Artificial neural network model for steel–concrete bond prediction

Dahou, Zohra; Sbartaï, Zoubir Mehdi; Castel, Arnaud; Ghomari, Fouad

doi:10.1016/j.engstruct.2009.02.010

Cited by 116 publications

(40 citation statements)

References 14 publications

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“…Neural networks were used to determine the shear strength of circular reinforced concrete columns, the results obtained agreed with the results obtained using various codes [21]. An artificial neural network was proposed for modeling the bond between conventional ribbed steel bars and concrete [22]. Erdem [23] applied artificial neural networks to predict the ultimate moment capacity of reinforced concrete slabs in fires.…”

Section: Literature Surveymentioning

confidence: 91%

Prediction of punching shear strength of two-way slabs

Elshafey

Rizk

Marzouk

et al. 2011

Engineering Structures

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Section: Literature Surveymentioning

confidence: 91%

Prediction of punching shear strength of two-way slabs

Elshafey

Rizk

Marzouk

et al. 2011

Engineering Structures

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“…Over the last two decades, various soft computing techniques such as Artificial Neural Networks (ANNs), Decision Trees, Fuzzy Logic (FL), Adaptive Neuro Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM) have been increasingly implemented to tackle civil engineering problems [12][13][14][15][16][17][18]. Some of these techniques have also been utilized for the prediction of the bond strength and shear capacity of the FRP bars in concrete.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

et al. 2019

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Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.

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“…Fall et al [107] used ANN for prediction of stability and performance of an active aluminum panel structure under uncertainty conditions. Dahou et al [108] used a multi-layer perceptron to model steel-concrete bond and predicted the ultimate pull-out load. Petroutsatou et al [109] used a multilayer feed-forward neural network and a general regression neural network to predict the construction costs of a road tunnel.…”

Section: Prediction Applicationsmentioning

confidence: 99%

Neurocomputing in Civil Infrastructure

et al. 2016

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Abstract. This article presents a review of the recent applications of Arti cial Neural Networks (ANN) for civil infrastructure including structural system identi cation, structural health monitoring, structural vibration control, structural design and optimization, prediction applications, construction engineering, and geotechnical engineering. The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural networks. Deep machine learning techniques are among the newest techniques to nd applications in civil infrastructure systems.

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Artificial neural network model for steel–concrete bond prediction

Cited by 116 publications

References 14 publications

Prediction of punching shear strength of two-way slabs

Prediction of punching shear strength of two-way slabs

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

Neurocomputing in Civil Infrastructure

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