Novel approach to efficient slope reliability analysis in spatially variable soils

Wang, Ze Zhou; Goh, S. H.

doi:10.1016/j.enggeo.2020.105989

Cited by 81 publications

(25 citation statements)

References 40 publications

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“…Based on the plane coordinate grid, the divided rectangle is used as the assessment unit, and the size of the unit can be determined according to the actual situation. The indicators that reflect the assessment plan often have different dimensions and orders of magnitude, and it is necessary to normalize all indicators, that is, to uniformly transform the attribute values of the assessment indicators into a certain range and have the same impact trend [ 15 ].…”

Section: Slope Engineering Geology Assessment Based On Improved Cnnmentioning

confidence: 99%

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Liu¹,

Yan²,

Zheng³

2022

Computational Intelligence and Neuroscience

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The geological mechanics, geotechnical characteristics, and hydrogeological conditions of slopes are complex and changeable, so their stability assessment is a complicated system; their traditional engineering geological assessment does not consider the opposition of the system, the uncertainty of performance indicators, and the ambiguity of index classification, being easy to distort results due to the ambiguity. Improved convolutional neural network (CNN) has outstanding advantages in analyzing problems with randomness and fuzziness. It can perform unified numerical processing on slope assessment indicators with precise values, interval values, and qualitative judgment values, making the traditional qualitative description is transformed into quantitative calculation. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the comprehensive assessment model for slope stability and engineering geology; elaborated the development background, current status, and future challenges of the improved CNN; introduced the methods and principles of the model structure, convolutional layer design, and data flow optimization of the improved CNN; performed the assessment index system establishment and index weight determination; established the mathematical assessment model for slope stability; conducted the assessment module design for slope stability based on the improved CNN; analysed the importance of individual factors to the comprehensive engineering geological characteristics; discussed the determination of assessment value of comprehensive unit engineering geological characteristics; explored the assessment module design for slope engineering geology based on the improved CNN; and finally carried out an engineering application and its result analysis. The study results show that the improved CNN can select some universal and objective factors according to the actual conditions of slopes, including topography, stratum lithology, geological structure, atmospheric rainfall, groundwater, engineering activities, setting up factor sets and judgment sets, and making fuzzy inferences. The comprehensive assessment model can use appropriate mathematical methods to judge the pros and cons of slope’s stability and engineering geology according to certain principles and standards, and grade the results and identify the most important geological problems. The results of this paper provide a reference for further researches on the design of a comprehensive assessment model for slope stability and engineering geology based on the improved CNN.

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Section: Slope Engineering Geology Assessment Based On Improved Cnnmentioning

confidence: 99%

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Liu¹,

Yan²,

Zheng³

2022

Computational Intelligence and Neuroscience

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show abstract

“…, 2017; Gopalakrishnan et al. , 2017), and geotechnical engineering (Wang and Goh, 2021). Previous studies have demonstrated that CNN can not only effectively capture the topology of images but also properly identify the complicated relationship between soil parameters and the performance of geotechnical structures (Wang and Goh, 2021; He et al.…”

Section: Bayesian Optimized Convolutional Neural Network (Bocnn)mentioning

confidence: 99%

“…, 2015, 2017; Zhu et al. , 2019), Convolutional Neural Networks (Wang and Goh, 2021), and Random forest algorithm (Liu et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

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Slope reliability analysis using Bayesian optimized convolutional neural networks

Lin

Meng

et al. 2022

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PurposeThis paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability analysis.Design/methodology/approachThe Bayesian optimization technique is firstly used to find the optimal structure of CNN based on the empirical CNN model established in a trial and error manner. The proposed methodology is illustrated through a two-layered soil slope and a cohesive slope with spatially variable soils at different scales of fluctuation.FindingsThe size of training data suite, T, has a significant influence on the performance of trained CNN. In general, a trained CNN with larger T tends to have higher coefficient of determination (R2) and smaller root mean square error (RMSE). The artificial neural networks (ANN) and response surface method (RSM) can provide comparable results to CNN models for the slope reliability where only two random variables are involved whereas a significant discrepancy between the slope failure probability (Pf) by RSM and that predicted by CNN has been observed for slope with spatially variable soils. The RSM cannot fully capture the complicated relationship between the factor of safety (FS) and spatially variable soils in an effective and efficient manner. The trained CNN at a smaller the scale of fluctuation (λ) exhibits a fairly good performance in predicting the Pf for spatially variable soils at higher λ with a maximum percentage error not more than 10%. The BOCNN has a larger R2 and a smaller RMSE than empirical CNN and it can provide results fairly equivalent to a direct Monte Carlo Simulation and therefore serves a promising tool for slope reliability analysis within spatially variable soils.Practical implicationsA geotechnical engineer could use the proposed method to perform slope reliability analysis.Originality/valueSlope reliability can be efficiently and accurately analyzed by the proposed framework.

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“…The random field is essentially a random (or stochastic) process consisting of indexed (i.e. ordered according to one or more reference directions) random variables [10] . At present, the random field theory has been widely used in geotechnical engineering, such as slopes [11] , tunnels [12] , and underground storage gas caverns [13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic analysis of water-sealed performance in underground oil storage considering spatial variability of hydraulic conductivity

Zhang

et al. 2022

Preprint

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For underground water-sealed oil storage, the spatial variability of the surrounding rock has a significant impact on the water-sealed effect of a water curtain system. This study presents a methodology for the probabilistic analysis of water curtain performance in underground oil storage, considering the spatial variability of hydraulic conductivity of the surrounding rock based on field data. Anisotropic random fields representing the spatial variability of hydraulic conductivity were established through spatial statistical analysis of field data and introduced into the finite element model of underground oil storage for water-sealed reliability analysis. The water-sealed performance of different water curtain system schemes was studied using Monte Carlo simulation (MCS). The results showed that the difference between the horizontal spatial correlation and the vertical spatial correlation of the surrounding rock has a significant impact on the water-sealed effect of the water curtain system. An excessively large pressure of water curtain boreholes provided a small contribution to improving water curtain performance. The distance between the water curtain holes and the caverns has the less significant affecting the water-sealed reliability of the storage cavern. Finally, the optimal design of the water curtain system is discussed. This study provides valuable insights and a theoretical basis for the optimisation of water curtain system design parameters for underground water-sealed oil storage.

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Novel approach to efficient slope reliability analysis in spatially variable soils

Cited by 81 publications

References 40 publications

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Slope reliability analysis using Bayesian optimized convolutional neural networks

Probabilistic analysis of water-sealed performance in underground oil storage considering spatial variability of hydraulic conductivity

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