Experimental assessment on durability properties of fly ash aggregate concrete with SVM modelling

Concrete is one of the most synthesised material in the construction sector in which it has aggregate as one of its components. The use of natural aggregates in concrete preparation uses a significant amount of non renewable resources and energy, having a significant environmental impact. Numerous r...

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Hauptverfasser: Reddy, C. Ritvik, Lalitha, G.
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:Concrete is one of the most synthesised material in the construction sector in which it has aggregate as one of its components. The use of natural aggregates in concrete preparation uses a significant amount of non renewable resources and energy, having a significant environmental impact. Numerous research has been carried out in order to safeguard natural reserves, seeking a solution to the waste disposal issue, and reduce construction costs by utilizing waste materials. FA(Fly Ash) aggregate is one such material that can be a substitute for natural aggregate. The durability parameters of concrete with Fly Ash (FA) aggregate are investigated in this study as a substitute for natural fine aggregates. In this study, five concrete mixes were prepared utilizing FA aggregate in percentage substitution of 0%, 10%, 20%, 30%, and 40% for each. The quantity of cement, compaction, curing rate, concrete cover, and porosity all influence the durability of the concrete. Concrete properties such as compressive strength, resistance to abrasion and half cell potentials are investigated. Durability parameters of the specimens were tested after 90-day curing. The results revealed that concrete with 30% FA aggregate had the highest compressive strength, improved resistance towards abrasion and least half cell potential values. Experimentation data were used to develop comprehensive prediction models by applying support vector machine (SVM) algorithm. The SVM model analyses R2 values with an accuracy of over 97%. As a result, we can use SVM to efficiently execute prediction modelling in construction area.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0161077