Neural network modeling of strength enhancement for CFRP confined concrete cylinders

Çevik, Abdülkadir; Güzelbey, İbrahim H.

doi:10.1016/j.buildenv.2007.01.036

Cited by 77 publications

(29 citation statements)

References 9 publications

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“…neural network with 1 hidden node for the first and second hidden layers and ends up with optimal neural network architecture. The flowchart of the whole process can be found in relevant literature 64 .…”

Section: Background On Artificial Neural Networkmentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.

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Section: Background On Artificial Neural Networkmentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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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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“…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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Neural network modeling of strength enhancement for CFRP confined concrete cylinders

Cited by 77 publications

References 9 publications

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

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

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

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