Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites

Yaswanth, K. K.; Revathy, J.; Gajalakshmi, P.

doi:10.1007/s12633-021-00988-7

Cited by 18 publications

(6 citation statements)

References 52 publications

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“…MAE is used to measure the absolute error between the measured value and the predicted value, MAPE is used to measure the percentage of the model's predicted error, and RMSE is used to measure the deviation between the observed value and the true value. The expressions of the four statistical parameters are as follows [30,31].…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

et al. 2023

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To obtain the magnesium oxychloride cement concrete (MOCC) ratio with excellent water resistance quickly and accurately, a BP neural network (BPNN) model with a topology structure of 4-10-2 was designed, and the PSO (particle swarm optimization), GWO (gray wolf optimization), and WOA (whale optimization algorithm) algorithms were used to optimize the model. The input layer parameters of the model above were n(MgO/MgCl2), Grade I fly ash, phosphoric acid (PA), and phosphate fertilizer (PF) content, and the output layer was the MOCC’s compressive strength and softening coefficient. The model had a dataset of 144 groups, including 100 training set data, 22 verification set data, and 22 test set data. The results showed that the PSO-BPNN model had the highest predictive accuracy among the four models, with a mean R2 of 0.99, mean absolute error(MAE) of 0.52, mean absolute percentage error(MAPE) of 0.01, and root mean square error (RMSE) of 0.73 in predicting compressive strength, and a mean R2 of 0.99, MAE of 0.44, MAPE of 0.01, and RMSE of 0.62 in predicting the softening coefficient. The results showed that using the PSO-BPNN to predict the compressive strength and softening coefficient of MOCC is feasible and can provide theoretical guidance for designing the MOCC mix.

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Section: Evaluation Indicatorsmentioning

confidence: 99%

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

et al. 2023

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“…Based on the trial-and-error method, several functions of neural networks were tried and it was found that LM-based ANN worked best in predicting the mix design. As the LM-ANN worked well in predicting the outputs when the number of inputs was comparatively smaller [ 56 ]. To obtain the best ANN models in predicting the mix-design, variations in the number of hidden layers and the number of hidden neurons were considered and trained accordingly.…”

Section: Development Of Ann Modelsmentioning

confidence: 99%

Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites

et al. 2022

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A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16: 25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials.

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“…Khoa et al [ 26 ] proposed to evaluate the strength of fly ash-based geopolymer concrete using the deep neural network (DNN) and res net architecture to solve the shortcomings of the long time and high cost of traditional laboratory experiment methods. Yaswanth et al [ 27 ] developed a neural network method for predicting the CS of geopolymer concrete and verified the accuracy of the developed model by comparing the consistency between the predicted and actual values. Awoyera et al [ 28 ] used gene expression programming (GEP) and an artificial neural network (ANN) to predict the CS, splitting, and flexural strength of gels self-compacting concrete with mineral admixtures, and the results show that GEP and ANN had high prediction effects.…”

Section: Introductionmentioning

confidence: 99%

A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions

Chen

Zhou

Shi

et al. 2023

Gels

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Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.

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Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites

Cited by 18 publications

References 52 publications

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites

A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions

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