Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

Khademi, Faezehossadat; Jamal, Sayed Mohammadmehdi; Deshpande, Neela; Londhe, Shreenivas

doi:10.1016/j.ijsbe.2016.09.003

Cited by 250 publications

(112 citation statements)

References 35 publications

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“…This method was implemented in other industry such as civil engineering, electrical power, construction and building materials and sustainable environment [3], [106], [107].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Muhammad¹,

Sapuan²,

Mastura³

2018

J. MECH. ENG. SCI.

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Materials selection in manufacturing process is an important stage and should be performed in parallel with selection of manufacturing process. In automotive industry, production of green automotive component could utilize the natural sources such as plant fibres. In recent years, several multi-criteria decision-making (MCDM) techniques are suggested to choose the best materials for particular application. Materials selection tools for natural fibre composites are studied from past researchers with the summary of the advantage and disadvantages. In addition, new optimization approach in materials selection by using statistical analysis such as multiple linear regression (MLR), response surface methodology (RSM) and Taguchi method (TM) is proposed in this study. The proposed method could evaluate the criteria or attribute in materials selection precisely by analyse the relationship of the parameters, goodness of fit, correlation, analysis of variance (ANOVA), determination of coefficient and the significant criteria in to the desired goal of the design problem.

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“…This method was implemented in other industry such as civil engineering, electrical power, construction and building materials and sustainable environment [3], [106], [107].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Muhammad¹,

Sapuan²,

Mastura³

2018

J. MECH. ENG. SCI.

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“…It is one of the strongest materials ever used with tensile strengths over 130 GP making it 200 times as strong as steel [14].The unique chemical structure of Graphene has been attractive for biologists and biomedical properties [15]. …”

Section: Figure 1bmentioning

confidence: 99%

Perspective of Applying Adaptive Neuro Fuzzy Inference System (ANFIS) in Laser Cladding of Graphene-Metal Alloys

Sohrabpoor¹

2017

NTMB

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“…Data based prediction models including ANN, Multiple Linear Regression (MLR) are widely used in various engineering applications [14][15][16][17]. These models can give further information for a better understanding of the material properties [18]. Among the prediction models, ANN provides more accurate predictions for concrete mechanical properties [19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

Yıldızel¹,

Uzun²,

Çöğürcü³

2019

ijisae

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Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.

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Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

Cited by 250 publications

References 35 publications

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Perspective of Applying Adaptive Neuro Fuzzy Inference System (ANFIS) in Laser Cladding of Graphene-Metal Alloys

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

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