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Browsing by Subject Machine learning

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Showing results 1 to 20 of 24  next >
Issue DateTitleAuthor(s)
2025Comparative analysis of machine learning models and standard codes for predicting compressive strength in hollow block masonrySathiparan, N.; Jeyananthan, P.
2025A comparative study of machine learning techniques and data processing for predicting the compressive strength of pervious concrete with supplementary cementitious materials and chemical composition influenceSathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2023Effect 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 techniquesSathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2025Effect of rice husk ash on compressive strength of sustainable pervious concrete and prediction model using machine learning algorithmsSathiparana, N.; Jeyananthan, P.; Subramaniam, D.N.
2021Harnessing Machine Learning Techniques for Mapping Aquaculture Waterbodies in BangladeshHannah, F.; Nejadhashemi, A.P.; Juan Sebastian, H.; Nathan, M.; Josue, K.; Ian Kropp; Eeswaran, R.; Belton, Ben.; Mahfujul Haque, M.
2025The impact of oxides of cementitious materials on mortar strength: A machine learning perspectiveSathiparan, N.
2025Investigation of the compactability of pervious concrete and its impact on porosity and compressive strengthSubramaniam, D.N.
2025Mathematical Model and Machine Learning Techniques to Predict the Compressive Strength of Groundnut Shell Ash Blended SandcreteSathiparan, N.; Jeyananthan, P.
2025Predicting compressive strength in cement mortar: The impact of fly ash composition through machine learningSathiparan, N.
2023Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivitySathiparan, N.; Pratheeba, J.
2023Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivitySathiparan, N.; Jeyananthan, P.
2024Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocitySathiparan, N.; Jeyananthan, P.
2023Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approachSathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parametersSathiparan, N.; Pratheeba, J.
2024Prediction of moisture content of cementstabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocitySathiparan, N.; Tharuka, R.A.N.S.; Jeyananthan, P.
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.
2023Soft computing techniques to predict the compressive strength of groundnut shell ash-blended concreteSathiparan, N.; Pratheeba, J.
2023Soft computing techniques to predict the compressive strength of groundnut shell ash‑blended concreteSathiparan, N.; Jeyananthan, P.
2023Soft computing techniques to predict the electrical resistivity of pervious concreteDaniel Niruban, S.; Pratheeba, J.; Sathiparan, N.
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.
Showing results 1 to 20 of 24  next >