Journal ArticleDOI
An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation
Qiujing Pan,Daniel Dias +1 more
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TLDR
This work develops an efficient reliability method which takes advantage of the Adaptive Support Vector Machine (ASVM) and the Monte Carlo Simulation (MCS), leading to accurate estimation of failure probability with rather low computational cost.About:
This article is published in Structural Safety.The article was published on 2017-07-01. It has received 190 citations till now. The article focuses on the topics: Reliability (statistics) & Support vector machine.read more
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Tunnel face stability in cohesion-frictional soils considering the soil arching effect by improved failure models
TL;DR: In this paper, an improved 3D rotational failure model based on the limit analysis method and an improved wedge-prism model were proposed to better assess the stability of tunnel faces in cohesion-frictional soils.
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Adaptive approaches in metamodel-based reliability analysis: A review
TL;DR: The extensive and comprehensive discussion presented aims to be a first step for the unification of the field of adaptive metamodeling in reliability; so that future implementations do not exclusively follow individual lines of research that progressively become more narrow in scope, but also seek transversal developments in the field.
Journal ArticleDOI
An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation
Jinhao Zhang,Mi Xiao,Liang Gao +2 more
TL;DR: An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation (AKEE-SS) is proposed, which is an accurate and efficient reliability analysis method for problems with highly nonlinear performance functions and small failure probabilities.
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A new bivariate dimension reduction method for efficient structural reliability analysis
TL;DR: A high-order unscented transformation is introduced to evaluate the two-dimensional integrals involved in BDRM, and the free parameter involved in HUT is suggested and it is found that the proposed method can keep the trade-off of accuracy and efficiency for structural reliability analysis.
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Support vector regression based metamodeling for structural reliability analysis
TL;DR: A simple yet effective algorithm by solving an optimization sub-problem to minimize the mean square error value obtained by cross-validation method is investigated in the present study to construct SVR model for structural reliability analysis.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support vector machine active learning with applications to text classification
Simon Tong,Daphne Koller +1 more
TL;DR: Experimental results showing that employing the active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings are presented.
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Remarks on a Multivariate Transformation
Hoek-brown failure criterion - 2002 edition
TL;DR: The Hoek-Brown failure criterion for rock masses is widely accepted and has been applied in a large number of projects around the world as discussed by the authors, however, there are some uncertainties and inaccuracies that have made the criterion inconvenient to apply and to incorporate into numerical models and limit equilibrium programs.
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AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation
TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.