Experimental and Articial Neural Network Based Studies on Thermal Conductivity of Lightweight Building Materials

Sevim, Davut; Fidan, Şehmus; Polat, Süleyman; Oktay, Hasan

doi:10.23884/ejt.2017.7.1.04

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Where b is bias. The intention of bias entries is to balance the origin of the activation function to provide better learning [36]. Transfer function can have described by the following equation:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks

et al. 2020

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The objective of this article was to forecast the ultimate failure load laminate stacking sequence combination on bonding joints which are exposed to tensile strength by using artificial neural networks. We have glass fiber composite materials with three different sequence combinations ([0°/90°], [±45°], [0°/90°/±45°]). Various adherend thicknesses and also ductile type adhesive was used in the experiment. The bonding geometry is a single lap and has four types of overlap angles 30°, 45°, 60°, 75° respectively. The experimental results demonstrate that composite laminate stacking sequence profoundly affects the bonding joints of failure load. Taking experimental results into account, Levenberg-Marquardt learning algorithm model was used by preferring a three layer forward on ANN so as to discipline network. In order to procure a precise ANN tool, an integrate methodology of experimental method has been used. The outcomes are used to ensure the experimental data's to the ANN. The method of ANN permits surveying much adequately the probabilities of composite laminate stacking sequence combination using the prevalent ones which are [0°/90°], [±45°] and [0°/90°/±45°]. Testing data and training results were quite well 0.998, 0.997 and 0.998 in turn. Consequences acquired can be used by engineers who are interested in the composite material design to enhance failure load.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks

et al. 2020

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“…, the thermal examination was performed, under the suspicion that air in the two holes is still, to decide the impact of cavity perspective proportion. In 2017 DavutSevim, et al [25], an investigational examination is performed to anticipate the thermal protection properties of wall structures of which the mechanical properties are known; by utilizing Levenberg-Marquardt training algorithm based ANN technique for vitality effective buildings. The delivered tests are concrete based and have generally high protection properties for vitality proficient buildings.…”

Section: Thermal Performance Of Building Components Insulating By Polmentioning

confidence: 99%

“…At the point the neural system is encouraged with information in the input layer, the qualities are sent to at least one node in the next layer. This node carries out [25][26] estimations on the values they get, and promote the outcomes to more nodes in the next layer, which rehash the procedure ( figure 2). a) The movement of the input units speaks to the unprocessed data that is encouraged into the network.…”

Section: Training Based Optimal Neural Network (Onn)mentioning

confidence: 99%

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2019

IJEAT

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Construction of housing with levels of popularity on energy saving is the most significant tasks. Lessening the thermal raise during the rooftop and divider boards is the most ideal advance to manage declining energy usage and to giving thermal help to the customers of private or business structures. Thermal insulation of buildings assumes a vital function in upgrading the comfort level of tenants by lessening the temperature of the building. In this investigation, we focused to reduce the thermal qualities in building by protecting polystyrene material in roof ceilings and wall panels. Here, we investigate the temperature among two climatic conditions i.e. summer and winter. The temperature is noted for 3 hours with 10 minutes interval for totally 40 specimens. Different types of specimens are used i.e. Reinforced Cement concrete (RCC) and Fiber Reinforced Concrete (FRC) specimens based on treated and untreated with insulating materials. The experimental results are validated by using training based Optimal Neural Network (ONN); here the ONN structure is trained with the help of Levenberg- Marquardt (LM) algorithm. The optimization predicted temperature for each specimen with Minimum Error Rate (MSE) compared to existing approaches.

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“…The increment in the thermal performance of the sample is related to a decrease in unit weight and an increase in porosity with the addition of wastes. The microstructure of a material strongly affects the thermal conductivity [41]. Vimmarova et al [40] investigated the thermal performance of gypsumlimemetakaolin binders and the values changed between 0.198 -0.348 W/mK.…”

Section: Thermal Conductivitymentioning

confidence: 99%

Investigating the Utilization of Waste Glass Powder, Colemanite Ore Waste and Sugar Factory Filter Cake in Gypsum-Lime Based Mortars

Durgun

2019

European Journal of Technic

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The wastes generated by the recent industrial developments threaten the future of the world. Therefore, researches on the recycling of the industrial wastes in various ways are increasing day by day. In this study, glass powder, a waste of glass industry, colemanite ore waste, a by-product of boron industry and filter cake, one of the wastes of sugar factories, were used. The evaluation of these wastes in gypsum and lime based mortars has been investigated. In this study, 10, 20 and 30% of the waste materials were used instead of gypsum. Unit weights, ultrasonic pulse velocities, apparent porosities and thermal conductivity coefficients of the gypsum-lime based mortars were determined. Compressive strength and bending strength tests were performed to determine the mechanical properties. The results indicated that the use of wastes negatively affected the mechanical properties while improving the thermal properties.

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Experimental and Articial Neural Network Based Studies on Thermal Conductivity of Lightweight Building Materials

Abstract: Abstract:The growing concern about energy consumption of heating

Cited by 8 publications

References 17 publications

Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks

Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks

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Investigating the Utilization of Waste Glass Powder, Colemanite Ore Waste and Sugar Factory Filter Cake in Gypsum-Lime Based Mortars

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