Experimental investigation on concrete and geopolymer concrete

Jeevanandan, K.; Sreevidya, V.

doi:10.1016/j.matpr.2019.05.448

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…It is a usual procedure to first mix the binder materials, fine aggregate and coarse aggregate using mixer machine [10] for about three to four minutes. Then the prepared alkaline liquid should be poured over the dry mix concrete and mixing to be continued for about four to five minutes to initiate geopolymerisation process.…”

Section: Concrete MIX Proportions and Specimen Preparationmentioning

confidence: 99%

Geopolymer Concrete under Ambient Curing

Anbarasan¹,

Sanjaiyan²,

Soundarapandiyan³

2021

Advances in Geopolymer-Zeolite Composites - Synthesis and Characterization

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Geopolymer concrete (GPC) has significant potential as a more sustainable alternative for ordinary Portland cement concrete. GPC had been introduced to reduce carbon footprints and thereby safeguarding environment. This emerging eco friendly construction product finds majority of its application in precast and prefabricated structures due to the special curing conditions required. Sustained research efforts are being taken to make the product suitable for in situ applications. The developed technology will certainly address the issues of huge energy consumption as well reduce water use which is becoming scarce nowadays. Ground Granulated Blast Furnace Slag (GGBS) a by-product of iron industries in combination with fly ash has proved to give enhanced strength, durability as well reduced setting time. This study investigates the effect of GGBS as partial replacement of fly ash in the manufacture of GPC. Cube and cylindrical specimens were cast and subjected to ambient curing as well to alternate wetting-drying cycles. The 28 day compressive strength, split tensile strength, flexural strength and density of GPC specimens were found. The study revealed increase in compressive strength, split tensile strength, density as well flexural strength up to 40 percent replacement of fly ash by GGBS.

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Section: Concrete MIX Proportions and Specimen Preparationmentioning

confidence: 99%

Geopolymer Concrete under Ambient Curing

Anbarasan¹,

Sanjaiyan²,

Soundarapandiyan³

2021

Advances in Geopolymer-Zeolite Composites - Synthesis and Characterization

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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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“…Concrete is a common building material; it has been widely used in industrial and civil buildings and has become one of the world's most widely used building materials because of its low price, excellent performance, simple production process, and other characteristics [1][2][3][4][5][6]. Over time, more and more infrastructure industries have given priority to concrete as a building material [7,8].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

Zhu

Zhou

et al. 2022

Materials

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Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the compressive strength of concrete, few researchers have compared the prediction accuracy of different machine learning models on the compressive strength of concrete. Firstly, the hyperparameters of BP neural network (BPNN), support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor algorithm (KNN), logistic regression (LR), and multiple linear regression (MLR) are tuned by the beetle antennae search algorithm (BAS). Then, the prediction effects of the above seven machine learning models on the compressive strength of concrete are evaluated and compared. The comparison results show that KNN has higher R values and lower RSME values both in the training set and test set; that is, KNN is the best model for predicting the compressive strength of concrete among the seven machine learning models mentioned above.

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Experimental investigation on concrete and geopolymer concrete

Cited by 7 publications

References 9 publications

Geopolymer Concrete under Ambient Curing

Geopolymer Concrete under Ambient Curing

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

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

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