A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches

Bilgehan, Mahmut

doi:10.1080/10589751003770100

Cited by 56 publications

(19 citation statements)

References 22 publications

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“…The efficiency of the inference engine depends on the internal organization of the knowledge base. The fuzzy rules can express conclusions to be drawn by generalization from the qualitative information stored in the knowledge base with the natural language [19]. Mamdani implication was used to compute the individual output membership functions.…”

Section: For Each Rule Calculate the Individual Outputs (5)mentioning

confidence: 99%

A Hybrid Neuro-fuzzy Model to Forecast Inflation

Enke

Mehdiyev

2014

Procedia Computer Science

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Section: For Each Rule Calculate the Individual Outputs (5)mentioning

confidence: 99%

A Hybrid Neuro-fuzzy Model to Forecast Inflation

Enke

Mehdiyev

2014

Procedia Computer Science

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“…S. Tesfamariam and H. Najjaran designed an adaptive network-fuzzy inference to estimate the concrete strength using mix design [24]. M. Bilgehan worked on a comparative study to estimate the concrete compressive strength using the neural network and the neuro-fuzzy modeling approaches [9]. M. L. Nehdi and M. T. Bassuoni found a Fuzzy logic approach to estimate durability of the concrete [6].…”

Section: Preview Researchmentioning

confidence: 99%

Designing a fuzzy expert system to predict the concrete mix design

Neshat

Adeli

2011

2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings

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The aim of this study is to design a Fuzzy Expert System to determine the concrete mix design. In the civil engineering, the determination of concrete mix design is so difficult and usually results in imprecision. Fuzzy logic is a way to represent a sort of uncertainty which is understandable for human. So, we can use the fuzzy logic to easily determine the concrete mix designs in a descriptive form. The input fields of system are Slump, Maximum Size of Aggregate (D max ), Concrete Compressive Strength (CCS) and Fineness Modulus (FM). The output fields are quantities of water, Cement, Fine Aggregate (F.A) and Course Aggregate (C.A). The experimental resultsshow that the average error of predicted compressive strength for FIS is 6.43%, the minimum error of which is 4.73%.

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“…where P i , A i andÃ i are the predicted, actual and averaged actual output of the network, respectively, and N is the total number of training patterns [49].…”

Section: Application Designmentioning

confidence: 99%

A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches

Cited by 56 publications

References 22 publications

A Hybrid Neuro-fuzzy Model to Forecast Inflation

A Hybrid Neuro-fuzzy Model to Forecast Inflation

Designing a fuzzy expert system to predict the concrete mix design

Comparison of ANFIS and NN models—With a study in critical buckling load estimation

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