Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation

1996 ◽  
Vol 136 (1-2) ◽  
pp. 145-163 ◽  
Author(s):  
Manolis Papadrakakis ◽  
Vissarion Papadopoulos ◽  
Nikos D. Lagaros
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Elastic Plastic

Research on the Application of Radial Basis Function Neural Network to Structural Reliability Analysis of Gravity Dam

2014 ◽  
Vol 496-500 ◽  
pp. 2505-2510 ◽  
Author(s):  
Yun Ji ◽  
Xiao Qing Liu ◽  
Tong Chun Li ◽  
Shuo Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Global Convergence ◽  
Basis Function ◽  
Structural Reliability ◽  
Gravity Dam ◽  
Performance Function

The performance function tends to be implicit or nonlinear in the evaluation of gravity dam reliability, making it difficult to apply some classical methods, such as JC method, Monte-Carlo simulation etc., as they are supposed to be too time-consuming. One possible solution to this problem may be the introduction of artificial neural networks, among which RBF is featured with faster convergence, better precision and can realize global convergence to some extent. In this paper, the application of RBF in gravity dam reliability is investigated, with some examples presented to convince that its reasonable to put it into use.

The Use of Quantitative Methods in Investment Decisions

2019 ◽  
pp. 256-275 ◽  
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

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