Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches

Gençel, Osman; Köksal, Fuat; Şahin, Murat; Durgun, Muhammed Yasin; Lobland, Haley E. Hagg; Brostow, Witold

doi:10.1080/08916152.2012.669810

Cited by 28 publications

(15 citation statements)

References 57 publications

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“…However, the variation of bulk densities of the porcelain samples in this study as shown in Table IV might have been caused by the method of production adopted composition of the raw materials made and the sintering temperature. Table V shows that P-3 sample has the highest bending strength of 30.54 MPa at 20 wt% of Kalalani vermiculite content when sintered at 1250 o C. This might be due to vitrification and densification which filled the microcracks and voids in the microstructure of the porcelain sample [19]. Therefore, the mechanical properties of ceramic sample are strongly dependent on composition made, the sintering temperature and the sintering time [20].…”

Section: Tab IIImentioning

confidence: 99%

Evaluation of Kalalani vermiculite for production of high strength porcelain insulators

Ngayakamo

Park

2019

SCI SINTER

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The present work has evaluated Kalalani vermiculite as a potential raw material for the production of high strength porcelain insulators. Three porcelain compositions were prepared to contain 0, 20 and 30 wt% of Kalalani vermiculite. Porcelain samples were fabricated using a semi-drying method. The chemical, mineralogical phases and microstructural characterization of the raw materials were carried out using XRF, XRD, and SEM techniques, respectively. Water absorption, bulk density, dielectric and bending strengths were performed on porcelain samples fired up to 1300 o C. However, at the sintering temperature of 1250 o C, the porcelain sample with 20 wt% of Kalalani vermiculite gave the dielectric strength of 61.3 kV/mm, bending strength of 30.54 MPa, bulk density of 2.21 g/cm 3 and low water absorption value of 0.21 % which is the prerequisite properties for high strength porcelain insulators. It was therefore concluded that Kalalani vermiculite has the potential to be used for the production of high strength porcelain insulators.

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Section: Tab IIImentioning

confidence: 99%

Evaluation of Kalalani vermiculite for production of high strength porcelain insulators

Ngayakamo

Park

2019

SCI SINTER

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“…The prediction results matched well with the experimental results. Gencel et al (2011Gencel et al ( , 2013 adopted an artificial neural network and linear regression algorithm to study the abrasion resistance of concrete with different constituents. The results demonstrated that an artificial neural network is more reliable than a conventional linear regression algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the strength of concrete radiation shielding based on LS-SVM

Ren

Shen

2015

Annals of Nuclear Energy

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“…However, there have been only a few studies conducted to investigate the thermal property of concrete materials. Gencel et al [23] predicted the thermal conductivity of concrete with vermiculite by using ANN with 20 data set. Experimental results were compared with those of the ANN model.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions

Fidan

Oktay

Polat

et al. 2019

Advances in Materials Science and Engineering

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Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.

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Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches

Cited by 28 publications

References 57 publications

Evaluation of Kalalani vermiculite for production of high strength porcelain insulators

Evaluation of Kalalani vermiculite for production of high strength porcelain insulators

Prediction of the strength of concrete radiation shielding based on LS-SVM

An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions

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