Statistical analysis of sustainable geopolymer concrete

Kishore, Y.S.N.; Nadimpalli, Sai Geeta Devi; Potnuru, Ashish Kumar; Vemuri, Jayaprakash; Khan, M.A.

doi:10.1016/j.matpr.2021.08.129

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…The alkali activator used in this study was composed of sodium hydroxide and sodium silicate mixed in specific molar ratios. A previous study [31] showed that the highest compressive strength for this specific source of mine tailing AAMs was produced with a sodium hydroxide concentration of 10 M. Because the compressive strength of alkali-activated materials generally increases with increasing sodium hydroxide concentrations [32], activator concentrations of 14 M and 18 M were also explored. However, these mix designs yielded samples with large visible cracks after oven curing and excessive efflorescence (i.e., crystal growth on the surface) during water immersion, so they no longer were considered.…”

Section: Alkali Activatormentioning

confidence: 99%

Improvements in Hydrolytic Stability of Alkali-Activated Mine Tailings via Addition of Sodium Silicate Activator

Clements,

Tunstall,

Bolanos Sosa

et al. 2024

Polymers

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Over 14 billion tons of mine tailings are produced throughout the world each year, and this type of waste is generally stored onsite indefinitely. Alkali activation is a promising strategy for the reuse of mine tailings to produce construction materials, converting this waste stream into a value-added product. One major problem with alkali-activated mine tailings is their low durability in water (i.e., low hydrolytic stability). In this article, the influence of a mixed sodium hydroxide/sodium silicate alkali activator on the compressive strength, hydrolytic stability, and microstructure of alkali-activated materials (AAMs) were systematically investigated. XRD, FTIR, NMR, and NAD were used to investigate microstructural changes, and a water immersion test was used to show improvements in hydrolytic stability. For gold mine tailings activated with pure sodium hydroxide, the compressive strength was 15 MPa and a seven-day water immersion test caused a strength loss of 70%. With an addition of 1 M sodium silicate in the activator, the AAMs achieved a compressive strength of over 30 MPa and strength loss of only 45%. This paper proposes a mechanism explaining why the strength and hydrolytic stability of AAMs are dependent on the dosage of soluble silicate. A high dosage of sodium silicate inhibits the depolymerization of the source material, which results in a sample with less amorphous aluminosilicate gel and, therefore, lower hydrolytic stability.

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Section: Alkali Activatormentioning

confidence: 99%

Improvements in Hydrolytic Stability of Alkali-Activated Mine Tailings via Addition of Sodium Silicate Activator

Clements,

Tunstall,

Bolanos Sosa

et al. 2024

Polymers

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“…These models can take more factors into account to predict the numerical range of the compressive strength more fully. However, it needs a lot of experimental data to support it [5,18,25].…”

Section: Detailed Approach Featuresmentioning

confidence: 99%

“…Compared with traditional cement concrete, its material and preparation process have changed. The main components of geopolymer are silicate, aluminate and other alkaline active substances, and polymer formed by the reaction of silicic acid and aluminic acid [5,6]. In addition, compared with traditional cement, industrial by-products that are now treated as waste can be used as feedstocks for geopolymers.…”

Section: Introductionmentioning

confidence: 99%

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Wang,

Zhang,

et al. 2024

Buildings

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The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

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“…In response to the above problems, engineers creatively proposed to use industrial waste slag and other materials with certain activities as cementing materials to partially or completely replace the cement in concrete, so as to alleviate the environmental pollution and resource consumption caused by cement production and promote the sustainable development of the construction industry [14][15][16][17][18]. Gel is an inorganic aluminosilicate compound formed from alkali-activated industrial waste, metakaolin, and other materials containing a large amount of Si and Al, which does not exist in the high-temperature calcination link in the production process, so it will not release harmful components into the air, nor will it produce solid waste that pollutes the environment due to coal combustion [19][20][21][22][23]. Table 1 and Figure 1 show the classification and properties of gels, respectively.…”

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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Statistical analysis of sustainable geopolymer concrete

Cited by 9 publications

References 29 publications

Improvements in Hydrolytic Stability of Alkali-Activated Mine Tailings via Addition of Sodium Silicate Activator

Improvements in Hydrolytic Stability of Alkali-Activated Mine Tailings via Addition of Sodium Silicate Activator

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

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

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