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Group Indexing of Fly Ashes Using Unsupervised Learning and Fuzzy Clustering Techniques

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Recent Advances in Civil Engineering for Sustainable Communities (IACESD 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 459))

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Abstract

For long, fly ash is the most sought-after material in the construction industry. It is used as cementitious material as a supplement (SCM) to ordinary Portland cement (OPC) concrete as a replacement for cement. Recently, its usage as a geopolymer cement while developing geopolymer concrete (GPC) mixes has been well documented in bibliography. Apart from this, fly ash has found wide utility in every other realm of civil engineering as a material to reckon with. Fly ashes have been traditionally classified into two groups Group-C and Group-F as per ASTM. BIS is classified as Class F, Class C and Class P. These categorizations are hinged on chemical constituents and their contribution as a constituent material in concrete matrix. This paper presents the application of unsupervised machine learning techniques namely, k-means, k-medoids and fuzzy c-means algorithm have been applied to classify fly ashes on a data set of 400 instances. The chemical composition like SiO2, Al2O3, Fe2O3, CaO and LOI have been considered as fly ash attributes. The data instances are clustered optimally into three groups. K-means and k-medoids algorithms were able to cluster with minimal overlapping with similar number of instances of each group. These optimal numbers of clusters were found using elbow method. However, fuzzy c-means algorithm grouped differently by attaching a fly ash data instance to an appropriate group with higher degree of belongingness of each data instance to a particular cluster. From the results obtained, the clustering rendered by fuzzy logic approach proved to be optimal as it placed the instances in groups affixing them with degree of belongingness. Based on this clustering, group characteristics are also elicited.

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References

  1. Moghal AAB (2017) State-of-the-art review on the role of fly ashes in geotechnical and geoenvironmental applications. J Mater Civ Eng 29(8):1–15

    Article  Google Scholar 

  2. Hamoui AKE, Raydan R, Khatib J, Jahami A (2022) A critical review of fly ash geopolymer concrete. Second Int Conf Constr Eng 21(3):1013–1028

    Google Scholar 

  3. Wang Y, Burris L, Douglas Hooton R, Shearer C, Suraneni P (2021) Effects of unconventional fly ashes on cementitious paste properties. Cem Concr Compos 125:104–291

    Google Scholar 

  4. Hasa HM, Saleh IR, Razzaq HY (2022) Fly ash utilization: use of fly ash in hydraulic concrete. 13(05)

    Google Scholar 

  5. Bhatt A, Priyadarshini S, Mohanakrishnan AA, Abri A, Sattler M, Techapaphawit S (2021) Physical, chemical, and geotechnical properties of coal fly ash: a global review. Elsevier, 486

    Google Scholar 

  6. Nataraja MC, Jayaram MA, Ravikumar CN (2006) Kohonen's feature maps for fly ash categorization. Int J Neural Syst 16(6):457–466

    Google Scholar 

  7. Jayaram MA, Nataraja MC, Ravikumar CN (2006) Group indexing fly ashes through radial basis function networks. Indian Concr J 80(6):39–45

    Google Scholar 

  8. Jayaram MA, Nataraja MC (2007) A three-stage neural model for attribute based classification and indexing of fly ashes. Inte Joint Conf Artif Intell 7:871–876

    Google Scholar 

  9. Jayaram MA, Nataraja MC, Ravikumar CN (2011) Cascading SOFM and RBF networks for categorization and indexing of fly ashes. de Gruyter 20:61–77

    Google Scholar 

  10. Risdanareni P, Puspitasari P, Jaya EJ (2017) Chemical and physical characterization of fly ash as geopolymer material. Matec Web Conf 97:1–31

    Google Scholar 

  11. Matthew Chelberg BS (2019) The effect of fly ash chemical composition on compressive strength of fly ash Portland cement concrete. Thesis The Ohio State University, 1–75

    Google Scholar 

  12. Mutnuru SR, Patankar SV (2021) Fly ash—an alternative construction material. Recent Adv Civ Eng Conf 1:18–23

    Google Scholar 

  13. Gupta P, Nagpal G, Gupta N (2021) Flyash based geopolymer: an emerging sustainable solution for heavy metal remediation from aqueous medium. Beni-Suef Univ J Basic Appl Sci 10:89

    Google Scholar 

  14. Das SK (2018) Parametric study of flyash based geopolymer concrete. Int J Eng Technol 7(2.31)

    Google Scholar 

  15. A detailed report on fly ashes: Ultratech

    Google Scholar 

  16. Longarini N, Crespi P, Zucca M, Giordano N, Silvestro G (2014) The use of fly ash in high strength concrete mix design. In: Conference on environment and mineral processing and exibition, 1640–4920

    Google Scholar 

  17. Ramli R, Idris FM, Yapandi MFKM, Saad JM, Akmal Biyamin S (2013) Investigation on different compositions of flyash concrete on gamma and neutron radiation. Conf Ser Mater Sci Eng 555:1–8

    Google Scholar 

  18. Santhikala R, Chandramouli K, Pannirselvam N (2022) Stabilization of expansive soil using flyash based geopolymer. Mater Today Proc 68(1):110–114

    Article  Google Scholar 

  19. Pritam AM, Patil SS, Shah CR (2020) The study on flyash based geo polymer concrete. Int J Mod Trends Eng Res 5(278):30–38

    Google Scholar 

  20. Nataraja MC, Jayaram MA, Raikumar CN (2007) Inductive classifying artificial network for fly ash type categorization. Article Eng Lett 14(1):28

    Google Scholar 

  21. Bhatt A, Priyadarshini S, Mohanakrishnan AA, Abri A, Sattler M, Techapaphawit S (2019) Physical, chemical, and geotechnical properties of coal fly ash: a global review. Case Stud Constr Mater 11:263

    Google Scholar 

  22. Nordin N, Abdullah MMAB, Tahir MFM, Sandu AV, Hussin K (2016) Utilization of fly ash waste as construction material. Int J Conserv Sci 7(1):161–166

    Google Scholar 

  23. Tkaczewska E (2021) Effect of chemical composition and network of fly ash glass on the hydration process and properties of Portland—fly ash cement. J Mater Eng Perform 30:9262–9282

    Article  Google Scholar 

  24. Katherine LA, Stutzman P, Juenger MCG (2016) Identifying glass compositions in fly ash. Front Mater 3(1):1–10

    Google Scholar 

  25. Nizar K, Mustafa Al Bakri AM, Rafiza AR, Kamarudin H, Alida A, Zarina Y (2014) Study on physical and chemical properties of fly ash from different area in Malaysia. Key Eng Mater 594–595:985–989

    Google Scholar 

  26. Jayaram MA (2007) Innovative methods and applications of soft computing in concrete technology: an interdiciplinary approach. Ph.D. thesis, submitted to Visveswaraya Technological University Belgaum, Karnataka, India

    Google Scholar 

  27. Han J, Kamber M, Pai J (2012) Data mining: concepts and techniques, IV edn. Morgan Kauffman Series

    Google Scholar 

  28. Ross TJ (2016) Fuzzy logic and engineering applications, IV edn. Wiley Publications

    Google Scholar 

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Authors and Affiliations

  1. RASTA—Center for Road Technology, VOLVO Construction Equipment Campus, Peenya Industrial Area, Bengaluru, India

    M. A. Jayaram & K. C. Chandana Priya

Authors
  1. M. A. Jayaram
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  2. K. C. Chandana Priya
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Correspondence to M. A. Jayaram .

Editor information

Editors and Affiliations

  1. Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

    N. Vinod Chandra Menon

  2. Department of Civil Engineering, National Institute of Technology, Mangalore, Karnataka, India

    Sreevalsa Kolathayar

  3. Department of Civil Engineering, University of Aveiro, Aveiro, Portugal

    Hugo Rodrigues

  4. Department of Civil Engineering, Jyothy Institute of Technology, Bangalore, Karnataka, India

    K. S. Sreekeshava

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jayaram, M.A., Chandana Priya, K.C. (2024). Group Indexing of Fly Ashes Using Unsupervised Learning and Fuzzy Clustering Techniques. In: Menon, N.V.C., Kolathayar, S., Rodrigues, H., Sreekeshava, K.S. (eds) Recent Advances in Civil Engineering for Sustainable Communities. IACESD 2023. Lecture Notes in Civil Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-97-0072-1_10

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  • DOI: https://doi.org/10.1007/978-981-97-0072-1_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0071-4

  • Online ISBN: 978-981-97-0072-1

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