. Hoboken, NJ: Wiley.
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.
Cha, Y.-J., W. Choi, and O. Büyüköztürk. 2017. “Deep learning-based crack damage detection using convolutional neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 361–378.
Goh, A. T. C., W. G. Zhang, and K. S. Wong. 2019. “Deterministic and reliability analysis of basal heave stability for excavation in spatial variable soils.” Comput. Geotech. 108 (Apr): 152–160. https://doi.org/10.1016/j.compgeo.2018.12.015.
Gong, W., H. Tang, H. Wang, X. Wang, and C. H. Juang. 2019. “Probabilistic analysis and design of stabilizing piles in slope considering stratigraphic uncertainty.” Eng. Geol. 259 (Sep): 105162. https://doi.org/10.1016/j.enggeo.2019.105162.
Griffiths, D. V., J. Huang, and G. A. Fenton. 2009. “Influence of spatial variability on slope reliability using 2-D random fields.” J. Geotech. Geoenviron. Eng. 135 (10): 1367–1378. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000099.
Jiang, S.-H., and J.-S. Huang. 2016. “Efficient slope reliability analysis at low-probability levels in spatially variable soils.” Comput. Geotech. 75 (May): 18–27. https://doi.org/10.1016/j.compgeo.2016.01.016.
Li, D. Q., D. Zheng, Z. J. Cao, X. S. Tang, and X. H. Qi. 2019. “Two-stage dimension reduction method for meta-model based slope reliability analysis in spatially variable soils.” Struct. Saf. 81 (Nov): 101872. https://doi.org/10.1016/j.strusafe.2019.101872.
Li, D.-Q., D. Zheng, Z.-J. Cao, X.-S. Tang, and K.-K. Phoon. 2016. “Response surface methods for slope reliability analysis: Review and comparison.” Eng. Geol. 203 (Mar): 3–14. https://doi.org/10.1016/j.enggeo.2015.09.003.
Liu, W.-S., and S. H. Cheung. 2020. “Decoupled reliability-based geotechnical design of deep excavations of soil with spatial variability.” Appl. Math. Modell. 85 (Sep): 46–59. https://doi.org/10.1016/j.apm.2020.04.001.
Liu, W.-S., S. H. Cheung, and W.-J. Cao. 2019. “An efficient surrogate-aided importance sampling framework for reliability analysis.” Adv. Eng. Software 135 (Sep): 102687. https://doi.org/10.1016/j.advengsoft.2019.102687.
Phoon, K.-K. 2004. Towards reliability-based design for geotechnical engineering. Seoul, South Korea: Special Lecture for Korean Geotechnical Society.
Wang, H., J. F. Wellmann, Z. Li, X. Wang, and R. Y. Liang. 2017. “A segmentation approach for stochastic geological modeling using hidden Markov random fields.” Math. Geosci. 49 (2): 145–177. https://doi.org/10.1007/s11004-016-9663-9.
Wang, L., C. Wu, L. Tang, W. Zhang, S. Lacasse, H. Liu, and L. Gao. 2020. “Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method.” Acta Geotech. 15 (11): 3135–3150. https://doi.org/10.1007/s11440-020-00962-4.
Wang, W., B. Wu, S. Yang, and Z. Wang. 2019. “Road damage detection and classification with faster R-CNN.” In Proc., 2018 IEEE Int. Conf. on Big Data, Big Data 2018. New York: IEEE.
Wang, X., Z. Li, H. Wang, Q. Rong, and R. Y. Liang. 2016. “Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty.” Struct. Saf. 62 (Sep): 88–100. https://doi.org/10.1016/j.strusafe.2016.06.007.
Wang, X., H. Wang, and R. Y. Liang. 2018. “A method for slope stability analysis considering subsurface stratigraphic uncertainty.” Landslides 15 (5): 925–936. https://doi.org/10.1007/s10346-017-0925-5.
Wang, Z. Z., C. Xiao, S. H. Goh, and M.-X. Deng. 2021. “Metamodel-based reliability analysis in spatially variable soils using convolutional neural networks.” J. Geotech. Geoenviron. Eng. 147 (3): 04021003. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002486.
Wang, Z.-Z., and S. H. Goh. 2021. “Novel approach to efficient slope reliability analysis in spatially variable soils.” Eng. Geol. 281 (Feb): 105989. https://doi.org/10.1016/j.enggeo.2020.105989.
Zhang, D. M., K. K. Phoon, H. W. Huang, and Q. F. Hu. 2015. “Characterization of model uncertainty for cantilever deflections in undrained clay.” J. Geotech. Geoenviron. Eng. 141 (1): 04014088. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001205.
Zhang, J., K. K. Phoon, D. Zhang, H. Huang, and C. Tang. 2021. “Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil.” J. Rock Mech. Geotech. Eng. 13 (6): 1358–1367. https://doi.org/10.1016/j.jrmge.2021.09.001.
Zhang, J., X. Yang, W. Li, S. Zhang, and Y. Jia. 2020. “Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method.” Autom. Constr. 113 (May): 103119. https://doi.org/10.1016/j.autcon.2020.103119.
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 (Apr): 103966. https://doi.org/10.1016/j.compgeo.2020.103966.
Zhu, B., T. Hiraishi, H. Pei, and Q. Yang. 2021. “Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression-based surrogate model.” Int. J. Numer. Anal. Methods Geomech. 45 (4): 478–501. https://doi.org/10.1002/nag.3169.
Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 148 • Issue 4 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 29, 2021
Accepted: Dec 20, 2021
Published online: Feb 9, 2022
Published in print: Apr 1, 2022
Discussion open until: Jul 9, 2022
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.
Cited by
- Zheming Zhang, Ze Zhou Wang, Siang Huat Goh, Jian Ji, Deep Learning–Based Prediction of Tunnel Face Stability in Layered Soils Using Images of Random Fields, Journal of Geotechnical and Geoenvironmental Engineering, 10.1061/JGGEFK.GTENG-12109, 150, 8, (2024).
- Liang Zhang, Lei Wang, Reliability Assessment of Pile-Founded T-Walls Using Kriging Method, Geo-Risk 2023, 10.1061/9780784484999.032, (307-316), (2023).
- Chuxuan Tang, Zheng Lu, Siang Huat Goh, Jie Liu, Hailin Yao, Jing Zhang, Xianwen Huang, Dynamic responses of an axisymmetric thermo-poroelastic half-space subjected to thermo-mechanical loads, Computers and Geotechnics, 10.1016/j.compgeo.2022.105147, 154, (105147), (2023).
- Shi-Ya Huang, Shao-He Zhang, Lei-Lei Liu, A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes, Reliability Engineering & System Safety, 10.1016/j.ress.2022.108761, 228, (108761), (2022).
- Chongzhi Wu, Li Hong, Lin Wang, Runhong Zhang, Samui Pijush, Wengang Zhang, Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network, Gondwana Research, 10.1016/j.gr.2022.06.011, (2022).