Journal ArticleDOI
Reliability analysis using radial basis function networks and support vector machines
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TLDR
It is shown that there is no obvious difference between RBFN-based RSMs and SVM- based RSMs, and the number of samples needed in RBFN/SVM-RSM2 is smaller than that of RBFN /SVM/RSM1.About:
This article is published in Computers and Geotechnics.The article was published on 2011-03-01. It has received 143 citations till now. The article focuses on the topics: Support vector machine.read more
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Review and application of Artificial Neural Networks models in reliability analysis of steel structures
TL;DR: In this article, the authors present a survey on the development and use of Artificial Neural Network (ANN) models in structural reliability analysis, identifying the different types of ANNs, the methods of structural reliability assessment that are typically used, the techniques proposed for ANN training set improvement and also some applications of ANN approximations to structural design and optimization problems.
Journal ArticleDOI
A review on prognostic techniques for non-stationary and non-linear rotating systems
TL;DR: In this paper, the authors present a review of the applicability of prognostic techniques for rotating machinery operating under non-linear and non-stationary conditions, as well as their application in the research field.
Journal Article
A review on prognostic techniques for non-stationary and non-linear rotating systems
TL;DR: In this paper, the authors present a review of the applicability of prognostic techniques for rotating machinery operating under non-linear and non-stationary conditions, as well as their application in the research field.
Journal ArticleDOI
Probabilistic analysis of underground rock excavations using response surface method and SORM
Qing Lü,Qing Lü,Bak Kong Low +2 more
TL;DR: In this article, a response surface is found using an iterative algorithm and the probability of failure is evaluated using the first-order and second-order reliability method (FORM/SORM).
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An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation
Qiujing Pan,Daniel Dias +1 more
TL;DR: 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.
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?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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.
Journal ArticleDOI
Number-theoretic methods in statistics
Kai-Tang Fang,Yuan Wang +1 more
TL;DR: In this paper, a number-theoretic method for numerical evaluation of multiple integral in statistics is presented, and its applications in statistics are discussed. But this method is not suitable for the analysis of multivariate distributions.
Journal ArticleDOI
Response‐Surface Approach for Reliability Analysis
TL;DR: In this paper, a polynomial expansion of the numerical nonlinear structural operator is made according to a response-surface approximation in terms of spatial averages of the design variables, which can be used for the analysis of structural and mechanical systems whose geometrical and material properties have spatial random variability.
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A fast and efficient response surface approach for structural reliability problems
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