Journal ArticleDOI
A new artificial neural network-based response surface method for structural reliability analysis
TLDR
In this article, a new artificial neural network-based response surface method in conjunction with the uniform design method for predicting failure probability of structures is presented, which involves the selection of training datasets for establishing an ANN model, approximation of the limit state function by the trained ANN model and estimation of the failure probability using first-order reliability method (FORM).About:
This article is published in Probabilistic Engineering Mechanics.The article was published on 2008-01-01. It has received 164 citations till now. The article focuses on the topics: Artificial neural network.read more
Citations
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Journal ArticleDOI
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
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.
Journal ArticleDOI
Reliability analysis using radial basis function networks and support vector machines
TL;DR: 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.
Journal ArticleDOI
Optimal design of aeroengine turbine disc based on kriging surrogate models
TL;DR: A design optimization method based on kriging surrogate models is proposed and applied to the shape optimization of an aeroengine turbine disc to improve the accuracy of surrogate models without significantly increasing computational cost.
Journal ArticleDOI
Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks
TL;DR: In this paper, the authors use accurate and efficient means to evaluate system reliability again and optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, and it is essential to use accurate, efficient, and accurate means for evaluating system reliability.
References
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Journal ArticleDOI
Bayesian interpolation
TL;DR: The Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data by examining the posterior probability distribution of regularizing constants and noise levels.
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
A new look at the response surface approach for reliability analysis
TL;DR: In this article, a polynomial approximation of actual limit states in the reliability analysis is used to reduce the number of analyses required by using closed-form mechanical models to predict the behavior of complex structural systems.
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
Neural Networks in Civil Engineering. I: Principles and Understanding
Ian Flood,Nabil A. Kartam +1 more
TL;DR: An understanding of how these devices operate is developed and the main issues concerning their use are explained, including factors affecting their ability to learn and generalize.
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