ORIGINAL ARTICLE
Impermeability Evaluation Of Concrete With Fly Ash Aggregate And Prediction With Modelling
 
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Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology , Telangana, India
 
 
Submission date: 2023-08-29
 
 
Final revision date: 2023-10-26
 
 
Acceptance date: 2023-10-31
 
 
Online publication date: 2023-11-16
 
 
Publication date: 2023-11-16
 
 
Corresponding author
Gurikini Lalitha   

Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, , Telangana, India, 500090, Hyderabad, India
 
 
Civil and Environmental Engineering Reports 2023;33(2):145-157
 
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ABSTRACT
Concrete is the most synthesized material in construction sector which it has aggregate as one of its components. The use of natural aggregate in concrete preparation uses a significant amount of non-renewable resources and energy, having a significant environmental impact. Multiple research projects have been conducted 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 replacement for natural aggregate. Durability parameters of concrete with Fly Ash (FA) aggregate are studied in this work as an alternative for fine aggregate. In this study, 5 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 attributes such as strength in compression, retaliation to abrasion and half-cell potential were 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.
 
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ISSN:2080-5187
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