Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

Sarıdemir, Mustafa; Topçu, İ̇lker Bekir; Özcan, Fatih; Severcan, Metin Hakan

doi:10.1016/j.conbuildmat.2008.07.021

Cited by 128 publications

(65 citation statements)

References 34 publications

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“…In the learning process, membership functions characterize the fuzziness in a fuzzy set, whether the elements in the set are discrete or continuous in a graphical form for eventual use in the mathematical formalism of fuzzy set theory [17,18]. Although FL was brought forward by Zadeh [14,15] in 1965, fuzzy concepts and systems attracted attention after a real control application in 1975 conducted by Mamdani and Assilian [15,19,20]. For control applications, fuzzy logic operations that include comparison of two or more membership functions are needed [21].…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…Zadeh has motivated his work on FL with the observation that the key elements in human thinking are not numbers but levels of fuzzy sets [15,16]. Fuzzy approach performs numerical computation by using linguistic labels stimulated by membership functions.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The concept of ''fuzzy set" was preliminarily introduced by Zadeh [14,15], who pioneered the development of fuzzy logic (FL) replacing Aristotelian logic which has two possibilities only. Zadeh has motivated his work on FL with the observation that the key elements in human thinking are not numbers but levels of fuzzy sets [15,16].…”

Section: Fuzzy Logicmentioning

confidence: 99%

See 2 more Smart Citations

A Fuzzy Logic Approach to Model and Predict HV Cable Insulation Behaviour under Thermal Aging

Bessissa¹,

Boukezzi

Mahi³

2014

APH

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Section: Fuzzy Logicmentioning

confidence: 99%

Section: Fuzzy Logicmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Logic Approach to Model and Predict HV Cable Insulation Behaviour under Thermal Aging

Bessissa¹,

Boukezzi

Mahi³

2014

APH

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“…where (net) is th hidden layer neurons weighted sum of the input layers, is the value of the th neuron of the input layer, is the connection weight of the th neuron in the input layer and the th neuron in the hidden layer, and is the deviation value [15,27].…”

Section: Development Of the Bpnn Modelmentioning

confidence: 99%

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Zhang

Wang

Huo

et al. 2017

Mathematical Problems in Engineering

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In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong'an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong'an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.

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“…One of the most popular artificial intelligence methods is fuzzy logic (FL). There are many studies available in the literature [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], aimed on estimating different concrete properties using fuzzy logic. In this study, a new fuzzy model, based on the Mamdani algorithm was introduced for prediction of compressive strength of lightweight pumice concretes, by using the fuzzy logic toolbox in MATLAB.…”

Section: Introductionmentioning

confidence: 99%

Rule-Based Mamdani-Type Fuzzy Logic Approach to Estimate Compressive Strength of Lightweight Pumice Concrete

Beycioğlu

Başyiğit

2015

Acta Phys. Pol. A

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In this study, a rule-based Mamdani-type fuzzy logic (RBMFL) model was developed for prediction of compressive strength of lightweight concretes containing silica fume (SF) and fly ash (FA). Pumice was used as the aggregate in the concretes. In the concrete mixture 0, 5, 10, 15 and 20% of fly ash and 0, 5, 10, 15 and 20% of silica fume, for each value of fly ash content, were added by replacing the cement. The compressive strength of the lightweight concretes was investigated experimentally. Experimental results were used to construct the fuzzy logic model. In the study, the values obtained from the model and experiment were divided into five groups (each group has five experimental results), according to the FA and SF contents, to evaluate approximate reasoning ability of RBMFL model. As a result, RBMFL model has shown satisfying relation with experimental results, which suggests an alternative approach to evaluation of compressive strength of lightweight concretes containing silica fume and fly ash.

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Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

Cited by 128 publications

References 34 publications

A Fuzzy Logic Approach to Model and Predict HV Cable Insulation Behaviour under Thermal Aging

A Fuzzy Logic Approach to Model and Predict HV Cable Insulation Behaviour under Thermal Aging

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Rule-Based Mamdani-Type Fuzzy Logic Approach to Estimate Compressive Strength of Lightweight Pumice Concrete

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