Predicting the Poisson Ratio of Lightweight Concretes using Artificial Neural Network

Davraz, Metin; Kılınçarslan, Şemsettin; Ceylan, Hakan

doi:10.12693/aphyspola.128.b-184

Cited by 18 publications

(12 citation statements)

References 5 publications

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“…In addition, LWC can provide energy saving because of its natural insulating character [8]. In designing of this concrete, lightweight aggregates are generally preferred.…”

Section: Introductionmentioning

confidence: 99%

Design of Self Compacting Lightweight Concrete Using Acidic Pumice with Different Powder Materials

Bozkurt

Taşkın

2017

Acta Phys. Pol. A

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This paper reports an experimental study on the design of self-compacting lightweight concrete using acidic pumice with different powder materials. For this aim, nine self-compacting lightweight concretes were designed with inclusion of two different fine aggregates and different powder materials. This way, two groups of concrete were designed. First group was composed of single type of aggregate which is acidic pumice from Bitlis Region with barite powder, fly ash powder and pumice powder inclusion, while the second group was generated with river sand as a fine aggregate and acidic pumice from Bitlis Region, as a coarse aggregate with the same powder addition. In the design process, slump-flow, V-funnel and L-box tests were applied to determine the fresh properties of selfcompacting lightweight concrete. After the design, test of compressive strength, which is one of the most important parameters of concrete, was applied to all self-compacting lightweight concretes in early age. Moreover, ultrasound pulse velocity test was also performed on all concrete series at the same ages.

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“…In addition, LWC can provide energy saving because of its natural insulating character [8]. In designing of this concrete, lightweight aggregates are generally preferred.…”

Section: Introductionmentioning

confidence: 99%

Design of Self Compacting Lightweight Concrete Using Acidic Pumice with Different Powder Materials

Bozkurt

Taşkın

2017

Acta Phys. Pol. A

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“…There are a lot of special concrete technology and special concrete researches in literature. Foam concrete, lightweight concrete and heavy concrete can be given as examples [5][6][7]. In this study, the strength properties of FRSCC created as combination of two special concretes, the SCC and FRC, * corresponding author; e-mail: nbozkurt@beu.edu.tr were investigated at different curing ages.…”

Section: Introductionmentioning

confidence: 99%

The Strength Properties of Fibre Reinforced Self Compacting Concrete

Bozkurt

Yazıcıoğlu

2017

Acta Phys. Pol. A

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This paper reports results of an experimental study of the strength properties of fibre reinforced self-compacting concrete (FRSCC). For this aim, a control self-compacting concrete and 24 FRSCCs were designed, applying fresh self-compacting concrete criteria tests. In the design of FRSCC, four steel and two polypropylene synthetic fibres of different lengths and aspect ratios were used. These fibres were used alone and in combinations with two and four of these fibres. In this way, not only the effect of single fibre and the synergy effect of hybrid fibres, but also the size and concentration effect of fibres, which had different ratios in total fibre volume, were investigated. In the design process, Portland cement and fly ash were used as the binder and the powder material. The compressive and flexural-tensile strength tests were employed at the age of 3, 7, 28, 56 and 90 curing days, to determine the strength properties of FRSCCs. Moreover, ultrasound pulse velocity test was also performed on all concrete series at the same ages.

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“…In the last two decades, neural networks (NNs) have been proposed as an alternative to conventional regression analysis and to numerical methods, used in estimation of hydrological and engineering data [3][4][5][6][7][8][9][10]. NNs are relatively stable with respect to noise in data and have a good generalization potential, to represent inputoutput relationships [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Output Neural Networks for Estimation of Synthetic Unit Hydrograph Parameters: A Case Study of a Catchment in Turkey

2017

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For developing unit hydrographs of catchments, the detailed information about the rainfall and the resulting flood hydrographs are needed. Such information, however, is available only for a few locations and for the remote locations such information is normally very scanty. In this study, Snyder based synthetic unit hydrographs were developed by using both, the digitized map and the digital elevation model of a case study of a small catchment in Turkey. Multi-output neural network technique was applied to predict three unit hydrograph parameters: peak discharge qp, time to peak tp and time base t b of a number of unit hydrographs observed in the catchment, based on most relevant geomorphological and meteorological parameters. Multi-output neural network was observed to outperform the conventional synthetic unit hydrograph methods. The advantage of the proposed multi-output neural network is based on the fact that it predicts the three parameters of the unit hydrograph, based on a single model, compared to the conventional neural network technique, which utilizes a model for each parameter.

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Predicting the Poisson Ratio of Lightweight Concretes using Artificial Neural Network

Cited by 18 publications

References 5 publications

Design of Self Compacting Lightweight Concrete Using Acidic Pumice with Different Powder Materials

Design of Self Compacting Lightweight Concrete Using Acidic Pumice with Different Powder Materials

The Strength Properties of Fibre Reinforced Self Compacting Concrete

Multi-Output Neural Networks for Estimation of Synthetic Unit Hydrograph Parameters: A Case Study of a Catchment in Turkey

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