Reliability Analysis of Spatially Variable Soil Slope Using Deep Learning Algorithm
Publication: Geo-Congress 2023
ABSTRACT
Landslides are the most disastrous natural catastrophe, which causes immense damage to infrastructure and loss of life globally. The properties of soil slope are spatially variable due to various loading and deposition conditions. Hence, slope reliability analysis should take into account the spatial variation of slope material. Previous researchers utilize the random field theory to consider spatial variability in slope reliability analysis. However, this method requires extensive computational resources and time. To address this issue, the present study proposes a methodology based on convolution neural network (CNN) and Monte Carlo simulation. CNN algorithm is utilized as a surrogate model to replicate the random field model of slope as CNN algorithms efficiently learn the spatial variation of the random field. The CNN model is further used to conduct reliability analysis using Monte Carlo simulation. An example application of the proposed method is performed for spatially variable soil slope to validate the proposed method. The example results suggest that the proposed method provides practical values of the probability of slope failure.
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REFERENCES
Cha, Y. J., W. Choi, and O. Büyüköztürk. 2017. “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks.” Comput. Civ. Infrastruct. Eng., 32 (5): 361–378. https://doi.org/10.1111/mice.12263.
Cho, S. E. 2007. “Effects of spatial variability of soil properties on slope stability.” Eng. Geol., 92 (3–4): 97–109. https://doi.org/10.1016/j.enggeo.2007.03.006.
Cho, S. E. 2010. “Probabilistic Assessment of Slope Stability That Considers the Spatial Variability of Soil Properties.” J. Geotech. Geoenvironmental Eng., 136 (7): 975–984. https://doi.org/10.1061/(asce)gt.1943-5606.0000309.
Cho, S. E. 2014. “Probabilistic stability analysis of rainfall-induced landslides considering spatial variability of permeability.” Eng. Geol., 171: 11–20. Elsevier B.V. https://doi.org/10.1016/j.enggeo.2013.12.015.
El-Ramly, H., N. R. Morgenstern, and D. M. Cruden. 2002. “Probabilistic slope stability analysis for practice.” Can. Geotech. J., 39 (3): 665–683. https://doi.org/10.1139/t02-034.
Ering, P., and G. L. Sivakumar Babu. 2016. “Probabilistic back analysis of rainfall induced landslide- A case study of Malin landslide, India.” Eng. Geol., 208: 154–164. Elsevier B.V. https://doi.org/10.1016/j.enggeo.2016.05.002.
Ering, P., and G. L. Sivakumar Babu. 2020. “Effect of spatial variability of earthquake ground motions on the reliability of road system.” Soil Dyn. Earthq. Eng., 136 (December 2019): 106207. Elsevier Ltd. https://doi.org/10.1016/j.soildyn.2020.106207.
Feng, X. T., H. Zhao, and S. Li. 2004. “A new displacement back analysis to identify mechanical geo-material parameters based on hybrid intelligent methodology.” Int. J. Numer. Anal. Methods Geomech., 28 (11): 1141–1165. https://doi.org/10.1002/nag.381.
Griffiths, D. V., and G. A. Fenton. 2004. “Slope stability analysis by finite elements.” J. Geotech. Geoenvironmental Eng., 130 (5): 507–518. https://doi.org/10.1680/geot.1999.49.3.387.
Griffiths, D. V., J. Huang, and G. A. Fenton. 2009. “Influence of Spatial Variability on Slope Reliability Using 2-D Random Fields.” J. Geotech. Geoenvironmental Eng., 135 (10): 1367–1378. https://doi.org/10.1061/(asce)gt.1943-5606.0000099.
Griffiths, D. V., J. Huang, and G. A. Fenton. 2015. “Probabilistic slope stability analysis using RFEM with non - stationary random fields.” Geotech. Saf. Risk V, (January): 1–6. https://doi.org/10.3233/978-1-61499-580-7-704.
Huang, S., S. Mahadevan, and R. Rebba. 2007. “Collocation-based stochastic finite element analysis for random field problems.” Probabilistic Eng. Mech., 22 (2): 194–205. https://doi.org/10.1016/j.probengmech.2006.11.004.
Jiang, S. H., and J. S. Huang. 2016. “Efficient slope reliability analysis at low-probability levels in spatially variable soils.” Comput. Geotech., 75: 18–27. Elsevier Ltd. https://doi.org/10.1016/j.compgeo.2016.01.016.
Le, T. M. H., D. Gallipoli, M. Sanchez, and S. Wheeler. 2015. “Stability and failure mass of unsaturated heterogeneous slopes.” Can. Geotech. J., 52 (11).
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: 60–72. Elsevier B.V. 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 meta-model based slope reliability analysis in spatially variable soils.” Struct. Saf., 81 (June): 101872. Elsevier. https://doi.org/10.1016/j.strusafe.2019.101872.
Li, S., H. Zhao, Z. Ru, and Q. Sun. 2016. “Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope.” Eng. Geol., 203: 178–190. Elsevier B.V. https://doi.org/10.1016/j.enggeo.2015.11.004.
Miranda, T., A. G. Correia, and L. R. e Sousa. 2009. “Bayesian methodology for updating geomechanical parameters and uncertainty quantification.” Int. J. Rock Mech. Min. Sci., 46 (7): 1144–1153. Elsevier. https://doi.org/10.1016/j.ijrmms.2009.03.008.
Phoon, K.-K., A. Santoso, and S.-T. Quek. 2010. “Probabilistic Analysis of Soil-Water Characteristic Curves.” J. Geotech. Geoenvironmental Eng., 136 (3): 445–455. https://doi.org/10.1061/(asce)gt.1943-5606.0000222.
Phoon, K. K., S. P. Huang, and S. T. Quek. 2002. “Simulation of second-order processes using Karhunen-Loeve expansion.” Comput. Struct., 80 (12): 1049–1060. https://doi.org/10.1016/S0045-7949(02)00064-0.
Phoon, K. K., and F. H. Kulhawy. 1999. “Characterization of geotechnical variability.” Can. Geotech. J., 36 (4): 612–624. https://doi.org/10.1139/t99-038.
Rana, H., and G. L. Sivakumar Babu. 2022a. “Probabilistic back analysis for rainfall-induced slope failure using MLS-SVR and Bayesian analysis Bayesian analysis.” Georisk Assess. Manag. Risk Eng. Syst. Geohazards, 0 (0): 1–14. https://doi.org/10.1080/17499518.2022.2084555.
Rana, H., and G. L. Sivakumar Babu. 2022b. “Evaluating Soil Parameters Considering Probabilistic Back Analysis for Slope Failures.” Geo-Congress 2022, 360–369. American Society of Civil Engineers.
Wang, L., C. Wu, Y. Li, H. Liu, W. Zhang, and X. Chen. 2019. “Probabilistic Risk Assessment of unsaturated Slope Failure Considering Spatial Variability of Hydraulic Parameters.” KSCE J. Civ. Eng., 23 (12): 5032–5040. https://doi.org/10.1007/s12205-019-0884-6.
Wang, Z. Z., and S. H. Goh. 2021. “Novel approach to efficient slope reliability analysis in spatially variable soils.” Eng. Geol., 281 (1): 105989. Elsevier B.V. https://doi.org/10.1016/j.enggeo.2020.105989.
Xu, F., B. Q. Huang, and K. Wang. 2013. “Study and application of slope displacement back analysis based on SVM-CTS.” Appl. Mech. Mater., 353–354: 163–166. https://doi.org/10.4028/www.scientific.net/AMM.353-356.163.
Zhao, H. B., and S. Yin. 2009. “Geomechanical parameters identification by particle swarm optimization and support vector machine.” Appl. Math. Model., 33 (10): 3997–4012. Elsevier Inc. https://doi.org/10.1016/j.apm.2009.01.011.
Information & Authors
Information
Published In
Geo-Congress 2023
Pages: 553 - 562
History
Published online: Mar 23, 2023
ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Geomechanics
- Geometry
- Geotechnical engineering
- Mathematics
- Methodology (by type)
- Monte Carlo method
- Neural networks
- Numerical methods
- Slopes
- Soil analysis
- Soil mechanics
- Soil properties
- Spatial analysis
- Spatial variability
Authors
Metrics & Citations
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