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A performance evaluation of neuro‑fuzzy and regression methods in estimation of sediment load of selective rivers

Varvani J., Khaleghi M. R.

Acta Geophysica

2019

Vol. 67, no. 1

205--214

Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.

A new method for the evaluation of the yield criteria accuracy

Paraianu L., Banabic D.

Computer Methods in Materials Science

2009

Vol. 9, No. 1

148-152

A fair description of the plastic anisotropy of thin sheet metals plays an important role in forming operations, once material flow, springback as well as wrinkling and limit strains are significantly affected by the yield criterion used in the analysis. Such a criterion is a relationship which identifies the stress states capable to produce the transition of the material from the elastic domain to the plastic one. This relationship can be interpreted as being the equation of a geometrical entity in the stress space (the so-called yield surface). In the analytical relationships that describe the yield criteria, the components of stress tensor are accompanied by material parameters. The values of the parameters are determined using experimental data. During the last years, new yield functions were introduced in order to improve the fitting of the experimental results, especially for aluminium and magnesium alloys [1]. The CERTETA team has developed several anisotropic yield criteria [2],[3]. The paper presents a test used to estimate the predictive accuracy of a few yield criteria for anisotropic materials. The study is focused on the yield criteria developed at the CERTETA research center. The performances of the models have been tested on two sorts of sheet metals widely used in the industry: steel SPCEN and an aluminium alloy AA3103-0. The results (predicted yield locus, distributions of the uniaxial yield stress, anisotropy coefficients) show the influence of the number of material parameters included in the identification procedure on the accuracy of the criteria.

Artykuł przedstawia test wykonany w celu oszacowania przewidywanej dokładności kilku kryteriów plastyczności dla materiałów anizotropowych. Działanie modeli zostało przetestowane na przykładzie blachy cienkiej ze stopu aluminium AA3103-0 szeroko stosowanej w przemyśle. Wyniki (oszacowanie miejsca płynięcia materiału, jednoosiowy rozkład granicy plastyczności, współczynniki anizotropii) pokazały wpływ wielu parametrów materiału uwzględnionych w procedurze identyfikacji na dokładność analizowanych kryteriów