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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	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.
2025	A comparative study of machine learning techniques and data processing for predicting the compressive strength of pervious concrete with supplementary cementitious materials and chemical composition influence	Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2025	Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters	Sathiparan, N.; Wijekoon, S.H.; Ravi, R.; Jeyananthan, P.; Subramaniam, D.N.
2024	Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity	Sathiparan, N.; Wijekoon, S.H.; Jeyananthan, P.; Subramaniam, D.N.