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Issue Date	Title	Author(s)
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.
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	Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural network	Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2024	Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity	Sathiparan, N.; Jeyananthan, P.
2024	Optimisation of pervious concrete performance by varying aggregate shape, size, aggregate-tocement ratio, and compaction effort by using the Taguchi method	Wijekoon, S.H.B.; Sathiparan, N.; Subramaniam, D.N.
2023	Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysis	Subramaniam, D.N.; Sathiparan, N.
2024	Characterisation of the shape of aggregates using image analysis	Subramaniam, D.N.; Dassanayake, D.H.H.P.; Ahilash, N.; Wijekoon, S.H.B.; Sathiparan, N.
2024	Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach	Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2023	Statistical Assessment of Different Aggregate Shape Factors	Wijekoon, S.H.B.; Subramaniam, D.N.; Sathiparan, N.
2025	Statistical investigation of aggregate size and shape impact on porosity and compressive strength of pervious concrete	Sajeevan, M.; Ahilash, N.; Shobijan, J.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Subramaniam, D.N.
2024	Prediction of moisture content of cementstabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity	Sathiparan, N.; Tharuka, R.A.N.S.; Jeyananthan, P.
2023	Soft computing techniques to predict the compressive strength of groundnut shell ash‑blended concrete	Sathiparan, N.; Jeyananthan, P.
2025	Comparative analysis of machine learning models and standard codes for predicting compressive strength in hollow block masonry	Sathiparan, N.; Jeyananthan, P.
2023	Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity	Sathiparan, N.; Jeyananthan, P.
2025	Mathematical Model and Machine Learning Techniques to Predict the Compressive Strength of Groundnut Shell Ash Blended Sandcrete	Sathiparan, N.; Jeyananthan, P.
2023	Compression and Bond Properties of Fired Clay Brick Masonry with Cocopeat Blended Binding Mortar	Muralitharan, M.; Sathiparan, N.
2024	Characterization of the shape of aggregates using image analysis and machine learning classification tools	Subramaniam, D.N.; Sajeevan, M.; Pratheeba, J.; Wijekoon, S.H.B.; Sathiparan, N.
2025	Effect of rice husk ash on compressive strength of sustainable pervious concrete and prediction model using machine learning algorithms	Sathiparana, N.; Jeyananthan, P.; Subramaniam, D.N.
2025	Optimizing fly ash and rice husk ash as cement replacements on the mechani-cal characteristics of pervious concrete	Sathiparan, N.; Subramaniam, D.N.
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.