Estimating Shear Strength of Short Rectangular Reinforced Concrete Columns Using Nonlinear Regression and Gene Expression Programming

Aval, Seyed Bahram Beheshti; Ketabdari, Hesam; Gharebaghi, Saeed Asil

doi:10.1016/j.istruc.2017.07.002

Cited by 69 publications

(21 citation statements)

References 24 publications

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“…The gene in GEP is composed of a head and a tail. The head composed of both function and terminal symbols (constants, variables, functions, and mathematical operators such as (1, a, b, √, cos ,*,−, /) (Beheshti et al, 2017). The tail contains only terminals (constant and variables) such as (1, a, b, c).…”

Section: Overview Of Genetic Programmingmentioning

confidence: 99%

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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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Section: Overview Of Genetic Programmingmentioning

confidence: 99%

“…Özcan (2012) used GEP to develop a model for splitting tensile strength of concrete. 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.…”

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

View full text Add to dashboard Cite

show abstract

“…The gene in GEP is composed of a head and a tail. The head is composed of both function and terminal symbols (constants, variables, functions, and mathematical operators) such as (1, a, b, √, cos ,*,−, /) (Aval, Ketabdari, & Gharebaghi, 2017). The tail contains only terminals (constant and variables) such as (1, a, b, c).…”

Section: Overview Of Genetic Programmingmentioning

confidence: 99%

“…Nazari and Torgal (2013) poposed a GEP model to predict the compressive strength of geopolymeric binders. Aval et al (2017) proposed a model for estimating shear strength of short rectangular reinforced concrete column using Gene Expression Programming. Ozcan (2012) used GEP to develop a model for splitting tensile strength of concrete.…”

Section: Overview Of Genetic Programmingmentioning

confidence: 99%

Predictive Model to the Bond Strength of FRP-to-Concrete Under Direct Pullout Using Gene Expression Programming

Murad

Ashteyat

Hunaifat

2019

Journal of Civil Engineering and Management

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Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is collected from the literature. The gene expression programming model is developed using eight parameters that predominantly control the bond strength. These parameters are concrete compressive strength, maximum aggregate size, fiber reinforced polymer (FRP) tensile strength, FRP thickness, FRP modulus of elasticity, adhesive tensile strength, FRP length, and FRP width. The model is validated using the experimental results and a statistical assessment is implemented to evaluate the performance of the proposed GEP model. Furthermore, the predicted bond results, obtained using the GEP model, are compared to the results obtained from several analytical models available in the literature and a parametric study is conducted to further ensure the consistency of the model by checking the trends between the input parameters and the predicted bond strength. The proposed model can reasonably predict the bond strength that is most fitting to the experimental database compared to the analytical models and the trends of the GEP model are in agreement with the overall trends of the analytical models and experimental tests.

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“…One of the important tasks in mechanical excavation is to predict the penetration rate (PR) of the miner [3,4]. Knowing the factors influencing the penetration rate is crucial because it can directly affect project's schedule, particularly cutting time, as well as operating costs [5,6]. Penetration rate is the key factor through performance prediction of tunnel boring machines (TBMs) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

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Estimating Shear Strength of Short Rectangular Reinforced Concrete Columns Using Nonlinear Regression and Gene Expression Programming

Cited by 69 publications

References 24 publications

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

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

Predictive Model to the Bond Strength of FRP-to-Concrete Under Direct Pullout Using Gene Expression Programming

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

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