Effect of SnO 2 , ZrO 2 , and CaCO 3 nanoparticles on water transport and durability properties of self-compacting mortar containing fly ash: Experimental observations and ANFIS predictions

Khotbehsara, Mojdeh Mehrinejad; Miyandehi, Bahareh Mehdizadeh; Naseri, Farzad; Ozbakkaloglu, Togay; Jafari, Faezeh; Mohseni, Ehsan

doi:10.1016/j.conbuildmat.2017.10.067

Cited by 64 publications

(21 citation statements)

References 49 publications

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“…In the fifth and last layer, each node is composed of a single node. Based on all input values of the lower layer, the output value is calculated using Equation (7). The output value has a continuous-type value rather than a fuzzy set type.…”

Section: • Layermentioning

confidence: 99%

“…Umrao [6] applied ANFIS to predict the compressive strength and elasticity of heterogeneous sedimentary rocks. Khotbehsara [7] applied ANFIS to predict SnO 2 performance and ZrO 2 and CaCO 3 nanoparticle solution transport and self-compression characteristics. Zamani [8] applied ANFIS to predict the ratio of diesel and gas in an oil reservoir.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Yeom

Kwak

2018

Symmetry

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In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.

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Section: • Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Yeom

Kwak

2018

Symmetry

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“…Concrete damage plasticity was used to model a concrete plate. Here, a stress-strain curve for compressive and tensile behavior of concrete was calculated [19][20][21][22][23]. This model was also able to define the failure behavior of the concrete in the software.…”

Section: Experimental Study and Modeling With Abaqusmentioning

confidence: 99%

“…Learngdm was used for training processes and this function was the gradient descent with momentum weight and bias learning function. Results from different neural network structures were evaluated based on two measures of accuracy [22]: The performances and accuracy of ANN models were evaluated according to statistical criteria, such as correlation coefficient (R2), and root mean square error (RMSE). Different researchers use these parameters for evaluating the efficiency of neural network models and the accuracy of ANN models [22].…”

Section: Modelling With Artificial Neural Networkmentioning

confidence: 99%

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

Badarloo

Jafari

2018

Buildings

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Use of composite steel shear walls (CSSW) in earthquake-resistant structures has grown in recent years. However, no thorough information exists on their performance, especially in cases where openings are present. In the present study, in order to first validate the analysis method, ABAQUS was used to model the studied composite shear wall with gap at UC-Berkeley, according to the results of which, a good agreement between the experimental and analytical models was observed. Then, the effect of the position and number of the openings on the performance of the walls was addressed. To this end, models with various openings, including openings close to the beam/column, horizontal/vertical openings and distributing opening, were prepared and analyzed. The results indicate that the maximum reduction in stiffness and strength occurred in walls with single openings. The size of opening and the opening’s area significantly affect shear wall performance. Ultimately, artificial neural network and fitness function tools were employed to obtain predictive models for shear wall performance. A neural network has proven an appropriate alternative method for predicting the displacement, stress, and strength of the composite shear wall.

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“…Using MATLAB to solve reliability and neural network method is current among researchers; the probabilistic method was employed by different researchers such as Lu et al to estimate safety index [11]. In some probabilistic and neural network, R 2 is used as the correlation coefficient in the experimental and numerical research [12][13][14] to obtain the accurate relationship between the experimental and the numerical method [15]. Using the guideline method to estimate the ultimate capacity of concrete structure has been seen in previous research, for example: Jafari et al (2017) investigated the behavior of concrete beam with the reliability method.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on the Effect of Concrete Grade on the CFT Circular Column’s Behavior under Axial Load

Badarloo

Jafari

2019

Civ Eng J

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Concrete-filled tubular (CFT) column improves the structure properties under different load pattern, so that it should be designed under two main load patterns (static and cyclic load) using current design method such as Finite Element Method (FEM) and analytical method (guideline equation). In this research, a CFT column with specific dimensions is modeled by using ABAQUS finite element software; the target of this study is to conduct a pushover analysis and also a hysteresis analysis under cyclic loading. Then, the concrete grade and percentage of column reinforcement were altered using the FEM, and eventually, compared with the results of analytical equations to measure the safety level of analytical equations. For this purpose, the CFT columns with C20, 30, 40 & 50 concrete cores were modeled with and without reinforcement, and the effect of concrete grade on the capacity of column was studied. In addition, MATLAB software was used to obtain beta index and load capacity design for the CFT column. The results demonstrated that the columns designed in accordance with the AISC have a good performance under the cyclic and static loading. The safety level of design equations ranged between 3 and 5, and the columns could resist higher loads (about 2.5-3.5 times) through the design by ABAQUS.

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Effect of SnO 2 , ZrO 2 , and CaCO 3 nanoparticles on water transport and durability properties of self-compacting mortar containing fly ash: Experimental observations and ANFIS predictions

Cited by 64 publications

References 49 publications

Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Performance Comparison of ANFIS Models by Input Space Partitioning Methods

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

Numerical Study on the Effect of Concrete Grade on the CFT Circular Column’s Behavior under Axial Load

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