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Predicting the Compressive Strength of Concrete Containing Fly Ash Cenosphere Using ANN Approach
The rising solid waste produced by industries is a major problem in different countries. Fly ash cenosphere (FAC) is a by-product of thermal power plants, which can partially replace fine aggregate in concrete. A reliable model for predicting concrete strength can save time, effort, and money. The present investigation implemented an artificial neural network (ANN) to predict the compressive strength of concrete that incorporates fine aggregate replacement with FAC. This research endeavors to predict the compressive strength of concrete that incorporates FAC at different levels of replacement, specifically at 7, 14, and 28 days. The utilization of the ANN methodology is employed to analyze and model three distinct variables, namely the fine aggregate content, the percentage of FAC ranging from 0 to 100%, and the water-to-cement (w/c) ratio. As an outcome, it is observed that FAC concrete is strongly influenced by cement content, % of FAC, and w/c ratio. Experimental data are highly correlated with ANN models as well. ANNs, however, demonstrate a higher degree of accuracy than other models. The results also signify that the ANN approach for concrete compressive strength prediction is solitary of the utmost best-fit method.
Predicting the Compressive Strength of Concrete Containing Fly Ash Cenosphere Using ANN Approach
The rising solid waste produced by industries is a major problem in different countries. Fly ash cenosphere (FAC) is a by-product of thermal power plants, which can partially replace fine aggregate in concrete. A reliable model for predicting concrete strength can save time, effort, and money. The present investigation implemented an artificial neural network (ANN) to predict the compressive strength of concrete that incorporates fine aggregate replacement with FAC. This research endeavors to predict the compressive strength of concrete that incorporates FAC at different levels of replacement, specifically at 7, 14, and 28 days. The utilization of the ANN methodology is employed to analyze and model three distinct variables, namely the fine aggregate content, the percentage of FAC ranging from 0 to 100%, and the water-to-cement (w/c) ratio. As an outcome, it is observed that FAC concrete is strongly influenced by cement content, % of FAC, and w/c ratio. Experimental data are highly correlated with ANN models as well. ANNs, however, demonstrate a higher degree of accuracy than other models. The results also signify that the ANN approach for concrete compressive strength prediction is solitary of the utmost best-fit method.
Predicting the Compressive Strength of Concrete Containing Fly Ash Cenosphere Using ANN Approach
Lecture Notes in Civil Engineering
Reddy, Krishna R. (editor) / Ravichandran, P. T. (editor) / Ayothiraman, R. (editor) / Joseph, Anil (editor) / Kowsalya, M. (author) / Sindhu Nachiar, S. (author) / Anandh, S. (author)
International Conference on Civil Engineering Innovative Development in Engineering Advances ; 2023 ; Kattankulathur, India
2024-01-31
8 pages
Article/Chapter (Book)
Electronic Resource
English
Fly ash cenosphere , Waste materials , Artificial neural network (ANN) approach , Fine aggregate replacement , Concrete compressive strength , Mechanical properties Engineering , Building Construction and Design , Sustainable Architecture/Green Buildings , Solid Construction , Construction Management
Study on Specific Compressive Strength of Concrete with Fly Ash Cenosphere
| Springer Verlag | 2020
| British Library Online Contents | 2017
| British Library Online Contents | 2017
| British Library Online Contents | 2017