Different mineral admixtures in concrete: a review

Kanamarlapudi, Lakshmisupriya; Jonalagadda, Krishna Bhanu; Jagarapu, Durga Chaitanya Kumar; Eluru, Arunakanthi

doi:10.1007/s42452-020-2533-6

Cited by 45 publications

(15 citation statements)

References 14 publications

(19 reference statements)

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“…The first one—a higher consistency than for the reference paste will be typical for A type, due to a higher w/c ratio, since the mix water is only partially absorbed into the material. The second option—a lower or the same consistency as for the reference paste will be observed for the B, C, and D types, respectively, since consistency usually becomes stiff with active additives, as well as fineness ( Figure 19 ) [ 49 , 50 , 55 , 130 , 131 ]. Water demand and thus change in workability is mainly based on the mineral additive surface area [ 132 ].…”

Section: Resultsmentioning

confidence: 99%

Concept of Evaluation of Mineral Additives’ Effect on Cement Pastes’ Durability and Environmental Suitability

2021

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This experimental study focuses on the assessment of mineral additives and their incorporation into cement composites (CC). The assessment was based on a holistic approach to the performance of the durability properties of CC. Environmental suitability was also taken into consideration. In the experiments, cement pastes with w/c ratios of 0.3, 0.4, and 0.5, respectively, were prepared. Natural zeolite (NZ) and densified silica fume (SF) at doses of 7.5 and 15.0 wt.% of cement were used as the investigated (replacement) materials. Their effects (including development over time) on density, strength (flexural and compressive), porosity by water absorption, permeability by rapid chloride penetration (RCP) test, phase content by thermal analysis, and hydration progression, were observed. The results were then used to propose an evaluation approach. Natural zeolite was used for its known pozzolanic activity and classification as a supplementary cementitious material (SCM). In contrast SF acted as a filler in cement pastes, and thus did not have a direct positive effect on durability. The concept of comprehensive analysis for unknown additive classification is proposed to expressly differentiate between SCM, inert, and improving mineral additive. This concept could be applied to the assessment of mineral additives with regards to the durability and suitability of cement composites.

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Section: Resultsmentioning

confidence: 99%

Concept of Evaluation of Mineral Additives’ Effect on Cement Pastes’ Durability and Environmental Suitability

2021

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“…However, the surface area's lowest and highest values are 170 and 1000 m 2 /kg, correspondingly [36]. The FA-specific gravity ranges between 1.9-2.55 [37]. Moreover, coal bottom ash is dark grey in color and porous [38].…”

Section: Physical Properties and Chemical Composition For Different Mineral Admixture Physical Propertiesmentioning

confidence: 99%

“…Shape Irregular [42], [43] Spherical [37] Spherical [37] Spherical [37], [44] Specific gravity 1.39 -2.47 [40], [41], [45]- [47] 1.9-2.55 [37] 2.25 [37] 2.6 [37] Average particle size 3.65-50.45μm [45]- [47] 0.5-300 μm [37] 0.1μ [37] 4.75 mm down [37], [44] Bulk density (Kg/m 3 ) 2190 [41] 540-860 [37] 750-850 [37] 1000-1100 [37],…”

Section: Ggbs (%)mentioning

confidence: 99%

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Influence of Mineral Admixtures on the Properties of Self-Compacting Concrete: An Overview

2021

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Mineral admixtures are often utilized in Self-Compacting Concrete (SCC) mixtures to provide stability and resistance to bleeding and segregation throughout transportation and placement. Additionally, these more refined materials help in lowering building costs and reducing the use of main resources. SCC is an innovative method of concrete, which is placed and compacted without the use of vibration. As a result, the concrete mixture has the ability to flow under its self-weight to fully fill formwork and achieve total compaction even when reinforced by crowded reinforcement. However, self-compacting concrete is not cost-effective, which results in the use of large amounts of ordinary cement and chemical admixtures. The utilization of mineral admixtures, including silica fume, ground granulated blast furnace slag, fly ash, and coal bottom ash, is an alternative method to decrease the high cost of self-compacting concrete - it is a term, which refers to the components that have been finely divided and added to concrete during the mixing process. Furthermore, the utilization of admixtures in the fabrication of self-compacting concrete has shown that it helps in lowering the heat of hydration. In addition, the inclusion of admixtures reduces the necessity for chemical admixtures that increase viscosity in concrete mixtures. This study aims to provide an overview of the previously conducted studies on mineral admixtures, which are utilized in SCC. Moreover, the study aims to discuss the durability and mechanical performance of SCC.

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“…erefore, it is beneficial to find and utilize active admixtures with highquality and low-energy consumption as alternatives of cement, partly or totally [1,2]. In addition, those active admixtures can even enhance the properties of concrete such as compressive strength, antipermeability, and corrosion resistance [3]. erefore, mix proportion of concrete, especially the high-performance concrete, has been in increasing demand.…”

Section: Introductionmentioning

confidence: 99%

Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete

Cui

Chen

Wang

et al. 2021

Advances in Civil Engineering

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The prediction of concrete strength is an interesting point of investigation and could be realized well, especially for the concrete with the complex system, with the development of machine learning and artificial intelligence. Therefore, an excellent algorithm should put emphasis to receiving increased attention from researchers. This study presents a novel predictive system as follows: extreme gradient boosting (XGBoost) based on grey relation analysis (GRA) for predicting the compressive strength of concrete containing slag and metakaolin. One of its highlights is a feature selection methodology, i.e., GRA, which was used to determine the main input variables. Another highlight is that its performance was compared with the frequently used artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) by using random dataset and the same testing datasets. For three same testing datasets, the average R2 values of ANN, GA-ANN, and XGBoost are 0.674, 0.829, and 0.880, respectively, indicating that XGBoost has the highest absolute fraction of variance (R2). XGBoost can provide best result by testing the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The average RMSE values of ANN, GA-ANN, and XGBoost are 15.569 MPa, 10.530 MPa, and 9.532 MPa, respectively, and those of MAPE of ANN, GA-ANN, and XGBoost are 11.224%, 9.140%, and 8.718%, respectively. Thus, the XGBoost definitely performed better than the ANN and GA-ANN. Finally, a type of application software based on XGBoost was developed for practical applications. This vivid software interfaces could help users in prediction and easy and efficient analysis.

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Different mineral admixtures in concrete: a review

Cited by 45 publications

References 14 publications

Concept of Evaluation of Mineral Additives’ Effect on Cement Pastes’ Durability and Environmental Suitability

Concept of Evaluation of Mineral Additives’ Effect on Cement Pastes’ Durability and Environmental Suitability

Influence of Mineral Admixtures on the Properties of Self-Compacting Concrete: An Overview

Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete

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