Prediction of web crippling strength of cold-formed steel sheetings using neural networks

Güzelbey, İbrahim H.; Çevik, Abdülkadir; Erkliğ, Ahmet

doi:10.1016/j.jcsr.2006.01.008

Cited by 85 publications

(40 citation statements)

References 11 publications

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“…To overcome optimization difficulty, a program has been developed in Matlab, which handles the trial-and-error process automatically [35][36][37][38] . The program tries various functions and when the highest RMSE (root mean squared error) of the testing set, as the training of the testing set, is achieved, it was reported [35][36][37][38] .…”

Section: Anfis Model Structure and Parametersmentioning

confidence: 99%

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Khalaj

Nazari²,

Livary³

2013

Mat. Res.

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The paper presents some results of the research connected with the development of new approach based on the Adaptive Network-based Fuzzy Inference Systems (ANFIS) of predicting the Vickers microhardness of the phase constituents occurring in five steel samples after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. To construct these models, 114 different experimental data were gathered from the literature. The data used in the ANFIS model is arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the ANFIS systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels.

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Section: Anfis Model Structure and Parametersmentioning

confidence: 99%

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Khalaj

Nazari²,

Livary³

2013

Mat. Res.

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“…Now using formulae (10) and (11) and (18) and (19), the linear stiffness before buckling and after buckling can be determined for plates without an imperfection. This is shown in Fig.…”

Section: Elastic Initial Post-buckling Behaviourmentioning

confidence: 99%

Analytical and finite element modelling of long plate mode jumping behaviour

Hofmeyer¹,

Courage²

2013

Thin-Walled Structures

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“…However, there is no well defined rule or procedure to have an optimal architecture and parameter settings where the trial and error method still remains valid. This process is very time consuming [28][29][30][31] . In this study the MATLAB FL toolbox is used for FL applications.…”

Section: Architecture Of Fuzzy Logicmentioning

confidence: 99%

“…In this study the MATLAB FL toolbox is used for FL applications. To overcome optimization difficulty, a program has been developed in MATLAB which handles the trial and error process automatically [28][29][30][31] . The program tries various numbers of parameters for the algorithm when the highest RMSE (Root Mean Squared Error) of the testing set, as the training of the testing set is achieved [28][29][30][31] .…”

Section: Architecture Of Fuzzy Logicmentioning

confidence: 99%

Prediction total specific pore volume of geopolymers produced from waste ashes by fuzzy logic

Nazari

Khalaj

2012

Mat. Res.

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In the present work, total specific pore volume of inorganic polymers (geopolymers) made from seeded fly ash and rice husk bark ash has been predicted by fuzzy logic. Different specimens, made from a mixture of fly ash and rice husk bark ash in fine and coarse form together with alkali activator made of water glass and NaOH solution, were subjected to porosimetry tests at 7 and 28 days of curing. The curing regime was different: one set of the specimens were cured at room temperature until reaching to 7 and 28 days and the other sets were oven cured for 36 hours at the range of 40-90 °C and then cured at room temperature until 7 and 28 days. A model based on fuzzy logic for predicting the total specific pore volume of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of fuzzy logic models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk bark ash in the ashes mixture, the percentage of coarse rice husk bark ash in the ashes mixture, the temperature of curing and the time of water curing. According to the input parameters, in the fuzzy logic model, the pore volume of each specimen was predicted. The training and testing results in the fuzzy logic model have shown a strong potential for predicting the total specific pore volume of the geopolymer specimens in the considered range.

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Prediction of web crippling strength of cold-formed steel sheetings using neural networks

Cited by 85 publications

References 11 publications

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Analytical and finite element modelling of long plate mode jumping behaviour

Prediction total specific pore volume of geopolymers produced from waste ashes by fuzzy logic

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