Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study

Çolak, Andaç Batur; Akçaözoğlu, Kubilay; Akçaözoğlu, Semiha; Beller, Gülhan

doi:10.1007/s13369-020-05280-1

Cited by 24 publications

(9 citation statements)

References 23 publications

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“…Each layer is interconnected by a computing element called a neuron. The most common type of algorithm utilized for MLP networks is feed‐forward (FF) back‐propagation (BP) algorithm 51 . In FF‐BP algorithm, the processing of information distributed from input layer to output layer is done at this level.…”

Section: Artificial Neural Network Designmentioning

confidence: 99%

“…The most common type of algorithm utilized for MLP networks is feed-forward (FF) back-propagation (BP) algorithm. 51 In FF-BP algorithm, the processing of information distributed from input layer to output layer is done at this level. During the back-propagation phase, the input layer is returned to correct errors between prediction data and target data.…”

Section: Artificial Neural Network Designmentioning

confidence: 99%

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Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

Shafiq

Çolak

Sindhu

2021

Numerical Methods in Fluids

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115

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In this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single‐walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid–fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge–Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.

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Section: Artificial Neural Network Designmentioning

confidence: 99%

Section: Artificial Neural Network Designmentioning

confidence: 99%

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

Shafiq

Çolak

Sindhu

2021

Numerical Methods in Fluids

Self Cite

115

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“…The proposed correlations and comparisons between ANNs have also shown that ANNs are more useful than correlations. Çolak et al [41] experimentally investigated the effect of high temperature on the flexural and compressive strength of mortars containing waste PET aggregates and proposed an ANN model. Mortar samples prepared in 5 different concentrations with waste PET aggregate substitution were heated up to 100, 150, 200, 250, 300 and 400°C and their flexural and compressive strengths were experimentally measured after waiting 1, 2 and 3 h at these temperatures.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Investigation on Workability and Bleeding Behaviors of Cement Pastes Doped with Nano Titanium Oxide (n-TiO2) Nanoparticles and Fly Ash

Çeli̇k¹,

Yıldız²,

Çolak³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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In this study, the workability of cement-based grouts containing n-TiO 2 nanoparticles and fly ash has been investigated experimentally. Several characteristic quantities (including, but not limited to, the marsh cone flow time, the mini slump spreading diameter and the plate cohesion meter value) have been measured for different percentages of these additives. The use of fly ash as a mineral additive has been found to result in improvements in terms of workability behavior as expected. Moreover, if nano titanium oxide is also used, an improvement can be obtained regarding the bleeding values for the cement-based grout mixes. Using such experimental data, a multi-layer perceptron artificial neural network model has been developed (5 neurons in the hidden layer of the network model have been developed using a total of 42 experimental data). 70% of the data employed in this model have been used for training, 15% for validation and 15% for the test phase. The results demonstrate that the artificial neural network model can predict Marsh cone flow time, mini slump spreading diameter and plate cohesion meter values with an average error of 0.15%.

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“…Çolak et al used ANN-based intelligent approach to predict various parameters and to bring up the utilization of neural network as a method to validate experimental results. [8][9][10][11] Moreover, ANN can evaluate the complex interaction effects of the process parameter variables. 7,12 Previously, it has been used in the optimization of the production of microbial enzymes such as α-galactosidase, 13 α-amylase 14 and enzymatic saccharification of apple pomace 15 and water hycinth.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

Kumar

Saha

Ghosh

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The industrial production of enzymes is generally optimized by one-factor-at-a-time (OFAT) approach. However, enzyme production by the method involves submerged or solid-state fermentation, which is laborious and time-consuming and it does not consider interactions among process variables. Artificial neural network (ANN) offers enormous potential for modelling biochemical processes and it allows rational prediction of process variables of enzyme production. In the present work, ANN has been used to predict the experimental values of xylanase production optimized by OFAT. This makes the reported ANN model to predict further optimal values for different input conditions. Both single hidden layered (6-3-1) and double hidden layered (6-12-12-1) were able to closely predict the actual values with MSE equals to 0.004566 and 0.002156, respectively. The study also uses multiple linear regression (MLR) analysis to calculate and compare the outcome with ANN predicted xylanase activity, and to establish a parametric sensitivity.

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Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study

Cited by 24 publications

References 23 publications

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

An Experimental Investigation on Workability and Bleeding Behaviors of Cement Pastes Doped with Nano Titanium Oxide (n-TiO2) Nanoparticles and Fly Ash

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

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