http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/109 2026-10-03T17:05:22Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13052 Title: Investigation of the compactability of pervious concrete and its impact on porosity and compressive strength Authors: Subramaniam, D.N. Abstract: Compressive strength is often predicted from porosity or mix-design parameters. The inability to convert laboratory scale-compaction-energy to industrial-scale limits the application of performance prediction models. This study analyses compactability as a predictor which translates easily across studies and applications. Pervious concrete specimens were cast using eight different aggregate-tocement ratios (2.5–7.0), compaction-levels (0–75 blows from standard-Proctor rammer) and 2-types of compaction energy distribution (single-layer and three-layer distributions). 672 specimens were tested for wet-density, theoretical-porosity, measured-porosity, compressive-strength and compactability. The Analysis-of-Variance and classification techniques, quadratic discriminant analysis and the Boosted Forest algorithm classified two groups of specimens based on five performance parameters with 94% accuracy. This indicated a significant difference imparted by compaction energy distribution on pervious concrete performance. Gaussian Process Regression predicted measured the porosity and compressive strength of samples with compactability and aggregate-to-cement ratio with 94% accuracy for both groups of specimens separately. The combined sample matrix model yielded high accuracy (86%) but failed on marginal observations significantly. 2025-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13051 Title: Investigation of Compaction on Compressive Strength and Porosity of Pervious Concrete Authors: Sajeevan, M.; Subramaniam, D.N.; Rinduja, R.; Pratheeba, J. Abstract: Pervious concrete (PC) is a sustainable substitute for conventional concrete application yet limited due to lack of understanding on its performance characteristics. The mix design affects the performance, mainly due to its porous structure that is also not uniform in characteristics when mass produced. Although zero compaction is envisaged for casting of PC, it is important for mass production of PC with uniform properties. This study analyses the impact of compaction on two primary performance indicators of pervious concrete, porosity and compressive strength. Laboratory specimens of size 150 mm cubic were cast with varying aggregate-to-cement ratio (2.5–7.0), compaction (15–75 blows by standard proctor hammer) and compaction distributions (two types), where water-to-cement ratio was maintained at 0.3 and aggregates used were between 12 and 25 mm. Twelve specimens of each design were cast, and six specimens were tested for compressive strength and porosity and another six specimens were cored to obtain cylindrical cores of 100 mm diameter for porosity measurements and porosity distribution analysis using image analytical tools. Results revealed that actual porosity (measured through image analysis) represented the performance of pervious concrete, and that it is perfectly linearly correlated with effective porosity. The type of compaction distribution had significant impact on the relationship between porosity and compressive strength, while the impact was not statistically evident in porosity and compressive strength separately. The performance of the samples, however, showed correlation to the type of compaction employed when machine learning tools are employed. 2025-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13050 Title: Investigation on the effectiveness of fourier shape analysis in classifying milled aggregates Authors: Mithulavan, V.; Samarasinghe, T.; Valluvan, R.; Karnan, N.; Sathiparan, N.; Subramaniam, D.N. Abstract: Material that constitute aggregate depend on their packing efficiency that is defined by the shape distribution of aggregates. Geometrical shape descriptors would not capture morphological aspects of different dimensional scales and frequencies, failing to wholistically numerically represent shape in material prediction models. Fourier shape descriptors are used to analyse single particle morphology, but efficiency to characterize shape of lump samples has not been assessed. This study analyses aggregates milled for different number of revolutions (0, 200, 500 and 1000) in a Los Angeles Abrasion Value instrument, that contrives morphological alterations of different scales. Means of zonal frequency components amplitudes (form, angularity and texture) are statistically different for all classes with more than 99 % confidence. Support Vector Machine and K Nearest Neighbour algorithms classified pairwise classification with an accuracy above 0.8 between milled and unmilled aggregates. Classification of different degrees of milling had significantly lower accuracy (0.57 – 0.65). Mean of texture zone amplitudes was dominant in feature importance in classifying milled and unmilled aggregates while mean of general form zone amplitudes dominated in classification of different degrees of milling (200 – 1000). The amplitude of the 45th frequency component is the dominant feature when all 53 frequency components are used as features, in classifying milled and unmilled aggregates, while 4th to 6th frequency components are dominant in classifying aggregates of different degree of milling. 2025-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13049 Title: Investigation of impact of aggregate shape on pervious concrete using machine learning classification methods Authors: Wijekoon, S.H.B.; Ahilash, N.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. Abstract: Optimizing compaction energy reduces uncertainty in mass production of pervious concrete, but depends on aggregate-shape. This study analyses impact of aggregate size and shape on porosity and compressive strength. Aggregate-to-cement-ratio (AC) (3–5), compaction (Comp) (0–60 blows), aggregate-size (5–25 mm) and aggregate-shape (0, 200 and 1000 revolutions in Los Angeles Abrasion Value) were used to cast 486 samples. Porosity was computed from constituent-ratios (T.Poro) and apparent-weight (M.Poro) and Compressivestrength (Com.S) using Universal Testing Machine. M.Poro and T.Poro had significantly different impact from size and shape of aggregates. Feature selection models, Minimum-Redundancy-Maimum-Relevance (MRMR) and Kruskal-Wallis had significantly different ranking of features, owing to the different concepts. MRMR indicated dominance of M.Poro while a tenth of which was observed for Com.S and less than a twentieth for T.Poro. MRMR indicated significant impact of aggregate size (second highest score, half of M.Poro), indicating the impact aggregate shape had on performance was not uniform across different size of aggregates. Three performance parameters (M.Poro, T.Poro and Com.S) classified observations for aggregate shape (degree of milling) with an accuracy of 92.2% and 93% on Support-Vector-Machine (SVM) and K-Nearest-Neighbours (KNN) algorithms. Inclusion of aggregate size as feature improved accuracy to more than 96% while further inclusion of AC Comp improved the accuracy further to 99.6%. Classification of aggregate size was less accurate (70–80% with performance parameters alone and 92–93% with three design parameters included), indicating less impact on pervious concrete performance compared to shape of aggregates. 2025-01-01T00:00:00Z