Journal ArticleDOI
System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling
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TLDR
Computer simulation results show that the proposed system reliability analysis method can accurately give the system failure probability with a relatively small number of deterministic slope stability analyses.About:
This article is published in Computers and Geotechnics.The article was published on 2015-01-01. It has received 177 citations till now. The article focuses on the topics: Latin hypercube sampling & Gaussian process.read more
Citations
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Journal ArticleDOI
Response surface methods for slope reliability analysis: Review and comparison
TL;DR: In this article, the authors reviewed previous studies on developments and applications of response surface methods (RSMs) in different slope reliability problems and provided some suggestions for selecting relatively appropriate RSMs in slope reliability analysis.
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Concrete dam deformation prediction model for health monitoring based on extreme learning machine
TL;DR: An extreme learning machine (ELM)‐based health monitoring model is proposed for displacement prediction of gravity dams and can produce good generalization performance and learns faster than networks trained using the back propagation algorithm.
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Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence
TL;DR: It is found that the proposed surrogate model combined with MCS can achieve accurate system failure probability evaluation using fewer deterministic slope stability analyzes than other approaches.
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Probabilistic methods for unified treatment of geotechnical and geological uncertainties in a geotechnical analysis
TL;DR: The probabilistic tools developed in the geotechnical profession for tasks such as uncertainty characterization, assessment of impact of uncertainties, uncertainty reduction, and evaluation of the value of uncertainty reduction are discussed.
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Artificial Bee Colony Algorithm Optimized Support Vector Regression for System Reliability Analysis of Slopes
TL;DR: An intelligent response surface method for system probabilistic stability evaluation of soil slopes is presented and it is shown that the new approach is promising in terms of accuracy and efficiency.
References
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Book
Gaussian Processes for Machine Learning
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Journal ArticleDOI
A fast and efficient response surface approach for structural reliability problems
Christian Bucher,U. Bourgund +1 more
TL;DR: In this paper, a new adaptive interpolation scheme is proposed which enables fast and accurate representation of the system behavior by a response surface (RS), which utilizes elementary statistical information on the basic variables (mean values and standard deviations) to increase the efficiency and accuracy.
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State of the Art: Limit Equilibrium and Finite-Element Analysis of Slopes
TL;DR: In the past 25 years great strides have been made in the area of static stability and deformation analysis as discussed by the authors, and the widespread availability of microcomputers has brought about considerable change in the computational aspects of slope stability analysis.
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Factors of safety and reliability in geotechnical engineering
TL;DR: In this paper, simple reliability analyses, involving neither complex theory nor unfamiliar terms, can be used in routine geotechnical engineering practice to evaluate the combined effects of uncertainties in the parameters involved in the calculations, and they offer a useful supplement to conventional analyses.
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Gaussian Processes for Machine Learning (GPML) Toolbox
TL;DR: The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction, including exact and variational inference, Expectation Propagation, and Laplace's method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.