Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete

Yadollahi, Ali; Nazemi, Ehsan; Zolfaghari, A.; Ajorloo, A.M.

doi:10.1016/j.pnucene.2016.02.010

Cited by 62 publications

(29 citation statements)

References 15 publications

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“…Chiew et al [19] predicted the properties of concrete using fuzzy adaptive resonance theory neural network and found the optimal mixtures based on similarity measurement. However, we should note that the methods in references [15][16][17][18][19] have some weak points. Previous studies mainly focused on the material cost of mixtures and ignored the carbon pricing [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Kao et al [17] analyzed the strength and slump of blended concrete using neural networks, and categorized the design dataset considering strength, mineral admixture content, and material cost. Yadollahi et al [18] estimated the strength and slump of radiation shielding concrete using neural works and found the optimal mixtures based on parameter analysis of neural networks. Chiew et al [19] predicted the properties of concrete using fuzzy adaptive resonance theory neural network and found the optimal mixtures based on similarity measurement.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies mainly focused on the material cost of mixtures and ignored the carbon pricing [15][16][17][18][19]. Moreover, the effect of increasing the carbon pricing on mix design is not highlighted [15][16][17][18][19]. On the other hand, neural networks are a local search optimization method and are likely to fall into local extremum [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Carbon Pricing on Optimal Mix Design of Sustainable High-Strength Concrete

Wang

2019

Sustainability

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Material cost and CO2 emissions are among the vital issues related to the sustainability of high-strength concrete. This research proposes a calculation procedure for the mix design of silica fume-blended high-strength concrete with an optimal total cost considering various carbon pricings. First, the material cost and CO2 emission cost are determined using concrete mixture and unit prices. Gene expression programming (GEP) is used to evaluate concrete mechanical and workability properties. Second, a genetic algorithm (GA) is used to search the optimal mixture, considering various constraints, such as design compressive strength constraint, design workability constraint, range constraints, ratio constraints, and concrete volume constraint. The optimization objective of the GA is the sum of the material cost and the cost of CO2 emissions. Third, illustrative examples are shown for designing various kinds of concrete. Five strength levels (from 95 to 115 MPa with steps of 5 MPa) and four carbon pricings (normal carbon pricing, zero carbon pricing, five-fold carbon pricings, and ten-fold carbon pricings) are considered. A total of 20 optimal mixtures are calculated. The optimal mixtures were found the same for the cases of normal CO2 pricing and zero CO2 pricing. Optimal mixtures with higher strengths are more sensitive to variation in carbon pricing. For five-fold CO2 pricing, the cement content of mixtures with higher strengths (105, 110, and 115 MPa) are lower than those of normal CO2 pricing. As the CO2 pricing increases from five-fold to ten-fold, for mixtures with a strength of 110 MPa, the cement content becomes lower. Summarily, the proposed method can be applied to the material design of sustainable high-strength concrete with low material cost and CO2 emissions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Carbon Pricing on Optimal Mix Design of Sustainable High-Strength Concrete

Wang

2019

Sustainability

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“…A. Yadollahi et al used neural network and taguchi method for predicting the optimal mixture of radiation shielding concrete. Authors found that machine learning is the best way to make the work without consuming much time and cost [5]. Temel Varol et.al used , an ANN model for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy AlCuMg/B4Cp composites This model can be used to predict the densification behavior of AlCu Mg / B4Cp composites produced with different siz es and quantities reinforced with different millng times and compact pressures [6].The ANN model was developed to pre dict natural rubber (NR) composite fatigue properties.The m echanical properties of natural rubber composites and Viscoelasticity propertieswere used as input vectors whi le fatigue properties (tensile fatigue life) were used as output vector.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

Antony¹,

Prajna²,

Jnanesh³

2019

IJRTE

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In the present study, machine learning approaches have been developed to predict the 180 days aged core shear strength of sandwich composites. The characteristics of the sandwich composites depends on the number of factors namely fibre type i.e., Chopped strand Mat, Stitched, Chopped strand Mat and Woven Roving, core density, bond between the core and the face sheets and the ability to bear the load in flexural mode. In the current approach deep learning and SVR models were worked out by taking on six different parameters namely foam density, aging temperature and variety of fiber types as input variables. For each set of these input variables, the 180 days aged shear strength of sandwich composites with a test frequency of 30 days was determined. The paper aims at predicting the core shear strength value of stitch bond sandwich composites using other three aforementioned fibers. To create the model and confirm the accuracy of the algorithm training and test data are considered. The results obtained revealed that the deep learning model develo ped provides better predictive ability than the model of SVR.

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“…Kostic and Vasovic [5] built a model for compressive strength of basic concrete. Chithiraet al [6] predicted the compressive strength of high strength concrete containing nano-silica, Yadollahi et al [7] modeled the optimal mixture ratio of radiation shielding concrete, Jiang et al [8] estimated the concrete corrosion, Şimşek et al [9] predicted the normal weight concrete properties with fuzzy approach, Yan and Lin [10] evaluated the anchorage reliability of concrete. However, many of studies containing NN application do not involve any studies about evaluation of training parameters.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Ultrasonic Pulse Velocity and the Modulus of Elasticity of Polymer Concrete

2016

IJACEBS

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Abstract-In this study, polymer concrete properties such as ultrasonic pulse velocity and the modulus of elasticity were modeled by artificial neural network. Seven polymers were selected as high density polyethylene, low density polyethylene, polypropylene, thermoplastic elastomer, dimethyl terephthalate, polyethylene terephthalate, polyethylene naphthalate and twenty-seven experiments was used to train the network. Two type of network training such as cascade forward back prop and feed forward back prop containing one hidden layer which has ten neurons was compared to predict the ultrasonic pulse velocity and the modulus of elasticity. Moreover, five experiments were used for comparing of the network models. The result show that feed forward back prop type training is more effective to predict the ultrasonic pulse velocity and the modulus of elasticity of polymer concrete comparing the cascade forward back prop training type.Keywords-A Neural Network Model, Polymer Concrete, Ultrasonic Pulse Velocity and the Modulus of Elasticity.

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Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete

Cited by 62 publications

References 15 publications

Effect of Carbon Pricing on Optimal Mix Design of Sustainable High-Strength Concrete

Effect of Carbon Pricing on Optimal Mix Design of Sustainable High-Strength Concrete

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

A Neural Network Model for Ultrasonic Pulse Velocity and the Modulus of Elasticity of Polymer Concrete

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