Flexural strength prediction for concrete beams reinforced with FRP bars using gene expression programming

Murad, Yasmin; Tarawneh, Ahmad; Arar, Fares; Al-Zu'bi, Amr; Al-Ghwairi, Abdullah; Aljaafreh, Ahmad J.; Tarawneh, Mohammad

doi:10.1016/j.istruc.2021.06.045

Cited by 49 publications

(35 citation statements)

References 39 publications

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“…The equations given as Equation (11) through (15) were employed to calculate the flexural capacity of FRP-reinforced concrete beams. The prediction made by Murad et al [ 26 ] using GEP are reported in Figure 9 for comparison. The statistical evaluation of Figure 9 demonstrates that the GEP model had a higher correlation compared to ACI model of 0.977 and 0.974, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…American Concrete Institute (ACI) 440.1 R-15 recommends basic formulations for the design of beams on the basis of mechanics [ 25 ]. However, Murad et al [ 26 ] found that the flexural strength achieved from FRP reinforced concrete beams has some deviations from experimental test results. Gene expression programming (GEP), a modern Artificial intelligence (AI) technique based on genetic algorithms, can accurately predict the flexural strength of FRPs.…”

Section: Introductionmentioning

confidence: 99%

“…In conclusion, empirical equations are available for the calculation of the flexural capacity of FRP-reinforced beams. Murad et al [ 26 ] investigated the capability of the GEP model and inferred that the developed model is capable of estimating the flexural capacity more accurately. After critically evaluating the results of the GEP model ( Section 3.4 ), it was concluded that Murad et al [ 26 ] based their evaluation on correlation coefficient (R) only; however, error analysis showed that the empirical model was better compared to the developed GEP model.…”

Section: Introductionmentioning

confidence: 99%

“…Murad et al [ 26 ] investigated the capability of the GEP model and inferred that the developed model is capable of estimating the flexural capacity more accurately. After critically evaluating the results of the GEP model ( Section 3.4 ), it was concluded that Murad et al [ 26 ] based their evaluation on correlation coefficient (R) only; however, error analysis showed that the empirical model was better compared to the developed GEP model. Moreover, the authors opine that the superiority of the AI technique is not specific, however, strongly depends on the nature (non-linearity) of the problem.…”

Section: Introductionmentioning

confidence: 99%

“…One AI technique may perform better for a particular problem, whereas; some other model may surpass it in accuracy for a different situation. In continuation of previous research by Murad et al [ 26 ] for developing a GEP tree-based model for flexural strength of an FRP-reinforced concrete beam, it is desirable to evaluate the performance of other models such as DT and GBT in solving a similar problem. Therefore, this research concentrates on developing novel DT and GBT models for estimating the flexural capacity of FRP reinforced concrete beams and comparing the capabilities of empirical relations and AI solution in terms of correlations and error analysis.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams

et al. 2022

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Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams

et al. 2022

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Machine Learning Models in Prediction of Strength Parameters of FRP‐Wrapped RC Beams

Kumar¹,

Arora²,

Kapoor³

et al. 2023

Machine Intelligence, Big Data Analytics, and IoT in Image Processing

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Neural network‐based models versus empirical models for the prediction of axial load‐carrying capacities of FRP‐reinforced circular concrete columns

Ali,

Ahmad,

Iqbal

et al. 2023

Structural Concrete

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This study presents new neural‐network (NN)‐based models to predict the axial load‐carrying capacities of fiber‐reinforced polymer (FRP) bar reinforced‐concrete (RC) circular columns. A database of FRP‐reinforced concrete (RC) circular columns having outside diameter and height ranged between 160–305 and 640–2500 mm, respectively was established from the literature. The axial load‐carrying capacities of FRP‐RC columns were first predicted using the empirical models developed in the literature and then predicted using deep neural‐network (DNN) and convolutional neural‐network (CNN)‐based models. The developed DNN and CNN models were calibrated using various neurons integrated in the hidden layers for the accurate predictions. Based on the results, the proposed DNN and CNN models accurately predicted the axial load‐carrying capacities of FRP‐RC circular columns with R2 = 0.943 and R2 = 0.936, respectively. Further, a comparative analysis showed that the proposed DNN and CNN models are more accurate than the empirical models with 52% and 42% reduction in mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively involved in the empirical models. Moreover, within NN‐based prediction models, the prediction accuracy of DNN model is comparatively higher than the CNN model due to the integration of neurons in each layer (9‐64‐64‐64‐64‐1) and embedded rectified linear unit (ReLu) activation function. Overall, the proposed DNN and CNN models can be utilized as paramount in the future studies.

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Flexural strength prediction for concrete beams reinforced with FRP bars using gene expression programming

Cited by 49 publications

References 39 publications

Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams

Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams

Machine Learning Models in Prediction of Strength Parameters of FRP‐Wrapped RC Beams

Neural network‐based models versus empirical models for the prediction of axial load‐carrying capacities of FRP‐reinforced circular concrete columns

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