ORIGINAL ARTICLE
Impermeability Evaluation Of Concrete With Fly Ash Aggregate And Prediction With Modelling
More details
Hide details
1
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
KEYWORDS
TOPICS
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.
REFERENCES (16)
1.
Singh, M and Siddique, R 2014. Strength properties and micro-structural properties of concrete containing coal bottom ash as partial replacement of fine aggregate. Construction and Building Materials 50, 246-256.
2.
Soco, E and Kalembkiewicz, J 2007. Investigations of sequential leaching behavior of Cu and Zn from coal fly ash and their mobility in environmental conditions, Journal of Hazardous Materials, 145, 482–487.
3.
Kisku, N, Joshi, H, Ansari, M, Panda, SK and Nayak, S 2017. Dutta, S.C., A critical review and assessment for usage of recycled aggregate as sustainable construction material, Construction Building Materials, 131, 721–740.
4.
Samuel, MF, Brooke Nicholas, GJ, Leonard, G M and Ingham, J M, 2011. Mixture design development and performance verification of structural lightweight pumice aggregate concrete. Journal of Materials in Civil Engineering 23(8), 1211–9.
5.
Patel, SK, Majhi, RK, Satpathy, HP and Nayak, AN 2019. Durability and microstructural properties of lightweight concrete manufactured with fly ash cenosphere and sintered fly ash aggregate. Construction and Building Materials 226, 579-590.
6.
Ramamurthy, K and Harikrishnan, KI 2006. Influence of binders on properties of sintered fly ash aggregate. construction and building materials 28(1), 33–8.
7.
IS: 383-1970, Specifications for Coarse and Fine Aggregate from Natural Sources for Concrete, Bureau of Indian Standards, New Delhi, India, 1970.
8.
IS 10262, Indian Standard Concrete Mix Proportioning – Guidelines, Bureau of Indian Standards, New Delhi, 2009.
9.
IS: 516, Indian Standard Methods of Tests for Strength Concrete, Bureau of Indian Standards, New Delhi, 1959 [Reaffirmed in 1999].
10.
ASTM C1138-97, Standard Test Method for Abrasion Resistance of Concrete (Underwater Method), ASTM International, West Conshohocken, USA, 1997, 1–4.
11.
Dai, F, Nie, G H and Chen, Y 2020. The municipal solid waste generation distribution prediction system based on FIG–GA-SVR model. Journal of Material Cycles and Waste Management 22(5), 1352-1369.
12.
Muller, KR, Mika, S, Ratsch, G, Tsuda, K and Scholkopf, B 2001. An introduction to kernel-based learning algorithms. IEEE transactions on neural networks 12(2), 181-201.
13.
Patel, SK, Majhi, RK, Satpathy, HP, and Nayak, AN 2019. Durability and microstructural properties of lightweight concrete manufactured with fly ash cenosphere and sintered fly ash aggregate. Construction and Building Materials 226, 579-590.
14.
Abid, SR, Hilo, AN, Ayoob, NS and Daek, YH 2019. Underwater abrasion of steel fiber-reinforced self-compacting concrete. Case Studies in Construction Materials 11, 1-17.
15.
Adriman, R, Ibrahim, IBM, Huzni, S, Fonna, S and Ariffin, AK 2022. Improving half-cell potential survey through computational inverse analysis for quantitative corrosion profiling. Case Studies in Construction Materials, 16, p.e00854.
16.
Lalitha, G, Sashidhar, C and Ramachandrudu, C 2020. Evaluation of Mechanical Properties on M30 Concrete Crushed Waste Glass as Fine Aggregate. Journal of Green Engineering 10(9), 5242-5249.