http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/108 2026-09-13T06:33:40Z 2026-09-13T06:33:40Z Sathiparan, N. Wijekoon, S.H. Jeyananthan, P. Subramaniam, D.N. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12996 2026-09-07T03:41:49Z 2024-01-01T00:00:00Z

Title: Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity Authors: Sathiparan, N.; Wijekoon, S.H.; Jeyananthan, P.; Subramaniam, D.N. Abstract: The present study explores the potential of machine learning to predict the porosity and permeability of pervious concrete constructed on mix parameters (compaction energy, aggregate-to-cement ratio and aggregate size) and ultrasonic velocity. The prediction models use non-destructive measurements and mixed design variables, which can help the construction sector apply the models without any theoretical expertise. The study uses 225 data samples from an experimental study. This study used six machine learning algorithms, namely, linear regression, artificial neural networks, boosted decision tree regression, random forest regression, K-nearest neighbour and support vector regression, to determine the best predictive model. The results show that the ANN model is the best technique for predicting the porosity of pervious concrete (R2 = 0.9502 for training datasets and R2 = 0.8958 for testing datasets) and boosted decision tress for permeability of pervious concrete (R2 = 0.9323 for training datasets and R2 = 0.7574 for testing datasets). The sensitivity analysis of the random forest regression model reveals that ultrasonic pulse velocity is the most influential parameter for the prediction of porosity and permeability of pervious concrete. The proposed models provide a more accurate method for estimating the porosity and permeability of pervious concrete.

2024-01-01T00:00:00Z Sathiparan, N. Wijekoon, S.H. Ravi, R. Jeyananthan, P. Subramaniam, D.N. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12995 2026-09-07T03:33:32Z 2025-01-01T00:00:00Z

Title: Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters Authors: Sathiparan, N.; Wijekoon, S.H.; Ravi, R.; Jeyananthan, P.; Subramaniam, D.N. Abstract: This study investigates the influence of aggregate size, aggregate-to-cement ratio, and compaction effort on pervious concrete's porosity and compressive strength. It proposes using response surface methodology and machine learning techniques to predict porosity and compressive strength. Fifteen mix designs (three aggregate sizes and five aggregate-to-cement ratios) with seven compaction energy levels were employed. Experimental data analysis using various regression models revealed that the quadratic model provided the best fit for predicting both porosity and compressive strength. Machine learning models were employed to predict porosity and compressive strength more accurately. Among the models investigated, the artificial neural network achieved superior performance across all datasets (training, testing, and validation). This suggests the artificial neural network model can effectively capture the complex relationships between input and response variables. Sensitivity analysis using SHAP (SHapley Additive exPlanations) revealed that compaction energy significantly impacts both porosity and compressive, while aggregate size has the most negligible influence.

2025-01-01T00:00:00Z Sathiparan, N. Jeyananthan, P. Subramaniam, D.N. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12994 2026-09-07T03:17:41Z 2024-01-01T00:00:00Z

Title: Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural network Authors: Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. Abstract: The present study investigates the effect of the total amount of chemical constituents in cement and supplementary cementitious materials on the compressive strength of pervious concrete. Experimental datasets were collected from the literature on different types of pervious concrete specimens modified with supplementary cementitious materials. A total of 659 data observations were collected. These were then analysed and modelled using three different approaches: linear regression (LR), full quadratic (FQ) and artificial neural network (ANN) models. These models’ purpose was to predict pervious concrete's compressive strength. The accuracy of the models was evaluated using correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), a-20 index and error distribution. The ANN model demonstrated superior effectiveness and accuracy in predicting the compressive strength of pervious concrete, as indicated by the performance metrics. The analysis results showed that Al2O3 content and curing time were the most influential parameters in predicting the compressive strength of concrete. In addition, SHapley Additive exPlanations (SHAP) analysis could determine if each input variable had a positive or negative effect on compressive strength. Al2O3, CaO, curing time and water content positively affected the compressive strength of pervious concrete, while SiO2 and corresponding alkalis had a negative effect.

2024-01-01T00:00:00Z Sathiparan, N. Jeyananthan, P. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12993 2026-09-07T03:04:38Z 2024-01-01T00:00:00Z

Title: Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity Authors: Sathiparan, N.; Jeyananthan, P. Abstract: The current study aimed to investigate the possibility of predicting the compressive strength of geopolymer mortar by mix design parameters, ultrasonic pulse velocity (UPV) and machine learning techniques. Here the geopolymer mortar is produced from eggshell ash and rice husk ash as precursors, NaOH solution as activator and quarry waste as fine aggregate. Twenty-seven combinations of geopolymer mix and a total of 189 mortar cubes were cast and tested for UPV and compressive strength. Seven different machine learning techniques were used to predict the compressive strength assessment tools: linear regression, artificial neural networks, boosted tree regression, random forest regression, K-Nearest Neighbor, support vector regression and XGboost. Among the diverse machine learning models evaluated in this study, XGboost exhibited remarkable efficacy in forecasting the compressive strength of geopolymer mortar. The investigation conducted using SHAP indicates that the concentration of UPV shows the most substantial influence on the prediction of compressive strength.

2024-01-01T00:00:00Z