Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Sonebi, Mohamed; Çevik, Abdülkadir

doi:10.1016/j.conbuildmat.2009.02.012

Cited by 78 publications

(31 citation statements)

References 4 publications

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“…All the formulations vary in function of f c and some with the concrete density [16,18]. 6 The variability obtained from the tests for SCC will be compared with the standard values assumed for ordinary concrete. The variability is measured by parameters like SD and COV.…”

Section: Statistical Parameters Analyzedmentioning

confidence: 99%

“…In this sense of particular studies, in [5] is shown how the hardened mechanical properties of SCC vary along the height of slender columns and relates such variations to the mesostructure of the material. In the work by Sonebi and Cevik [6], the feasibility of obtaining properties of the fresh SCC as well as the compressive strength by the use of artificial neural networks (Genetic Programming) is presented. The results of the present study are also obtained in the direction pointed in [4].…”

Section: Introductionmentioning

confidence: 99%

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Hardened properties of self-compacting concrete — A statistical approach

Filho¹,

Barragán

Casas

et al. 2010

Construction and Building Materials

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This work presents a statistical stud y on the variability of the mechanical properties of hardened self-compacting concrete, including the compressive strength, splitting tensile strength and modulus of elasticity. The comparison of the experimental results with those derived from several codes and recommendatio ns allows evaluating if the hardened behavio ur of self-compacting concrete can be appropriately predicted by the existing formulations. The variables analyzed include the maximum size aggregate, paste and gravel content. Results from the analyzed self-compacting concretes presented variability measures in the same range than the expected for conventional vibrated concrete, with all the results within a confidence level of 95%. From several formulations for conventional concrete considered in this study, it was observed that a safe estimation of the modulus of elasticity can be obtained from the value of compressive strength; with lower strength self-compacting concretes presenting higher safety margins. However, most codes overestimate the material tensile strength.

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Section: Statistical Parameters Analyzedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hardened properties of self-compacting concrete — A statistical approach

Filho¹,

Barragán

Casas

et al. 2010

Construction and Building Materials

View full text Add to dashboard Cite

show abstract

“…After the specified curing period was over (7,28 and 90 days for NVC series and 2, 28 and 90 days for SCC series), the concrete cubes were subjected to splitting tensile test by using universal testing machine. The tests were carried out triplicately.…”

Section: Test Proceduresmentioning

confidence: 99%

“…In this sense, GP can be accepted to be superior to regression techniques and neural networks. GP has proven to be an effective tool to model and obtain explicit formulations of experimental studies including multivariate parameters where there are no existing analytical models 7,8 . In our previous works, the effects of different types of nanoparticles on physical and mechanical aspects of concrete specimens were studied [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

“…It is convenient to use these models for numerical experiments to review the effects of each variable on the mix proportions [4][5][6] . Besides ANNs, genetic programming (GP) has begun to arise for the explicit formulation of the properties and the performances of concrete recently 7,8 . Genetic programming offers many advantages as compared to classical regression techniques.…”

Section: Introductionmentioning

confidence: 99%

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Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete

Nazari

Azimzadegan

2012

Mat. Res.

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In the present paper, two models based on artificial neural networks (ANN) and gene expression programming (GEP) for predicting splitting tensile strength and water absorption of concretes containing ZnO 2 nanoparticles at different ages of curing have been developed. To build these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The used data in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models, the splitting tensile strength and water absorption values of concretes containing ZnO 2 nanoparticles were predicted. The training and testing results in these two models have shown the strong potential of the models for predicting the splitting tensile strength and water absorption values of concretes containing ZnO 2 nanoparticles. Although neural networks have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural networks.

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How Competitive Is Genetic Programming in Business Data Science Applications?

Kordon¹,

Kotanchek

2020

Genetic Programming Theory and Practice XVII

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Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Cited by 78 publications

References 4 publications

Hardened properties of self-compacting concrete — A statistical approach

Hardened properties of self-compacting concrete — A statistical approach

Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete

How Competitive Is Genetic Programming in Business Data Science Applications?

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