Prediction of compressive strength of geopolymer composites using an artificial neural network

Yadollahi, Mehrzad Mohabbi; Benli, Ahmet; Demirboğa, Ramazan

doi:10.1179/1433075x15y.0000000020

Cited by 40 publications

(11 citation statements)

References 7 publications

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“…Since then, scientists have paid close attention to geopolymers due to their unique combination of superior mechanical performance, chemical and fire resistance, low CO 2 emissions, and low energy consumption [ 31 , 32 ]. These features are intimately connected to the chemical interactions between aluminosilicate and alkali-polysialate [ 33 ]. The use of geopolymer concrete (GeoPC) in place of conventional cement concrete results in an embodied carbon reduction of up to 80%, depending on the precursor and activator utilized [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

et al. 2022

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The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na2SiO3/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R2). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.

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Section: Introductionmentioning

confidence: 99%

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

et al. 2022

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“…Dao et al (2019) [12] also showed that their models have strong potential for predicting the 28-day compressive strength and suggested that the modelling would help in reducing the time and cost of laboratory tests in the optimal design of geopolymer. Yadollahi et al (2015) [13] estimated the compressive strength of geopolymer concrete using artificial neural networks (ANN) method and found that parameters including silica modulus, Na₂O content, water/solid ratios, and curing time can affect the compressive and tensile strengths of geopolymer concrete. Naseri et al (2017) [14] estimated the results of compressive strength of concrete using SVM (Support Vector Machine).…”

Section: Introductionmentioning

confidence: 99%

An Empirical Model for Geopolymer Reactions Involving Fly Ash and GGBS

Beulah

Sudhir

Chen

et al. 2022

Advances in Materials Science and Engineering

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Numerous works are reported in literature regarding the enhancement of compressive strength of fly ash-GGBS geopolymer combinations with addition of alkali activators of varying concentrations. However, a limited study has been chronicled, revealing the specific role of alkali or alkaline earth contributed by the fly ash-GGBS combinations on the compressive strength development. It is well known that the strength of a geopolymer is dependent on gel formation from Al/Si ratio, Ca/Si ratio, and Ca/(Si + Al) ratio but their exact role when cured for various extended periods is unknown as yet. In the present study, alkali concentration in a fly ash-GGBS geopolymer combination was varied from 6 M to 12 M with increments of two mol in six different fly ash-GGBS combinations with a minimum of 20 percent and a maximum of 70 percent GGBS. The correlation coefficients between compressive strength and Al/Si, Ca/Si, and Ca/(Si + Al) ratios exhibited values higher than 0.95 taken individually. Multiple linear regression analysis with compressive strength (as dependent parameter) and individual values of Al/Si, Ca/Si, and Ca/(Si + Al) ratios (as independent parameters) was effectuated. It was observed that, depending on the composition, the compressive strength circumstantiated a changeover from Ca/Si to Ca/(Si + Al) ratio in the intermediate composition range. Such a detailed analysis is considered supportive of developing a suitable composition which will provide the optimum compressive strength of the combination.

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“…The ability to draw relevant inferences makes ANN a very effective prediction method. Many researchers have used ANN to predict the compressive strength of different types of geopolymer concrete with significant success [ 32 , 38 , 39 , 40 ]. However, the use of ANN for GPC and the influence of bottom ash (BA) as a replacement for cement and sand in fly ash-based geopolymer concrete has rarely been reported.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

et al. 2021

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Geopolymer concrete (GPC) offers a potential solution for sustainable construction by utilizing waste materials. However, the production and testing procedures for GPC are quite cumbersome and expensive, which can slow down the development of mix design and the implementation of GPC. The basic characteristics of GPC depend on numerous factors such as type of precursor material, type of alkali activators and their concentration, and liquid to solid (precursor material) ratio. To optimize time and cost, Artificial Neural Network (ANN) can be a lucrative technique for exploring and predicting GPC characteristics. In this study, the compressive strength of fly-ash based GPC with bottom ash as a replacement of fine aggregates, as well as fly ash, is predicted using a machine learning-based ANN model. The data inputs are taken from the literature as well as in-house lab scale testing of GPC. The specifications of GPC specimens act as input features of the ANN model to predict compressive strength as the output, while minimizing error. Fourteen ANN models are designed which differ in backpropagation training algorithm, number of hidden layers, and neurons in each layer. The performance analysis and comparison of these models in terms of mean squared error (MSE) and coefficient of correlation (R) resulted in a Bayesian regularized ANN (BRANN) model for effective prediction of compressive strength of fly-ash and bottom-ash based geopolymer concrete.

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Prediction of compressive strength of geopolymer composites using an artificial neural network

Cited by 40 publications

References 7 publications

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

An Empirical Model for Geopolymer Reactions Involving Fly Ash and GGBS

Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

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