Lindry Lydia Karongkong, Agustinus Agus Setiawan*, Harianto Hardjasaputra

Department of Civil Engineering, Universitas Pembangunan Jaya

Jl. Cendrawasih Raya, Sawah Baru. Ciputat, Tangerang Selatan, Banten 15413

 

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ABSTRACT

Concrete is a construction material that has uncertain quality and must be designed with such a composition to achieve the expected quality. Geopolymer concrete is an environmentally friendly concrete obtained by replacing Portland cement with fly ash as a binder. Geopolymer concrete requires an alkaline solution as an activator in the polymerization of aluminum and silica. The determining process of the concrete mix composition is called mix design. Mix design for normal concrete is regulated in SNI 7656:2012 while for geopolymer concrete there is no specific standard that regulates it. With the development of technology and information, existing geopolymer concrete data can be used for mix design modeling. With the data of the geopolymer concrete mixture processed using the SPSS multiple linear regression method, the regression model obtained is Y = 0.165x1 + 0.055x2 + 0.037x3 - 0.053x4 + 0.263x5 - 0.288x6 - 137.18. This regression model states that the variables x4 (NaOH) and x6 (water) have a negative effect on the compressive strength of concrete. The effect of independent variables on the compressive strength of concrete simultaneously is 29.6% while the remaining 70.1% is influenced by other factors with the standard error of the estimate value of the model is 9,60179.


Keywords: Concrete, Geopolymer, Multiple linear regression, SPSS.


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REFERENCES

  1. Davidovits, J. 2020. Geopolymer Chemistry and Applications. 5th edition Geopolymer Institute

  2. Diana, A. I., Fansuri, S., Deshariyanto, D. 2020. Addition of bamboo leaf ash as a substitution of cement material on concrete performance. PADURAKSA, 9, 172–182.

  3. Duxon P., Jiminez, A.F., Provis, J.L., Luckey, G.C., Palomo, A., van Deventer, J.S.J. 2007. Geopolymer technology: the current state of the art. Journal of Materials Science, 42, 2917–2933.

  4. Fassa, F. 2017. Introduction to construction materials. Universitas Pembangunan Jaya.

  5. Ghozali, I. 2006. Multivariate analysis application with spss program. Semarang: Badan Penerbit Universitas Diponegoro.

  6. Ghozali, I. 2011. Aplikasi Analisis Multivariate dengan Program IBM SPSS 19. Semarang: Badan Penerbit Universitas Diponegoro.

  7. Hardjasaputra, H., Ekawati, E. 2018. Penelitian Rancangan Campuran Beton Geopolimer Berbasis Fly Ash Pltu Suralaya–Banten Terhadap Kuat Tekan Dan Kuat Lentur. Jurnal Ilmiah Teknik Sipil, 22, 24–33.

  8. Hardjasaputra, H., Cornelia, M., Gunawan, Y., Surjasaputra, I., Lie, H., Rachmansyah, Ng, G. 2019. Study of mechanical properties of fly ash-based geopolymer concrete. IOP Conference Series: Materials Science and Engineering (hal. 1-9). IOP Publishing.

  9. Hardjito, D., Rangan, B.V. 2005. Development and properties of low-calcium fly ash-based geopolymer concrete. perth, Australia: Curtin University of Technology.

  10. Hasan, A., Arif, M., Shariq, M. 2019. Use of geopolymer concrete for a cleaner and sustainable environment – a review of mechanical properties and microstructure. Journal of Cleaner Production, 223, 704–728.

  11. Janie, D.N. 2012. Statistik Deskriptif & Resgresi Linier Berganda dengan SPSS. Semarang: Semarang University Press.

  12. Patankar, S., Ghugal, Y., Jamkar Sanjay S. 2015. Mix design of fly ash based geopolymer concrete. Advances in Structural Engineering, 1619–1634.

  13. Pham, T., Nguyen, T., Nguyen, L., Nguyen, P. 2020. A neural network approach for predicting hardened property of geopolymer concrete. International Journal of GEOMATE, 19, 176–184.

  14. Rachmansyah, Hardjasaputra, H., Meilanie, C. 2019. Experimental study of effect additional water on high performance geopolymer concrete. MATEC Web of Conferences 270, 01004.

  15. Reddy, R.S. 2020. Mix design for flyash based geopolymer concerte. Journal of Engineering Science, 489–504.

  16. Ritchie, H., Roser, M. 2016. Emissions by sector. Diambil kembali dari Our World in Data: https:// ourworldindata.org/emissions-by-sector#direct-industrial-processes-5-2

  17. Santoso, W. 2019. Produksi Semen OPC Bakal Dikurangi. (Bisnis.com, Pewawancara).

  18. Saputro, T. H., Hermawan, A. 2019. Implementasi big data untuk pemodelan estimasi kuat tekan dengan metode linear regression. Jurnal Sistem dan Teknologi Informasi, 7, 207–212.

  19. Spesifikasi abu terbang batubara dan pozolan alam mentah atau yang telah dikalsinasi untuk digunakan dalam beton. 2014. SNI 2460:2014. Badan Standardisasi Nasional.

  20. The procedure for making a normal concrete mix plan. SNI 03-2847-2002. Badan Standardisasi Nasional.

  21. Wirotama, I.G., Nurlina, S., Firdausy, A.I. 2018. Correlation of concrete compressive strength values using non-destructive tests and destructive tests. Jurnal Mahasiswa Jurusan Teknik Sipil, 1, 404–411.


ARTICLE INFORMATION

Received: 2021-07-19
Revised: 2022-03-24
Accepted: 2022-03-24
Available Online: 2022-05-27


Cite this article:

Karongkong, L.L., Setiawan, A.A., Hardjasaputra, H., Predicting of geopolymer concrete compressive strength using multiple linear regression method. International Journal of Applied Science and Engineering, 19, 2021272. https://doi.org/10.6703/IJASE.202206_19(2).006

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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