Compressive Strength of Fly‐Ash‐Based Geopolymer Concrete by Gene Expression Programming and Random Forest

Khan, Mohsin Ali; Memon, Shazim Ali; Farooq, Furqan; Javed, Muhammad Faisal; Aslam, Fahid; Alyousef, Rayed

doi:10.1155/2021/6618407

Cited by 125 publications

(61 citation statements)

References 95 publications

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“…According to Carrillo et al [ 67 ], a strong correlation between the experimental data and predicted results is achieved when the R value is between 0.8 and 1, while a moderate correlation is obtained if the R value ranges between 0.5 and 0.8. This was also in line with another study reported by Khan et al [ 68 ]. Moreover, close relationships between the experimental and estimated data have been proven with correlation values of 0.8–0.9 in many previous studies.…”

Section: Resultssupporting

confidence: 93%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5–20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM’s strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (p values < 0.0001 and high F-values) as well as several statistical validation methods (R > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted R2 and adj. R2 was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.

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Section: Resultssupporting

confidence: 93%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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“…However, the decision tree with bagging gives precise performance than an individual one, as illustrated in Figure 6d. This is due to an increase in model efficiency as it takes several data to train the best model by using weak base learners [47]. The ensemble model is optimized by making 20 sub-models, as depicted in Figure 6c.…”

Section: Decision Tree/ensemble Modelmentioning

confidence: 99%

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

et al. 2021

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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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“…Meanwhile, the experimental research on the compressive-shear mechanical properties of rubber fiber concrete has not been reported in the relevant literature, and therefore, a huge amount of test data is needed in the future to support the research in this aspect. e follow-up research shall carry out further experimental research on the shear mechanical properties of rubber fiber concrete by considering more influencing factors such as rubber particle content, fiber content, and axial compression ratio, so as to provide a direct basis for predicting the shear stress of rubber fiber concrete by applying machine learning [33].…”

Section: Failure Criterion Of Octahedral Stress Spacementioning

confidence: 99%

Experimental Study and Failure Mechanism Analysis of Rubber Fiber Concrete under the Compression‐Shear Combined Action

Zhang

Sun

et al. 2021

Advances in Materials Science and Engineering

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In order to examine the compression-shear combined mechanical properties of rubber fiber concrete, an experimental study was carried out on rubber fiber concrete of three different configurations using a material compression-shear testing machine by considering different axial compression ratios. The failure modes and shear stress-strain curves of rubber fiber concrete under different loading conditions were obtained. By comparatively analyzing the mechanical parameters of rubber fiber concrete under different axial compression ratios, the following conclusions were drawn. With the increase of the axial compression ratio, the failure mode in the shear direction gradually developed from a relatively straight crack to a main crack accompanied by a certain amount of axial cracks; meanwhile, the number of concrete slags on the shear failure section was gradually increased and the friction marks were gradually deepened. The addition of rubber particles increased the randomness and discreteness of the concrete upon failure, while fibers inhibited the development of oblique micro-cracks and the dropping of concrete slags. The shear stress of the concrete specimen containing rubber particles was significantly lower than those without rubber particles. Comparatively, fibers showed little effect on the shear stress. As the axial compression ratio increased, the shear stress and shear strain of rubber fiber concrete were gradually increased, but the increasing amplitude of shear stress tended to become flattened. Under the influence of the axial compression ratio, the shear stress of C-0%-0%, C-30%-0%, and C-30%-0.6% was increased by 4.57 times, 3.26 times, and 2.69 times, respectively, suggesting a gradually decreasing trend. At the same time, based on the principal stress space and the octahedral stress space, the compression-shear combined failure criterion was proposed for the three different rubber fiber concretes. The research findings are of great significance to the engineering application and development of rubber fiber concrete.

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Compressive Strength of Fly‐Ash‐Based Geopolymer Concrete by Gene Expression Programming and Random Forest

Cited by 125 publications

References 95 publications

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

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

Experimental Study and Failure Mechanism Analysis of Rubber Fiber Concrete under the Compression‐Shear Combined Action

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