http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/109 2026-09-04T16:00:33Z 2026-09-04T16:00:33Z Sathiparan, N. Jeyananthan, P. Subramaniam, D.N. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12989 2026-09-03T09:33:18Z 2024-01-01T00:00:00Z

Title: Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach Authors: Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. Abstract: Utilizing silica fume as a substitute for cement in pervious concrete offers a viable approach to achieve sustainability within the realm of construction industry. The mechanical characteristics of pervious concrete are influenced by several factors, such as quantity of silica fume utilized as a replacement for cement, the cement content, coarse aggregate, sand, admixture and water used in the mix, aggregate size and the curing period. The present study introduces a predictive model that utilizes machine learning approaches to estimate the compressive strength of pervious concrete blended with silica fume. The models underwent training and testing procedures using 222 datasets from various literature sources. In this study, seven machine learning algorithms were used as statistical evaluation methods to identify the most suitable and reliable model for predicting compressive strength of pervious concrete. Among several models under consideration, the eXtreme Gradient Boosting model showed superior performance in forecasting compressive strength of pervious concrete. The coefficient of determination value obtained for training data is almost one, which suggests a robust correlation between the anticipated and actual values. The root mean squared error of training data is 0.28 MPa, which indicates the mean variation between the predicted and observed values. The coefficient of determination value for the test datasets is 0.97, along with a root mean squared error of 2.21 MPa. The outcomes of the sensitivity analysis conducted on the eXtreme Gradient Boosting model indicate that the parameter with the most significant impact on predicting the compressive strength of pervious concrete is the admixture content, followed by the curing period. This work provides a comprehensive evaluation of the compressive strength of pervious concrete, thereby enhancing the existing knowledge and facilitating its practical application in this domain.

2024-01-01T00:00:00Z Wijekoon, S.H.B. Subramaniam, D.N. Sathiparan, N. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12986 2026-09-03T07:47:03Z 2023-01-01T00:00:00Z

Title: Statistical Assessment of Different Aggregate Shape Factors Authors: Wijekoon, S.H.B.; Subramaniam, D.N.; Sathiparan, N. Abstract: This study aims to investigate aggregate geometric characteristics and analyze the possible aggregate shape factors on their statistical relationships. The study used 2192 crushed rock particles from 5 to 30 mm in diameter for the investigation. The particles were milled in a ball mill at 0, 1000 and 2000 revolutions to obtain different morphological aspects. The milled aggregate was taken and digital image processing techniques were used to obtain images and data of the aggregate which represent the 14 different shape aspects. The obtained data were statistically analyzed using data analysis tools and represented the statistical relationships. These statistical classifications can be useful for researchers to optimize the performance of aggregate mixtures in different morphological shape aspects.

2023-01-01T00:00:00Z Sajeevan, M. Ahilash, N. Shobijan, J. Pravinjan, V. Virupashan, K. Sathiparan, N. Subramaniam, D.N. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12985 2026-09-03T06:40:24Z 2025-01-01T00:00:00Z

Title: Statistical investigation of aggregate size and shape impact on porosity and compressive strength of pervious concrete Authors: Sajeevan, M.; Ahilash, N.; Shobijan, J.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Subramaniam, D.N. Abstract: The shape and size of aggregates are rarely investigated in pervious concrete research despite their impact on porosity and compressive strength. This study analyses the impact of size and shape distribution of aggregates and mix design on compressive strength and porosity of pervious concrete. 486 specimens of 81 different designs (aggregate-size, aggregate-shape, aggregate-to-cement ratio and compaction) were investigated. Compressive strength was measured after 28 days of curing. The porosity of samples was estimated as theoretical porosity (computed from constituent ratios) and measured porosity (using water displacement). Data visualisation was used for pattern recognition while ANOVA, Post-hoc and Bayesian independent t-Tests were employed to statistically verify effects. A significant impact of aggregate-shape on porosity and compressive strength was observed while aggregate size significantly lesser impact. Aggregate to cement ratio had a larger impact on compressive strength while compaction had a significant yet lower impact. Porosity and compressive strengths had different relationships for different shapes of aggregates.

2025-01-01T00:00:00Z Sathiparan, N. Tharuka, R.A.N.S. Jeyananthan, P. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12984 2026-09-03T06:15:31Z 2024-01-01T00:00:00Z

Title: Prediction of moisture content of cementstabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity Authors: Sathiparan, N.; Tharuka, R.A.N.S.; Jeyananthan, P. Abstract: This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learning models, and XGBoost emerges as the most promising model, demonstrating superior accuracy in predicting moisture content based on factors like soil properties, cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive exPlanations) to understand how these features influence the model’s predictions. UPV is the most significant factor affecting predicted moisture content, followed by cement content and soil properties like uniformity coefficient. Also, the study explores the possibility of using a reduced set of features for moisture content prediction. They demonstrate that a combination of UPV, cement content, and uniformity coefficient can achieve good accuracy, highlighting the potential for practical applications where obtaining all data points might be challenging.

2024-01-01T00:00:00Z