Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges

Zhang, Wengang; Gu, Xianfeng; Li, Hong; Han, Liang; Wang, Lin

doi:10.1016/j.asoc.2023.110066

Cited by 65 publications

(17 citation statements)

References 74 publications

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“…In assessing the probability of slope failure, the methods routinely applied are the Monte Carlo method, first-order second-moment method (FOSM), and point estimates method [10,13,31,[39][40][41][42][43][44]. However, due to advances in technology, recent methodologies using artificial intelligence have been developed for predicting slope stability [45][46][47][48][49].…”

Section: Failure Probabilitymentioning

confidence: 99%

Probabilistic Approach to Transient Unsaturated Slope Stability Associated with Precipitation Event

Costa,

Dantas,

Cavalcante

et al. 2023

Sustainability

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The massif rupture is not always reached under saturated conditions; therefore, the analysis of the unsaturated phenomenon is necessary in some cases. This study performed a probabilistic approach for unsaturated and transient conditions to understand the contribution of physical and hydraulic parameters involved in slope stability. The proposed slope stability model was based on the infinite slope method and a new unsaturated constitutive shear strength model proposed in 2021 by Cavalcante and Mascarenhas. The first-order second-moment method, which incorporated multiple stochastic variables, was used in the probabilistic analysis, allowing the incorporation of seven independent variables for the probability of failure analysis as well as for quantifying the contribution of the variables to the total variance of a factor of safety at any state of moisture. This implementation allows a more realistic estimative for the probability of failure, showing in a practical way the decrease and increase of the probability of failure during a rain event. The model provided promising results highlighting the need to migrate from deterministic analyses to more robust probabilistic analyses, considering the most significant number of stochastic variables. The proposed model helps to understand the influence of moisture content on slope stability, being a possible tool in natural disaster risk management.

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Section: Failure Probabilitymentioning

confidence: 99%

Probabilistic Approach to Transient Unsaturated Slope Stability Associated with Precipitation Event

Costa,

Dantas,

Cavalcante

et al. 2023

Sustainability

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“…Recently, Ullaha et al [2] also published a brief review of the actual methods for slope stability assessments, having dived them into five distinct groups instead of four. Among all these approaches, the literature has emphasizedthe finite elements methods [3], reliability analysis [4,5], and those approaches based on machine learning algorithms [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. More recently, some new approaches have been proposed based on the vector sum method [26,27].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

An Evolutionary Neural Network Approach for Slopes Stability Assessment

Tinoco,

Gomes Correia,

Cortez

et al. 2023

Applied Sciences

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A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a first attempt at the development of a classification system for rock and soil cuttings, as well as embankments based on visual features, was made in this paper using soft computing algorithms. The achieved results, although interesting, nevertheless have some important limitations to their successful use as auxiliary tools for transportation network management tasks. Accordingly, we carried out new experiments through the combination of modern optimization and soft computing algorithms. Thus, one of the main challenges to overcome is related to the selection of the best set of input features for a feedforward neural network for earthwork hazard category (EHC) identification. We applied a genetic algorithm (GA) for this purpose. Another challenging task is related to the asymmetric distribution of the data (since typically good conditions are much more common than bad ones). To address this question, three training sampling approaches were explored: no resampling, the synthetic minority oversampling technique (SMOTE), and oversampling. Some relevant observations were taken from the optimization process, namely, the identification of which variables are more frequently selected for EHC identification. After finding the most efficient models, a detailed sensitivity analysis was applied over the selected models, allowing us to measure the relative importance of each attribute in EHC identification.

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“…However, it is important to be aware of the limitations and uncertainties associated with ML approaches before applying them to real-world geotechnical engineering projects. Numerous studies (Baghbani et al 2022;Zhang et al 2023;Zhang et al 2022) have exposed these limitations, which are primarily: a) the scarcity of high-quality data, b) the difficulty in interpreting the models, and c) the lack of generalization. Regarding data availability, geotechnical data can be costly and often incomplete or uncertain.…”

Section: Introductionmentioning

confidence: 99%

A super-learner machine learning model for a global prediction of compression index in clays

Díaz,

Spagnoli

2024

Applied Clay Science

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Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges

Cited by 65 publications

References 74 publications

Probabilistic Approach to Transient Unsaturated Slope Stability Associated with Precipitation Event

Probabilistic Approach to Transient Unsaturated Slope Stability Associated with Precipitation Event

An Evolutionary Neural Network Approach for Slopes Stability Assessment

A super-learner machine learning model for a global prediction of compression index in clays

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