Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete

Aslam, Fahid; Farooq, Furqan; Amin, Muhammad; Khan, Kaffayatullah; Waheed, Abdul; Akbar, Arslan; Javed, Muhammad Faisal; Alyousef, Rayed; Alabdulijabbar, Hisham

doi:10.1155/2020/8850535

Cited by 140 publications

(59 citation statements)

References 54 publications

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“…For external validation and testing of the proposed GEP model, various statistical error tests were also employed. The literature discloses a suggested criterion that the slope (inclination) of any of the regression lines (

) traversing the origin should be approximately equal to 1 [ 106 ]. The slope of regression lines is 1.001 and 0.995 as shown in Table 6 .…”

Section: Resultsmentioning

confidence: 99%

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

et al. 2021

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For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

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) traversing the origin should be approximately equal to 1 [ 106 ]. The slope of regression lines is 1.001 and 0.995 as shown in Table 6 .…”

Section: Resultsmentioning

confidence: 99%

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

et al. 2021

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“…This type of tree is known as the GEP expression tree (ETs). Selection of individual chromosomes takes place and then they are copied into the next generation, as per the fitness by roulette wheel sampling with elitism [ 23 ]. This ensures the durability and replication of the best individual to the next generation.…”

Section: Methodsmentioning

confidence: 99%

“…The model’s overall efficiency by cross-validation is then tested by taking an average of 10 rounds by various errors. Similarly, the model evaluation is also done by using statistical indicators [ 23 ]. Three types of the indicator are used in our current study, which is listed below (Equations (1)–(3)).…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

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“…The predicted results of all three models are also verified through the statistical checks suggested in the literature. The inclination (slope) of the regression line, that is, m ′ or m (crossing through an origin) must be near to 1 (Aslam et al, 2020). The authors also endorsed that the squared coefficient of correlation (crossing an origin) between the experimental outputs and predictive model results, that is, R 2 o or between the model predictive results and experimental outputs, that is, R 2 o must be near to 1 .…”

Section: Statistics and External Verification Of Artificial Neural Network Artificial Neuro-fuzzy Interface And Gene Expression Programmimentioning

confidence: 94%

Geopolymer Concrete Compressive Strength via Artificial Neural Network, Adaptive Neuro Fuzzy Interface System, and Gene Expression Programming With K-Fold Cross Validation

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The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and accurate model to estimate the compressive strength (f′c) of fly ash–based geopolymer concrete (FGPC). A database of 298 instances is developed from the peer-reviewed published work. The database consists of the ten most prominent explanatory variables and f′c of FGPC as a response parameter. The statistical error checks and criteria suggested in the literature are considered for the verification of the predictive strength of the models. The statistical measures considered in this study are MAE, RSE, RMSE, RRMSE, R, and performance index (ρ). These checks verify that the ANFIS predictive model gives an outstanding performance followed by GEP and ANN predictive models. In the validation stage, the coefficient of correlation (R) for ANFIS, GEP, and ANN model is 0.9783, 0.9643, and 0.9314, respectively. All three models also fulfill the external verification criterion suggested in the literature. Generally, the GEP predictive model is ideal as it delivers a simplistic and easy mathematical equation for future use. The k-fold cross-validation (CV) of the GEP model is also conducted, which verifies the robustness of the GEP predictive model. Furthermore, the parametric study is carried via proposed GEP expression. This confirms that the GEP model accurately covers the influence of all the explanatory variables used for the prediction of f′c of FGPC. Thus, the proposed GEP equation can be used in the preliminary design of FGPC.

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Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete

Cited by 140 publications

References 54 publications

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Geopolymer Concrete Compressive Strength via Artificial Neural Network, Adaptive Neuro Fuzzy Interface System, and Gene Expression Programming With K-Fold Cross Validation

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