Revelence of Multiple Breathing Cracks on Fixed Shaft Using ANFIS and ANN

Nanda, J.; Das, L. D.; Choudhury, Sasanka; Parhi, Dayal R.

doi:10.1007/978-981-15-2696-1_58

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…In 2020, Zhou et al 21 built an Artificial Neural Network (ANN) model that assessed the effects of various variables on the bond strength based on a large database from an extensive survey of existing single‐lap shear bond tests on FRP‐concrete specimens 21,22 . Neuro‐fuzzy and Group Method of Data Handling (GMDH) modeling have been applied in many literature works referred to several fields like engineering geology, 23‐26 mechanical engineering, 27‐29 electrical engineering, 30 science 31 and industrial ergonomics 32 . The results of researches reported that soft computing methods in predicting are accurate and reliable 23‐27,29‐33 …”

Section: Introductionmentioning

confidence: 99%

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

Alizadeh

Naderpour

Mirrashid

2020

Engineering Reports

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The bond strength among the concrete and reinforced bars is one of the most important factors in designing the reinforced concrete structures. One of the serious problems encountered by utilization of steel-reinforcement bars is corrosion when exposed to different environments. Glass fiber-reinforced polymers (GFRPs) are known as a solution to prevent the destruction of the civil infrastructure. The present study attempts to predict the bond strength between the GFRP bars and concrete based on the neuro-fuzzy inference system and artificial neural networks using 159 beam specimens including notched, splice, hinged, and inverted hinged. The neuro-fuzzy inference system consists of five input series. The output, which is the bond strength, is compared with those presented in the various code relations such as ACI 440.1R-15 and CSA S806-12. It was concluded that the outputs are more compatible with the experimental results in comparison with the amounts obtained from the codes equations.

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Section: Introductionmentioning

confidence: 99%

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

Alizadeh

Naderpour

Mirrashid

2020

Engineering Reports

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“…Papers [73][74][75][76][77] have discussed about neural networks for navigational control of mobile robots in highly cluttered environments. Neural networks [78][79][80][81][82] can be efficiently used for solving various engineering problems along with problems related to robots' control. Potential energy attraction has been used by scientists and engineers to model artificial intelligence potential field method for solving various engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation

R¹,

Chidananda

2020

CTCSA

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In this paper application of average fuzzy inference technique has been analysed for navigation control of robotic agent. Also, the reviews of other AI techniques for control of robots are carried out. The robotic agent uses sensors to map the surroundings and take the decision with the help of Fuzzy AI technique to avoid obstacles. In this paper a novel averaging method has been deployed to optimize the results obtained from various fuzzy membership functions. Using the Average Fuzzy Inference technique robot navigates from start position to goal position avoiding obstacles while reaching the target. The simulation results agree with experimental results. The methodology can be used for various applications by the scientific communities to address various engineering problems.

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Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data

Saadatmorad

Jafari-Talookolaei

Pashaei

et al. 2021

Lecture Notes in Civil Engineering

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A novel model is developed to identify damages severities in rectangular laminated composite plates (RLCPs) based on the finite element method (FEM) and adaptive neuro-fuzzy inference system (ANFIS). Empirical data are generated via FEM, and then the ANFIS algorithm is applied to identify damages' severities of single damage scenarios. In this study, the networks' inputs and outputs are the first five natural frequencies and damages' severities. Also, a multilayer perceptron neural network (MLP) is developed to compare results obtained from ANFIS. Results show that the MLP model can identify the severity of every single damage scenario in RLCPs with a high precision (R > 0.9). Also, results obtained indicate that ANFIS can identify and predict the severity of every single damage scenario in RLCPs better than MLP with perfect precision (R = 0.99999).

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Revelence of Multiple Breathing Cracks on Fixed Shaft Using ANFIS and ANN

Cited by 3 publications

References 9 publications

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation

Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data

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