Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

Khademi, Faezehossadat; Akbari, Mahmoud; Jamal, Sayed Mohammadmehdi; Nikoo, Mehdi

doi:10.1007/s11709-016-0363-9

Cited by 246 publications

(123 citation statements)

References 23 publications

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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

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Section: Figure 1bmentioning

confidence: 99%

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

Sohrabpoor¹

2017

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“…In the study of Asteris et al [23] ANNs were used to estimate the compressive strength of self-compacting concrete through a training process, involving eleven input parameters and one output parameter, which is compressive strength of concrete. Moreover, similar methods such as fuzzy logic and genetic algorithms have also been used for modelling the compressive strength of concrete [24][25][26][27][28][29][30][31][32]. A detailed state-of-the-art report can be found in earlier literatures [33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Masonry Compressive Strength Prediction Using Artificial Neural Networks

Argyropoulos

Cavaleri²,

Rodrigues

et al. 2019

Communications in Computer and Information Science

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The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.

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“…They concluded that their model estimates the shear contribution of FRP with good accuracy compared with the results of guidelines equations such as ACI 440.2R‐08, CSA‐S806, and fib‐TG9.3. In 2017, Khademi et al (Khademi, Akbari, Jamal, & Nikoo, ) conducted a study for predicting the 28 days compressive strength of concrete by using 173 various mix designs. Three data‐driven models (adaptive ANFIS, ANN, and multiple linear regression [MLR]) were used.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro‐fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer‐strengthened circular reinforced concrete columns

et al. 2019

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In this study, a fuzzy logic‐based model for predicting the ultimate strength of FRP‐confined circular reinforced concrete (RC) columns is presented. The adaptive neuro‐fuzzy inference system (ANFIS) model was generated using valid experimental data with seven input variables. Input parameters were considered in such a way that all the parameters affecting the compressive strength of the column were simultaneously involved. Different models for compressive strength of fiber reinforced polymer (FRP)‐confined concrete including the model in American Concrete Institute (ACI), to calculate the maximum stress endured by the column under axial load, were presented and compared with the results of the ANFIS model. Also, for similarity to other models, the ACI equation for calculating the maximum compressive strength tolerated by a column was considered without reducing coefficients as ACI‐N and was compared with other models. The results obtained from the ANFIS model were compared with results from other models. ANFIS model showed the highest accuracy among all models in predicting the experimental results.

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

Cited by 246 publications

References 23 publications

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

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

Masonry Compressive Strength Prediction Using Artificial Neural Networks

Adaptive neuro‐fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer‐strengthened circular reinforced concrete columns

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