New empirical approaches for compressive strength assessment of CFRP confined rectangular concrete columns

Sharifi, Yasser; Moghbeli, Adel

doi:10.1016/j.compstruct.2020.113373

Cited by 10 publications

(2 citation statements)

References 32 publications

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“…A variety of machine learning methods have been widely applied in performance prediction, data classification, image recognition, structural simulation, etc. In particular, artificial neural networks (ANNs) and support vector regression have attracted the most attention [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…Ahmad used SVR, NMR (nuclear magnetic resonance), and an ANN to predict the splicing strength of reinforced concrete members based on the diameter of the rebar, the compressive strength of concrete, and the protective cover thickness. The results show that SVR has the highest forecasting accuracy [ 31 ]. Tran used SVR to predict the adhesion strength of the interface between FRP and concrete using the water–cement ratio, the recycled aggregate replacement rate, the sand rate, and other parameters.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

et al. 2022

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The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (ffrp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

et al. 2022

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Stepwise Regression for LTB Strength Prediction of I-Beams Subjected to Different Loading Conditions

Rossi,

Hosseinpour,

de Carvalho

et al. 2024

Iran J Sci Technol Trans Civ Eng

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Shear capacity assessment of steel fiber reinforced concrete beams using artificial neural network

Sharifi

Moghbeli

2021

Innov. Infrastruct. Solut.

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Incorporating steel fibers to the concrete members enhances the shear capacity. The shear capacity of steel fiber reinforced concrete (SFRC) beams is an important issue for designing the reinforced concrete structures. Due to numerous parameters that affect the shear capacity of SFRC beams, developing an exact equation to measure the shear resistance of SFRC beams is complicated. To present a more exact equation for shear capacity assessment of SFRC beams, compare to existing formulae the artificial neural networks (ANNs) developed. A series of reliable experimental data collected from the literature. A model-based ANN method for presenting an exact empirical formula developed. The accuracy of the developed formula is verified using several criteria, and a comparison study was carried out between the experimental data and the existing equations. It is understood that the obtained formula gives the most exact result among others. A sensitivity analysis based the Garson's algorithm was executed to identify the most efficient variables. Keywords Artificial neural networks • Steel fiber reinforced concrete beams • Shear capacity of the SFRC beams • Garson's algorithm • Model-based ANN method

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New empirical approaches for compressive strength assessment of CFRP confined rectangular concrete columns

Cited by 10 publications

References 32 publications

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

Stepwise Regression for LTB Strength Prediction of I-Beams Subjected to Different Loading Conditions

Shear capacity assessment of steel fiber reinforced concrete beams using artificial neural network

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