Prediction of columns with GFRP bars through Artificial Neural Network and ABAQUS

Ahmad, Afaq; Aljuhni, Aiman; Arshid, Muhammad Usman; Elchalakani, Mohamed; Abed, Farid

doi:10.1016/j.istruc.2022.03.090

Cited by 7 publications

(16 citation statements)

References 48 publications

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“…The GFRP spirals, presented as 3D solid features, were wound around the bars with a translational pitch of 50 mm. As GFRP has higher resistance against corrosion; therefore, relatively lesser clear cover was used for the GFRP reinforcements, as suggested by [22,23]. Few additional parts incorporated as parts of testing apparatus, such as top and bottom steel plates, top and bottom peripheral collars, and rubber pads, were also simulated as 3D solid features, using standard properties of steel and rubber, as can be observed from Figure 5a,b.…”

Section: Geometric and Materials Propertiesmentioning

confidence: 99%

“…The initial and maximum times of increment greatly affect the viscosity parameter to be selected for the model. To attain the closest possible value, initial calibration was started using smaller values of the viscosity (almost 15% of the step time increment), as suggested by [22,23]. The viscosity values of 0.001, 0.0018, 0.002, 0.03, and 0.005 were examined on the control model.…”

Section: Viscosity Parameter (ν)mentioning

confidence: 99%

“…The axial load-axial deformation curves of all the elements indicate that the most accurate results are provided by the C3D8R element. It was observed from [22,23] that the C3D8R element took lesser running time for the analysis, with comparatively greater degree of accuracy. The numerical specimens, having the mesh types of C3D4H (1648.93 kN), C3D6H (1648.93 kN), C3D10H (974.24 kN), and C3D8R (1580.82 kN), demonstrated the differences of 5.57%, 5.57%, −37.62%, and 1.21%, respectively, in the ultimate axial load-carrying capacities compared with that of the experimental test result as 1561.09 kN.…”

Section: Mesh Element Typementioning

confidence: 99%

“…Jabbar and Farid [4] observed that in addition to higher corrosion resistance, the GFRP bars have 13% higher tensile strength and 58% higher tensile yield strain than steel. Over the last two decades, different investigations have been conducted on the GFRP in axial members having solid cross sections and under different loading conditions [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Tobbi et al [25] investigated the cover spalling in which the lateral confinement is gained from GFRP spiral reinforcement that enhances the strength and ductility of the columns.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Circular Hollow Concrete Columns Reinforced with GFRP Bars and Spirals

et al. 2023

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Glass fiber-reinforced polymer (GFRP) reinforcements are useful alternatives to traditional steel bars in concrete structures, particularly in vertical structural elements such as columns, as they are less prone to corrosion, and impart increasing strength and endurance of buildings. There is limited research on the finite element analysis (FEA) of the structural behavior of hollow glass fiber-reinforced polymer reinforced concrete (GFRPRC) columns. The hollow portion can be used for the service duct and for reducing the self-weight of the members. Numerical analysis of the compressive response of circular hollow concrete columns reinforced with GFRP bars and spirals is performed in this study. This article aims to investigate the axial behavior of hollow GFRP concrete columns and compare it with that of solid steel reinforced concrete (RC) columns as well as hollow steel RC columns. The Abaqus software is used to construct finite element models. After calibration of modeling using an experimental test result as a control model, a parametric study is conducted. The columns with the same geometry, loading, and boundary conditions are analyzed in the parametric study. It is resulted that the hollow GFRP concrete columns provide a greater confinement effect than the solid steel RC columns. The average variation in the ultimate axial load-carrying capacities of the experimental results, from that of the FEA values, was noted to be only 3.87%, while the average difference in the corresponding deformations was 7.08%. Moreover, the hollow GFRP concrete columns possess greater axial load and deformation capacities compared with the solid steel RC columns.

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Section: Geometric and Materials Propertiesmentioning

confidence: 99%

Section: Viscosity Parameter (ν)mentioning

confidence: 99%

Section: Mesh Element Typementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of Circular Hollow Concrete Columns Reinforced with GFRP Bars and Spirals

et al. 2023

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“…They claimed that ANN decreases the number of numerical case studies and the solution time with satisfactory results. Ahmad et al (2022) used FEA and ANN methods to predict the ultimate response of concrete columns with glass fiberreinforced polymers. They also obtained results with a good agreement of numerical results with the experimental results and ANN results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the onset of shear localization based on machine learning

Akar

Ayli

Ulucak

et al. 2023

AIEDAM

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Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors’ knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.

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Prediction of load-bearing capacity of RC columns (CWA) using Artificial Neural Networks (ANN) trained on a hybrid experimental database HEXP

Al-Sayegh,

Mahmoudabadi,

Shabbir

et al. 2024

Journal of Engineering Research

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Prediction of columns with GFRP bars through Artificial Neural Network and ABAQUS

Cited by 7 publications

References 48 publications

Investigation of Circular Hollow Concrete Columns Reinforced with GFRP Bars and Spirals

Investigation of Circular Hollow Concrete Columns Reinforced with GFRP Bars and Spirals

Prediction of the onset of shear localization based on machine learning

Prediction of load-bearing capacity of RC columns (CWA) using Artificial Neural Networks (ANN) trained on a hybrid experimental database HEXP

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