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Results 571-580 of 585 (Search time: 0.003 seconds).
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Issue DateTitleAuthor(s)
2024Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approachSathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2024Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural networkSathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2025Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parametersSathiparan, N.; Wijekoon, S.H.; Ravi, R.; Jeyananthan, P.; Subramaniam, D.N.
2024Optimisation of pervious concrete performance by varying aggregate shape, size, aggregate-tocement ratio, and compaction effort by using the Taguchi methodWijekoon, S.H.B.; Sathiparan, N.; Subramaniam, D.N.
2024Characterisation of the shape of aggregates using image analysisSubramaniam, D.N.; Dassanayake, D.H.H.P.; Ahilash, N.; Wijekoon, S.H.B.; Sathiparan, N.
2023Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysisSubramaniam, D.N.; Sathiparan, N.
2024Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocitySathiparan, N.; Jeyananthan, P.
2024Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocitySathiparan, N.; Wijekoon, S.H.; Jeyananthan, P.; Subramaniam, D.N.
2025A systematic review of corncob ash in construction: Current findings and future directionsSathiparan, N.
2025Investigation of impact of aggregate shape on pervious concrete using machine learning classification methodsWijekoon, S.H.B.; Ahilash, N.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.