Predictive compressive strength models for green concrete

Murad, Yasmin; Imam, Rana; Hajar, Husam A. Abu; Habeh, Dua’a; Hammad, Abdullah; Shawash, Zaid

doi:10.1108/ijsi-05-2019-0044

Cited by 24 publications

(6 citation statements)

References 26 publications

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“…This result is consistent with the Narayanan and Darwish [6,10]. Similarly, recent studies have also discussed similar behavior [34][35][36][37][38][39][40]. The adopted procedure for evaluating each of the variables has been wildly utilized in research [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Effect Of Shear Span To Depth Ratio (A/d)supporting

confidence: 82%

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

Odeh

Alawadi²

2022

Sustainability

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The assessment of shear behavior in SFRC beams is a complex problem that depends on several parameters. This research aims to develop an artificial neural network (ANN) model that has six inputs nodes that represent the fiber volume (Vf), fiber factor (F), shear span to depth ratio (a/d), reinforcement ratio (ρ), effective depth (d), and concrete compressive strength (fc′) to predict shear capacity of steel fiber-reinforced concrete beams, using 241 data test gathered from previous researchers. The proposed ANN model provides a good implementation and superior accuracy for predicting shear strength compared to previous literature, with a Root Mean Square Error (RMSE) of 0.87, the average ratio (vtest/vpredicted) of 1.00, and the coefficient of variation of 22%. It was shown from parametric analysis the reinforcement ratio and shear span to depth ratio contributed the most impact on the shear strength. It can also be noticed that all parameters have a nearly linear impact on the shear strength except the shear span to depth ratio has an exponential effect.

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Section: Effect Of Shear Span To Depth Ratio (A/d)supporting

confidence: 82%

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

Odeh

Alawadi²

2022

Sustainability

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“…Murad [31] also proposed a GEP model to estimate the compressive strength of concrete modified with nanomaterials where code guidelines are lacking in these three cases. Several researchers [10,14,[32][33][34][35][36][37][38][39][40][41] have proved that GEP is a powerful tool for creating accurate models for solving various problems in the civil engineering field.…”

Section: Overview Of Gene Expression Programmingmentioning

confidence: 99%

Shear Strength Prediction of Concrete Beams Reinforced with FRP Bars and Stirrups Using Gene Expression Programming

et al. 2023

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Existing reinforced concrete (RC) structures in humid regions suffer from deterioration due to the corrosion of ordinary reinforcement bars damaging the whole system. The deterioration of the transverse reinforcement leads to shear failure, which is one of the most dangerous failure modes. Therefore, researchers suggested using fiber-reinforced polymer (FRP) bars as a replacement for reinforcement bars in humid regions to integrate sustainability and improve their serviceability and durability. A simple model that can accurately estimate the shear strength of concrete beams designed with FRP longitudinal bars and stirrups is lacking. This research proposed a simplified Gene expression programming (GEP) based model to estimate the shear strength of FRP concrete beams. Seven parameters that principally dominate the shear behavior of FRP beams were utilized to create the GEP model. The parameters are the beam width, beam depth, concrete compressive strength, FRP tensile longitudinal reinforcement ratio, area of stirrups, spacing between the stirrups, and the ultimate FRP strength of stirrups. A comparison was made between the GEP and ACI-440 models; the R2 values of the total database were 92% and 54% for the GEP and ACI models, respectively. The R2 of the GEP model is considerably higher than that measured for the ACI model, and the errors of the GEP model are low, which affirms that the GEP is superior to the ACI model in estimating the shear strength of FRP beams. The trends of the GEP and ACI-440 models and the empirical results are similar, confirming the GEP model’s consistency. Using the GEP model to estimate the shear strength of concrete beams designed with FRP longitudinal bars and stirrups is recommended.

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“…Beheshti et al (2017) proposed a model for estimating shear strength of short rectangular reinforced concrete column using Gene Expression Programing. Murad et al (2019b) proposed predictive models for green concrete using GEP. Gandomi et al (2014) predict the shear strength of slender RC beams using gene expression programing.…”

Section: Overview Of Genetic Programmingmentioning

confidence: 99%

Compressive Strength Prediction of Lightweight Short Columns at Elevated Temperature Using Gene Expression Programing and Artificial Neural Network

Ashteyat

Obaidat

Murad

et al. 2020

Journal of Civil Engineering and Management

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The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.

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Predictive compressive strength models for green concrete

Cited by 24 publications

References 26 publications

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

Shear Strength Prediction of Concrete Beams Reinforced with FRP Bars and Stirrups Using Gene Expression Programming

Compressive Strength Prediction of Lightweight Short Columns at Elevated Temperature Using Gene Expression Programing and Artificial Neural Network

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