Research Papers - Department of Civil Engineering

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Author: search.filters.author.Muttil, N

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    PublicationOpen Access
    Reviving Urban Landscapes: Harnessing Pervious Concrete Pavements with Recycled Materials for Sustainable Stormwater Management
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-10-29) Gunathilake, T.A; Siriwardhana,K.D; Miguntanna,N; Miguntanna, Nadeeka; Rathnayake, U; Muttil, N
    This study examines the effectiveness of pervious concrete pavements as a sustainable and cost-effective stormwater management technique, particularly by incorporating locally sourced recycled materials into their design. It evaluates the stormwater treatment potential of three pervious concrete pavement types incorporating recycled plastic, glass, and crushed concrete aggregates, with six design variations produced using 25% and 50% replacements of coarse aggregates from these materials. The key properties of pervious concrete, namely compressive strength, porosity, unit weight, and infiltration, and key water quality indicators, namely pH, electrical conductivity (EC), total suspended solids (TSS), colour, turbidity, chemical oxygen demand (COD), nitrate (NO3−), and orthophosphate (PO43−), were analysed. Results indicated an overall improvement in the quality of the stormwater runoff passed through all pervious concrete pavements irrespective of composition. Notable reductions in turbidity, TSS, colour, COD, PO43−, and NO3− underscored the effectiveness of pervious concrete containing waste materials in the treatment of stormwater runoff. Pervious concrete pavements with 25% recycled concrete exhibited optimal performance in reducing TSS, COD, and PO43− levels, while the 50% recycled concrete variant excelled in diminishing turbidity. However, the study found that the use of recycled materials in pervious concrete pavements affects properties like compressive strength and infiltration rate differently. While incorporating 25% and 50% recycled concrete aggregates did not significantly reduce compressive strength, the effectiveness of stormwater treatment varied based on the mix design and type of recycled material used. Thus, this study highlights the potential of utilizing recycled waste materials in pervious concrete pavements for sustainable stormwater management.
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    PublicationOpen Access
    Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance
    (MDPI, 2023-06-15) Sandamal, K; Shashiprabha, S; Muttil, N; Rathnayake, U
    Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.
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    PublicationOpen Access
    Analysis of Meandering River Morphodynamics Using Satellite Remote Sensing Data—An Application in the Lower Deduru Oya (River), Sri Lanka
    (MDPI, 2022-07-16) Basnayaka, V; Samarasinghe, J. T; Gunathilake, M. B; Muttil, N; Hettiarachchi, D. C; Abeynayaka, A; Rathnayake, U
    River meandering and anabranching have become major problems in many large rivers that carry significant amounts of sediment worldwide. The morphodynamics of these rivers are complex due to the temporal variation of flows. However, the availability of remote sensing data and geographic information systems (GISs) provides the opportunity to analyze the morphological changes in river systems both quantitatively and qualitatively. The present study investigated the temporal changes in the river morphology of the Deduru Oya (river) in Sri Lanka, which is a meandering river. The study covered a period of 32 years (1989 to 2021), using Landsat satellite data and the QGIS platform. Cloud-free Landsat 5 and Landsat 8 satellite images were extracted and processed to extract the river mask. The centerline of the river was generated using the extracted river mask, with the support of semi-automated digitizing software (WebPlotDigitizer). Freely available QGIS was used to investigate the temporal variation of river migration. The results of the study demonstrated that, over the past three decades, both the bend curvatures and the river migration rates of the meandering bends have generally increased with time. In addition, it was found that a higher number of meandering bends could be observed in the lower (most downstream) and the middle parts of the selected river segment. The current analysis indicates that the Deduru Oya has undergone considerable changes in its curvature and migration rates.
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    PublicationOpen Access
    Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence
    (MDPI, 2022-06-10) Meddage, D. P. P; Ekanayake, I. U; Herath, S; Gobirahavan, R; Muttil, N; Rathnayake, U
    Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions