New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming

Soleimani, Sepehr; Rajaei, Shabnam; Jiao, Pengcheng; Sabz, Arash; Soheilinia, Sina

doi:10.1016/j.measurement.2017.08.043

Cited by 85 publications

(49 citation statements)

References 37 publications

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“…Moreover, that the error value for testing is lesser as compared with other training and validation set. This illustrates that the present GEP model can accurately predict the axial capacity of CFST members and can be used for the generalization purpose [72].…”

Section: Resultsmentioning

confidence: 68%

New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach

et al. 2020

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The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses of this member subjected to axial-load only. The distinguishing feature of gene expression programming (GEP) has been utilized for establishing a prediction model for the axial behavior of long CFST. The proposed equation correlates the ultimate axial capacity of long circular CFST with depth, thickness, yield strength of steel, the compressive strength of concrete and the length of the CFST, without need for conducting any expensive and laborious experiments. A comprehensive CFST short circular column under an axial load was obtained from extensive literature to build the proposed models, and subsequently implemented for verification purposes. This model consists of extensive database literature and is comprised of 227 data samples. External validations were carried out using several statistical criteria recommended by researchers. The developed GEP model demonstrated superior performance to the available design methods for AS5100.6, EC4, AISC, BS, DBJ and AIJ design codes. The proposed design equations can be reliably used for pre-design purposes—or may be used as a fast check for deterministic solutions.

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Section: Resultsmentioning

confidence: 68%

New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach

et al. 2020

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“…For example, the ETs shown in Figure 1 which is a chromosome with two genes can be written mathematically as ( ) * a b b a   + +   . Many recent studies indicated that GEP can be used efficiently in civil engineering applications (Mousavi et al, 2012;Soleimani et al, 2018;Lim et al, 2016;González-Taboada et al, 2016;Gholampour et al, 2017;Gandomi et al, 2014;Nazari & Torgal, 2013). Özcan (2012) used GEP to develop a model for splitting tensile strength of concrete.…”

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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“…Several studies that conducted recently have shown that GEP can be used efficiently in civil engineering applications (Mousavi, Aminian, Gandomi, Alavi, & Bolandi, 2012;Soleimani, Rajaei, Jiao, Sabz, & Soheilinia, 2018;Lim, Karakus, & Ozbakkaloglu, 2016; González-Taboada, González-Fonteboa, Martínez-Abella, & Pérez-Ordóñez, 2016; Gholampour, Gandomi, & Ozbakkaloglu, 2017;Gandomi et al, 2014;Nazari & Torgal, 2013). The GEP models are developed using an experimental database and can reasonably predict the results.…”

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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New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming

Cited by 85 publications

References 37 publications

New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach

New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach

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

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