Investigation on the behaviour of ternary blended concrete with scba and sf

Teja, K. Varun; Sai, Prakash; Meena, T.

doi:10.1088/1757-899x/263/3/032012

Cited by 21 publications

(5 citation statements)

References 12 publications

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“…The primary constituents of CBCC are aggregates, water, and ordinary Portland cement (OPC) [ 4 , 5 ]. Following aluminum and steel, OPC is the third most energy-demanding substance on the earth, consuming 7% of the total energy of global industry [ 6 , 7 ]. Regrettably, the manufacture of OPC produces large quantities of greenhouse gases, i.e., CO 2 , which substantially add to climate change [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

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Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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“…Commonly, solid origin at ors utilize fly cinderasan in complete swap for Portland bond at values up to 30 percent of the aggregate cementations piece [9]. The utilization of high rates (high volumes) of fly powder has been considered broadly finished the most recent 15 years, and the advantages of this sort of cement have been all around reported [10].…”

Section: Fly Ashmentioning

confidence: 99%

Mechanical Properties of Steel Fiber Reinforced Geopolymer Concrete Incorporated with Fly-Ash and GGBS

Babu¹,

Kumar²

2022

MSF

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As it expands, it is becoming easier to obtain fresh insights into the numerous modifications and advancements that are taking place in the concrete business. Aside from that, as infrastructure continues to expand, the need for specialised concrete continues to increase in the present market. In addition, cement, aggregates, and other additions are the major elements in the production of concrete, with the exception of water. The goal strength for this current experimental attempt was 40 N/mm2, which was achieved. In this experimental effort, steel fibres with a standard length of 25mm, a diameter of 0.5mm, and an aspect ratio of 50 are used to completely replace cement in a concrete mix. It is the crimpled sort of steel fibre that has been utilised in this experiment, and it has been used in equal numbers with flash and GGBS in this study. Steel fibres are added to the binder, which is GGBS, and low calcium-based flash is provided by a local thermal plant, up to a maximum of 2 percent by weight, in order to compensate for the stress created by strain. This kind of construction achieves a balanced concrete mix that can bear stress while still staying robust in compression. It was determined that the insertion of steel fibres resulted in the biggest gain in strength, with a 1.5 percent increase in strength. A considerable increase in compressive, flexural, and split tensile strength, among other attributes, was seen with the addition of steel fibres to the mix. The usage of fly ash and ground-granulated blast-furnace slag (GGBS) aids in the mitigation of air pollution by removing carbon dioxide and carbon monoxide from the environment, which are created during the cement-making process.

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“…After cooling and condensing, it gathers in cotton bags. It is an ultrafine material with spheres with diameters less than one mm [17], [18]. 5% to 10% replacement of cement with silica fume showed good results in both strength and durability aspects when combined with SCBA [19]- [22].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Ternary Blended Concrete Mixes which contains Silica Fume and Bagasse Ash

Sivaprasad,

Venkata Krishnaiah

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Cement industries are producing high amounts of carbon dioxide, this problem leads to supplementary cementitious materials utilization. Utilization of agricultural and industrial waste in concrete reduces pollution and disposal problems. This work presents the performance evaluation of the ternary blended concrete mixes in which Silica fume and Sugarcane bagasse ash are used as mineral admixtures. A total of six mixes is used including the Reference mix, which is denoted by M0, M11, M12, M13, M14, and M15 whereas M0 represents the reference mix. M11, M12, M13, M14, and M15 are prepared by partially replacing the cement with SCBA by 5%, 10%, 15%, 20%, and 25% respectively. All the mixes were tested against mechanical and workability properties along with durability Properties and the results are clear that the mix with 15% SCBA and 5% Silica Fume significantly got better results.

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Investigation on the behaviour of ternary blended concrete with scba and sf

Cited by 21 publications

References 12 publications

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Mechanical Properties of Steel Fiber Reinforced Geopolymer Concrete Incorporated with Fly-Ash and GGBS

Performance Evaluation of Ternary Blended Concrete Mixes which contains Silica Fume and Bagasse Ash

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