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

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.

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Structural Reliability Assessment of Steel Four-Bolt Unstiffened Extended End-Plate Connections Using Monte Carlo Simulation and Artificial Neural Networks

Iranian Journal of Science and Technology Transactions of Civil Engineering ◽

10.1007/s40996-020-00480-z ◽

2020 ◽

Author(s):

Hamidreza Abbasianjahromi ◽

Somayeh Shojaeikhah

Keyword(s):

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Structural Reliability ◽

Reliability Assessment ◽

End Plate ◽

Artificial Neural ◽

Extended End Plate ◽

Plate Connections

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A Comparison of Traditional Segmentation Methods with Segmentation Based upon Artificial Neural Networks by Means of Conjoint Data from a Monte-Carlo-Simulation

Classification, Data Analysis, and Data Highways - Studies in Classification, Data Analysis, and Knowledge Organization ◽

10.1007/978-3-642-72087-1_41 ◽

1998 ◽

pp. 385-392 ◽

Cited By ~ 2

Author(s):

H. Gierl ◽

S. Schwanenberg

Keyword(s):

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Segmentation Methods ◽

Artificial Neural

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The Use of Quantitative Methods in Investment Decisions

Handbook of Research on Global Issues in Financial Communication and Investment Decision Making - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-5225-9265-5.ch013 ◽

2019 ◽

pp. 256-275 ◽

Cited By ~ 5

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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