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Issue DateTitleAuthor(s)
2023Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivitySathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023A mathematical model to predict the porosity and compressive strength of pervious concrete based on the aggregate size, aggregate‑to‑cement ratio and compaction effortSathushka Heshan, W.; Thirugnasivam, S.; Daniel Niruban, S.; Sathiparan, N.
2023Characterization of the shape of aggregates using image analysis and machine learning classification toolsDaniel Niruban, S.; Mohan, S.; Pratheeba, J.; Sathushka, Heshan Bandara Wijekoon; Sathiparan, N.
2023Effect of aggregate size, aggregate to cement ratio and compaction energy on ultrasonic pulse velocity of pervious concrete: prediction by an analytical model and machine learning techniquesSathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023Soft computing techniques to predict the electrical resistivity of pervious concreteDaniel Niruban, S.; Pratheeba, J.; Sathiparan, N.
2023Characterization of the shape of aggregates using image analysis and machine learning classification toolsDaniel Niruban, S.; Sajeevan, M.; Pratheeba, J.; Sathushka Heshan, B.W.; Sathiparan, N.
2023Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approachSathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023Mathematical Model to Predict the Compressive Strength of Pervious ConcreteSathushka Heshan, W.; Janotha, P.; Shajeefpiranath, T.; Daniel Niruban, S.; Sathiparan, N.
2023Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysisDaniel Niruban, S.; Sathiparan, N.

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