Prediction of pressuremeter modulus (EM) using GMDH neural network: a case study of Kenny Hill Formation

Zaki, Mohd; Ismail, Mohd Nazri; Govindasamy, Darvintharen; Leong, Frankie Cheah Peng

doi:10.1007/s12517-020-05336-4

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…32 The results of researches reported that soft computing methods in predicting are accurate and reliable. [23][24][25][26][27][29][30][31][32][33] Thus, according to the importance of the bond strength of the GFRP bars, this research aimed to predict it based on the neuro-fuzzy inference system, artificial neural network and GMDH which operate based on the results of the experimental data collected from various descripts and is expressed based on the terms of bar condition, concrete, and confinement from transverse reinforcements.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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