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Issue Date	Title	Author(s)
2023	Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity	Sathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023	A mathematical model to predict the porosity and compressive strength of pervious concrete based on the aggregate size, aggregate‑to‑cement ratio and compaction effort	Sathushka Heshan, W.; Thirugnasivam, S.; Daniel Niruban, S.; Sathiparan, N.
2023	Effect 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 techniques	Sathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023	Soft computing techniques to predict the electrical resistivity of pervious concrete	Daniel Niruban, S.; Pratheeba, J.; Sathiparan, N.
2023	Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach	Sathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023	Investigation of Compaction on Compressive Strength and Porosity of Pervious Concrete	Sajeevan, M.; Daniel Niruban, S.; Rinduja, R.; Pratheeba, J.
2022	Influence of aggregate gradation and compaction on compressive strength and porosity characteristics of pervious concrete	Anburuvel, A.; Daniel Niruban, S.
2023	Mathematical Model to Predict the Compressive Strength of Pervious Concrete	Sathushka Heshan, W.; Janotha, P.; Shajeefpiranath, T.; Daniel Niruban, S.; Sathiparan, N.
2023	Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysis	Daniel Niruban, S.; Sathiparan, N.
2023	Investigation of boundary layer impact on pervious concrete	Sajeevan, M.; Daniel Niruban, S.