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International Journal of Pavement Engineering Volume 24, 2023 - Issue 2
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Research Article

Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach

Navaratnarajah Sathiparana Department of Civil Engineering, Faculty of Engineering, University of Jaffna, Jaffna, Sri LankaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-8570-0580
ORCID Icon,
Pratheeba Jeyananthanb Department of Computer Engineering, Faculty of Engineering, University of Jaffna, Jaffna, Sri LankaORCID Iconhttps://orcid.org/0000-0003-0476-3964
ORCID Icon &
Daniel Niruban Subramaniama Department of Civil Engineering, Faculty of Engineering, University of Jaffna, Jaffna, Sri LankaORCID Iconhttps://orcid.org/0000-0002-1023-1617
ORCID Icon
Article: 2287146 | Received 13 Sep 2023, Accepted 19 Nov 2023, Published online: 06 Dec 2023

Citations (3)

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Navaratnarajah Sathiparan, Sathushka Heshan Wijekoon, Pratheeba Jeyananthan & Daniel Niruban Subramaniam. (2024) Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity. International Journal of Pavement Engineering 25:1.
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Articles from other publishers (2)

Navaratnarajah Sathiparan, Pratheeba Jeyananthan & Daniel Niruban Subramaniam. (2024) Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations. Multiscale and Multidisciplinary Modeling, Experiments and Design.
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Navaratnarajah Sathiparan, Pratheeba Jeyananthan & Daniel Niruban Subramaniam. (2024) Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach. Asian Journal of Civil Engineering 25:3, pages 2963-2977.
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