WITHDRAWN: Salvaged malleable concrete and its perfunctory, micro-structural and robustness competences – A review

Ramana, P.V.; Surendranath, Arigela; Meena, Ayush

doi:10.1016/j.matpr.2021.12.056

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…The process of polymerization is the chemical reaction in this case. Unlike regular Portland pozzolanic cement, Mosel Rubber Concrete does not form calcium silicate hydrates for matrix formation and strength but utilizes silica and alumina polycondensation reactions [26][27][28][29][30][31][32]. Morsel gum paste binds together coarse aggregate, fine aggregate, and other unreacted materials to form morsel gum concrete.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Morsel Rubber Mathematical Models in Seismic Areas

Meena¹,

Sharma²,

Patodiya³

et al. 2022

ASPS Conf. Proc.

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Reinforced concrete is the most consumed material in the world, followed by air and water. As the demand for reinforced concrete as a building material increases, so makes the demand for Portland cement and ordinary cement. On the other hand, environmental protection has become a significant issue due to climate change caused by global warming. This is due to emissions of greenhouse gases such as CO2 from human activities. Of all greenhouse gases, CO2 is responsible for approximately 65% ??of global warming. The cement industry also contributes to releasing CO2 into the atmosphere by producing large amounts of Portland cement and releasing large amounts of CO2. One can need to protect the environment by preventing the uncontrolled disposal of waste. Several efforts have been made to solve this problem. These include using additional cementitious materials such as fly ash, silica fume, granular blast furnace slag, rice husk ash, grass, metakaolin, and alternative binders to Portland cement. Consumption of morsel rubber concrete can reduce the environmental impact in the cement industry. From the perspective of global warming, the technology of self-compacting morsel rubber concrete can significantly reduce the emission of CO2 into the atmosphere. This report presents the analysis and test results of the properties of class 40 self-compacting Morsel rubber concrete using various new approaches.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Morsel Rubber Mathematical Models in Seismic Areas

Meena¹,

Sharma²,

Patodiya³

et al. 2022

ASPS Conf. Proc.

View full text Add to dashboard Cite

show abstract

“…As shown in Section 6, the hybrid system displays a spectrum of learning solutions. At one end of the spectrum lies the naïve Bayes method, while at the other lies the decision tree approach [29][30][31][32][33]. In other parts of the range, the two techniques are combined by learning a decision tree using pruning methods and including a Naïve Bayes model at the leaf nodes, consisting of the remaining attributes that have not been branched upon.…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayesto Machine Learning Approach for Structural Dynamic Complications

Ramana

2022

ASPS Conf. Proc.

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Learning is one of the most powerful concepts in artificial intelligence research. It allows a system to learn from its environment and automatically modify its behavior to suit its needs. On par with human champions, the world’s best computer backgammon player is a computer program that learns by playing against itself. The learning algorithm and available data set limit computer learning. Several techniques are available to perform machine learning. Decision trees, the naïve Bayes approach, and the more general Bayes net approach are a few choices. The naïve Bayes approach is an instance of the more general Bayes nets. This paper examines and analyzes the naïve Bayes and decision tree approaches to learning. Various techniques to avoid over-fitting, such as ensemble construction and cross-validation, are also implemented and analyzed. A novel hybrid between the naïve Bayes approach and the decision tree method is presented. The hybrid system produces a spectrum of options that could be used for learning by merely changing parameter values. At one end lies the naïve Bayes approach, while at the other lies the decision tree technique. The proposed hybrid scheme solves the problem of poor naïve Bayes performance in a domain with dependent attributes and the memory consumption problem of the decision tree. One can analyze this idea and show encouraging experimental data that backs the need for such a solution.

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WITHDRAWN: Salvaged malleable concrete and its perfunctory, micro-structural and robustness competences – A review

Cited by 2 publications

References 27 publications

Evaluation of Morsel Rubber Mathematical Models in Seismic Areas

Evaluation of Morsel Rubber Mathematical Models in Seismic Areas

Naïve Bayesto Machine Learning Approach for Structural Dynamic Complications

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