System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling

Kang, Fei; Han, Shaoxuan; Salgado, Rodrigo; Li, Junjie

doi:10.1016/j.compgeo.2014.08.010

Cited by 197 publications

(60 citation statements)

References 40 publications

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“…The second storm event also increased substantially the width of the 95% intervals of the SF. During the next four consecutive dry days (days [14][15][16][17], the stability of the hillslope improved somewhat with a mean SF on day 17 of about 1.35, while the associated 95% ranges that exhibited a smaller spread. The third and last storm event (days 18 to 22) was most severe, with its relatively large precipitation amount threatening the stability of the hillslope.…”

Section: Uncertainty In the Safety Factormentioning

confidence: 99%

“…Conventional (deterministic) methods for slope stability analyses calculate the SF without recourse to the underlying uncertainty in soil and hillslope properties [15,16] designers to address issues beyond those that can be dealt with using deterministic methods. Such methods allow us to calculate confidence intervals of the SF due to uncertainty in soil properties and stratigraphic conditions, data errors, and model structural inadequacies [15,17].…”

Section: Introductionmentioning

confidence: 99%

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The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Gomes

Vrugt

Vargas

et al. 2017

Computers and Geotechnics

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a b s t r a c tWe investigate the uncertainty in bedrock depth and soil hydraulic parameters on the stability of a variably-saturated slope in Rio de Janeiro, Brazil. We couple Monte Carlo simulation of a threedimensional flow model with numerical limit analysis to calculate confidence intervals of the safety factor using a 22-day rainfall record. We evaluate the marginal and joint impact of bedrock depth and soil hydraulic uncertainty. The mean safety factor and its 95% confidence interval evolve rapidly in response to the storm events. Explicit recognition of uncertainty in the hydraulic properties and depth to bedrock increases significantly the probability of failure.

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Section: Uncertainty In the Safety Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Gomes

Vrugt

Vargas

et al. 2017

Computers and Geotechnics

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show abstract

“…In order to estimate the mean of response variable Y using fewer samples, Mckay et al put forward Latin hypercube sampling (LHS) in 1979 [10]. Owen pointed out that LHS was a technique comparable to jittered sampling.…”

Section: Lhs and Its Properties 21 Lhsmentioning

confidence: 99%

Regression modeling based on improved genetic algorithm

Shi

Benda

et al. 2017

Teh. vjesn.

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Original scientific paper Regression model is a well-established method in data analysis with applications in various fields. The selection of independent variables and mathematically transformed in a regression model is often a challenging problem. Recently, some scholars have used evolutionary computation to solve this problem, but the result is not effective as we desired. The crossover operation in GA is redesigned by using Latin hypercube sampling, then combining two commonly used statistical criteria (AIC, BIC) we are presenting an improved genetic algorithm based for solving statistical model selection problem. The proposed algorithm can overcome strong path-dependence and rely on experience of classical approaches. Comparison of simulation results in solving statistical model selection problem with this improved GA, traditional genetic algorithm and classical algorithm for model selection show that the new GA has superiority in solution of quality, convergence rate and other various indices. Keywords: genetic algorithm; Latin hypercube sampling; regression analysis; regression model selection Regresijsko modeliranje zasnovano na poboljšanom genetičkom algoritmuIzvorni znanstveni članak Regresijski model je dobro uhodana metoda u analizi podataka s primjenom u raznim područjima. Izbor nezavisnih varijabli i matematički transformiranih u regresijski model, često predstavlja izazovan problem. Nedavno je nekoliko znanstvenika primijenilo evolucijski proračun za rješenje tog problema, ali rezultat nije učinkovit onoliko koliko smo željeli. Ukrižena (crossover) operacija u GA redizajnirana je primjenom Latin hypercube uzorkovanja, a zatim, kombinacijom dvaju uobičajeno korištenih statističkih kriterija (AIC, BIC), dajemo poboljšani genetički algoritam za rješavanje problema izbora statističkog modela. Predloženim se algoritmom može prevladati jaka ovisnost o putanji i osloniti na iskustvo stečeno primjenom klasičnih pristupa. Usporedba rezultata simulacije u rješavanju problema odabira statističkog modela s ovim poboljšanim GA, tradicionalnog genetičkog algoritma i klasičnog algoritma za odabir modela pokazuje da je novi GA superiorniji u rješavanju kvalitete, brzine konvergencije i drugih različitih pokazatelja.

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“…In fact, Gaussian processes (GPs) are a commonly used learning approach designed to solve regression and probabilistic issues [29]. The Gaussian process can be used as a probabilistic distribution of functions on the Bayesian inference [31].…”

Section: Gaussian Process Classification (Gpc)mentioning

confidence: 99%

“…More precisely, any linear function applied to X t results in a Gaussian distribution. It can be written as X~GP(m, K), which means that the random function X has a Gaussian process distribution with the mean function m and the covariance function K [29].…”

Section: Gaussian Process Classification (Gpc)mentioning

confidence: 99%

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

Azimi

Moghaddam

Monfared

2018

J Environ Health Sci Engineer

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Background The level of groundwater resources is changing rapidly and this requires the discovery of newer groundwater resources. Drought is one of the most significant natural phenomena affecting different aspects of human life and environment. During the last decades, the application of artificial intelligent techniques has been recognized as effective approaches to forecast an annual precipitation rate. Method In this study, the association analysis of climate drought and a decline in groundwater level is addressed using Gaussian process classification (GPC) and backpropagation (BP) artificial neural network (ANN). This methodology is proposed to create a framework for decision making and reduce uncertainty in water resource management calculations, and in particular to optimize the management of groundwater drinking water sources. Results Underground water levels in 609 study plains in Iran were used to predict drought over the test period, extending from 2017 to 2021. The artificial intelligence methods were implemented in the Python programming environment to achieve an annual precipitation rate. A statistical summary of the Rasterized Cells of the zoning maps was used to validate the prediction results. Considering the relationship between water quality reductions and drought in Iranian aquifers due to the occurrence of groundwater drought periods, the results were validated by analysis of the effect of climate drought using the Standardized Precipitation Index (SPI) on the occurrence of observed droughts with the Groundwater Resources Index (GRI). The results are well-illustrated by the observation of the predicted digits in the third dimension of the Gaussian distribution. Conclusion According to the SPI indicator, the southern regions of the country, and especially the central parts of the plain, can be considered the most affected areas by the most severe future droughts. The prediction results indicate a decrease in drought severity as part of a two-year sequence involving a recurrence of drought exacerbation and relative decline, as well as a failed state after the critical condition of aquifers.

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System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling

Cited by 197 publications

References 40 publications

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Regression modeling based on improved genetic algorithm

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

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