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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, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
Published in Next materials (01.10.2025)
Published in Next materials (01.10.2025)
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Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysis
Subramaniam, Daniel Niruban, Sathiparan, Navaratnarajah
Published in The international journal of pavement engineering (28.01.2023)
Published in The international journal of pavement engineering (28.01.2023)
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Sustainable Pervious Concrete with Silica Fume as Cement Replacement: A Review
Sathiparan, Navaratnarajah, Subramaniam, Daniel Niruban
Published in Transportation research record (01.02.2025)
Published in Transportation research record (01.02.2025)
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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, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
Published in Measurement : journal of the International Measurement Confederation (15.02.2024)
Published in Measurement : journal of the International Measurement Confederation (15.02.2024)
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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, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
Published in Asian journal of civil engineering. Building and housing (01.01.2024)
Published in Asian journal of civil engineering. Building and housing (01.01.2024)
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Potential use of crushed waste glass and glass powder in sustainable pervious concrete: A review
Sathiparan, Navaratnarajah, Subramaniam, Daniel Niruban
Published in Cleaner Waste Systems (01.12.2024)
Published in Cleaner Waste Systems (01.12.2024)
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Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural network
Sathiparan, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
Published in The international journal of pavement engineering (31.12.2024)
Published in The international journal of pavement engineering (31.12.2024)
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Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach
Sathiparan, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
Published in Asian journal of civil engineering. Building and housing (01.04.2024)
Published in Asian journal of civil engineering. Building and housing (01.04.2024)
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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
Wijekoon, Sathushka Heshan, Shajeefpiranath, Thirugnasivam, Subramaniam, Daniel Niruban, Sathiparan, Navaratnarajah
Published in Asian journal of civil engineering. Building and housing (01.01.2024)
Published in Asian journal of civil engineering. Building and housing (01.01.2024)
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Boosted Forest Model for Aggregate Size and Shape Impact on Porosity and Compressive Strength of Pervious Concrete
Subramaniam, Daniel Niruban, Jeyananthan, Pratheeba, Sathiparan, Navaratnarajah
Published in International journal of pavement research & technology (18.07.2025)
Published in International journal of pavement research & technology (18.07.2025)
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Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations
Sathiparan, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
Published in Multiscale and Multidisciplinary Modeling, Experiments and Design (01.09.2024)
Published in Multiscale and Multidisciplinary Modeling, Experiments and Design (01.09.2024)
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