Faculty of Engineering
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Publication Embargo Spatial distribution and hydrological influences of microplastic contamination in the upstream reach of the ambathale water treatment plant intake, Sri Lanka(Springer Science and Business Media, 2026-08-07) Rathnayake, D; Miguntanna, N; Rathnayake, UMicroplastic contamination has emerged as a global environmental concern, with rivers serving as important pathways for the accumulation and transport of these particles. This study investigated the spatial distribution of microplastic contamination and the influence of hydrological and physicochemical factors on microplastic occurrence in the upstream reach of the Ambathale Water Treatment Plant intake along the Kelani River, Sri Lanka. Water samples were collected from four locations within the study area, including three upstream sites and one downstream site. Microplastics were detected at all sampling locations, with Kelaniya recorded the highest microplastic concentration (20.08 ± 10.19 mg/L), while Ambathale had the lowest (9.12 ± 3.89 mg/L). Three main microplastic types were identified: fibers, fragments, and films. Fibers and fragments were the most abundant microplastic types across all sampling locations, while films occurred at comparatively lower concentrations. Statistical analyses revealed a significant positive correlation between river water level and microplastic concentration (r = 0.883, p = 0.001), indicating the importance of hydrological conditions in microplastic transport. Fiber and film concentrations exhibited particularly strong positive relationships with water-level fluctuations (r = 0.940 and r = 0.904, respectively; p < 0.01). Furthermore, positive associations between microplastic abundance and suspended solids suggest that particulate matter plays an important role in the transport and distribution of microplastics within the river system. The findings demonstrate significant spatial variability in microplastic contamination and highlight the influence of hydrological processes on microplastic occurrence and transport in the Kelani River. This study provides baseline information for future monitoring and contributes to a better understanding of microplastic dynamics in tropical freshwater environments.Publication Open Access CALCOM: An integrated techno-economic and life-cycle environmental framework for electric vehicle assessment in Sri Lanka(Elsevier B.V., 2026-08-01) Abeygunawardena, N; Wijayapala, A; Jathunga, TElectric vehicle (EV) adoption is accelerating worldwide, creating a need for comprehensive frameworks that assess economic feasibility and environmental performance. This study presents a context-adaptive levelized cost of mileage (CALCOM) framework to evaluate the economic and environmental performance of battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and internal combustion engine vehicles (ICEVs) under Sri Lankan conditions. The framework integrates discounted life-cycle cost (LCC), net present value (NPV), and greenhouse gas (GHG) emissions into a unified assessment model. Real-world operational ad cost data were collected from owners representing Nissan Leaf (BEV), Toyota Aqua (HEV), and Toyota Vitz (ICEV). The analysis included purchase cost, energy consumption, maintenance, battery replacement, salvage value, and environmental costs over a 10-year ownership period. The BEV achieved the lowest levelized cost of mileage of 0.080 USD/km which further decreased to 0.050 USD/km under renewable charging. Life-cycle GHG emissions were 59% lower than those of ICEV. Sensitivity analysis identified electricity price, annual distance travelled, and charging efficiency as the primary determinants of BEV competitiveness. Threshold analysis indicated that BEVs remain economically attractive when domestic electricity tariffs are maintained below 0.32 USD/kWh. The findings demonstrate that BEVs offer the greatest economic and environmental benefits and offer evidence-based guidance for policies supporting electric mobility in developing countries.Publication Embargo Fusion of Lean Six Sigma-DMAIC Tools and Techniques into Construction Variation Management: Barriers and Strategies(American Society of Civil Engineers (ASCE), 2026-11-01) Shashini, T.W.K.N; Jayanetti, J.K.D.D.T; Disaratna, V; Ranadewa, T; Perera, B.A.K.SVariations in construction projects pose a significant challenge to achieving successful project outcomes. Lean Six Sigma has the potential to manage variations in the construction industry. This study aims to explore the fusion of lean Six Sigma (LSS)-Define, Measure, Analyze, Improve, and Control (DMAIC) tools and techniques into construction practice to manage variations. Adopting a pragmatic stance and a qualitative approach, empirical data were collected through a three-round Delphi expert survey using semistructured interviews, with the data analyzed through content analysis. The findings reveal that 25 LSS-DMAIC techniques and tools can be effectively applied to manage variations in the construction sector. In addition, 17 barriers and 21 strategies were identified, and a mapping was undertaken to align each barrier with suitable strategies. This research addresses a gap in existing literature by extending investigations of LSS-DMAIC in construction to the specific context of managing variations, identifying barriers to implementation, and proposing strategies, an area that has not been explicitly examined previously. By investigating the barriers to implementing LSS-DMAIC and by suggesting tailor-made strategies for overcoming each barrier, the study makes both theoretical and practical contributions. It provides a framework for on the applicability of LSS-DMAIC in developing countries while offering construction managers context-specific solutions to enhance project outcomes and improve stakeholder satisfaction.Publication Open Access Performance Enhancement of Cooperative NOMA in Satellite Communication Through Combining Techniques(Institute of Electrical and Electronics Engineers Inc., 2026) Ashwini K; Singh, A; Jagadeesh V.K; Shenoy, R; Jayakody, D. N.KThis article studies the analysis of the key performance parameters in the proposed cooperative Non-Orthogonal Multiple Access (NOMA) system considering the satellite network. NOMA can be coupled with standard relaying procedures to increase the overall capabilities of the wireless communication system. The system under consideration consists of two users, one near and a far user, and a satellite. The paper explores by using a Decode and Forward (DF) relaying mechanism at the user closer to the satellite, which will forward the signal received from the satellite user that is distant from it. In separate case studies, the far user combines the direct and indirect transmission signals using Maximal Ratio Combining (MRC) and Selection Combining (SC) techniques. The expressions for outage, average Bit Error Rate (BER), and capacity are derived for MRC and SC methods. It is also derived and approximated, and closed-form expressions are obtained. The simulation results of these analytical expressions reveal that the MRC method of combining outperforms the SC technique in terms of outage, BER, and ergodic capacity.Publication Embargo Size-dependent nonlinear vibration problem of piezoelectric graphene origami auxetic metamaterial sandwich microplates under coupled thermo-fluid-viscoelastic multi-physics(Taylor and Francis Ltd., 2026-07-04) Saffari, P.R; Senjuntichai, T; Rajapakse, NThis paper investigates the nonlinear dynamic behavior of a microplate combining functionally graded graphene origami-enabled auxetic metamaterials (FG-GOEAM) in sandwich architecture with piezoelectric layers on a viscoelastic substrate under fluid-structure interaction and thermal loading. Accordingly, the primary objective is to develop a unified analytical framework to model and predict the intricate, size-dependent nonlinear dynamics of this multi-physics system. Graded graphene origami (GOri) elements are dispersed through the plate thickness to achieve negative Poisson’s ratio and improved thermal conductivity. First-order shear deformation theory (FSDT) with von Kármán geometric nonlinearity models large-amplitude deflections, while modified couple stress theory (MCST) captures size dependency. Thermal effects include uniform, linear, and nonlinear temperature distributions, and fluid-plate interaction is modeled via Navier-Stokes equations. Hamilton’s principle derives the governing equations, discretized using the Galerkin method into nonlinear time-dependent ordinary differential equations. The harmonic balance technique solves these equations to obtain nonlinear frequency-amplitude relationships for forced vibration. Effects of strain-gradient length-scale parameter, thermal field properties, piezoelectric actuation voltage, fluid layer depth, viscoelastic foundation stiffness and damping, GOri content, folding, and distribution patterns are parametrically examined using nonlinear frequency response curves.Publication Open Access QPred: A Lightweight Deep Learning-Based Web Pipeline for Accessible and Scalable Streamflow Forecasting(Tech Science Press, 2026-03-12) Makumbura, Randika K.; Wijesundara, H; Sajindra, H; Rathnayake, U; Kumar, V; Duraibabu, D; Sen, SumitAccurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges and loggers, while river water depth in the Aglar and Paligaad watersheds was converted to discharge using site-specific rating curves, resulting in a daily dataset of precipitation, river water level and discharge. Four DL architectures were trained, including vanilla Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), and evaluated using Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Root-Mean-Square-Error-Standard-Deviation Ratio (RSR), and Percentage Bias (PBIAS) metrics. Performance was watershed-specific, as the vanilla LSTM demonstrated the best generalisation for the Aglar watershed (R2 = 0.88, NSE = 0.82, RMSE = 0.12 during validation), while the GRU achieved the highest validation accuracy in Paligaad (R2 = 0.88, NSE = 0.88, RMSE = 0.49). All models achieved satisfactory to excellent performance during calibration (R2 > 0.91, NSE > 0.91 for both watersheds), demonstrating strong capability to capture streamflow dynamics. The highest performing models were selected and embedded into the QPred application. QPred was developed as a lightweight web pipeline, utilising Google Colab as the primary execution environment, Flask as the backend inference framework, Google Drive for artefact storage, and Ngrok for secure HTTPS tunnelling. A user-friendly front end utilises range sliders (bounded by observed minima and maxima) to gather inputs and provides discharge data along with metadata, thereby enhancing transparency. This work demonstrates that accurate, context-aware deep learning models can be delivered through low-cost, web-based platforms, providing a reproducible and scalable pipeline for hydrological applications in other watersheds and for practitioners. CPublication Open Access Adaptive Path Planning for Mobile Robots Using a Hybrid PRM–GA Optimization Approach(John Wiley and Sons Ltd, 2026-04-10) Jathunga, T; Rajapaksha, S; Jayasinghe, S; Abeygunawardena, NThis study addresses the challenge of path planning in mobile robots, that requires efficient navigation in complex environments. Traditional approaches often struggle to meet the increasing demands of modern multi-robot systems operating in dynamic environments. To address these limitations, this study proposes an improved path planning technique by combining the probabilistic roadmap (PRM) with the genetic algorithm (GA), forming a hybrid PRM–GA approach designed to optimize the routes of mobile robots. Experiments were carried out for scenarios involving 2, 3, and 9 robots to analyze the performance of the proposed method under increasing complexity. The proposed PRM–GA method was compared with widely used path planning algorithms including (Formula presented.), Rapidly exploring random tree (RRT), and conventional PRM. Performance of each method was evaluated focusing on path efficiency and energy consumption. The enhanced fitness function within the GA evaluates robot paths based not only on distance but also on smoothness and turn count, promoting routes with fewer directional changes. The proposed PRM–GA method reduces robot energy consumption while improving navigation efficiency. Experimental results demonstrate that the PRM–GA hybrid method outperforms (Formula presented.), RRT, and PRM by encouraging smoother paths with fewer turns, thereby enhancing the operational efficiency of multi-robot systems. The effectiveness of the proposed approach highlights its potential for practical applications in sectors where efficient mobile robot navigation is essential.genetic algorithmPublication Open Access Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills(Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, Rohan S; Wijesinghe, R, E; Herath H.M.L.P.B; Nalin de Silva K.M.; de Silva, Rohini M; Rajapaksha, Suranga M; Wijenayake, U; Manatunga, Danushika CThe existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.Publication Open Access PV-Assisted Charging for Electric Three-Wheelers in Sri Lanka: A Comparative TCO and Sensitivity Analysis(Algerian Centre for the Development of Renewable Energy, 2026-05-24) Abeygunawardena, Nuwanthi; Jathunga, T; Rodrigo, MThis research explores the potential of solar-powered electric three-wheelers as a sustainable and cost-effective transportation solution in Sri Lanka. Traditional three-wheelers contribute to air pollution and fuel dependency, while electric three-wheelers offer a cleaner alternative. By integrating solar power, these vehicles can further reduce emissions and operational costs. The study examines the economic benefits of three types of three-wheelers, covering fuel-based, electric, and solar-powered electric three-wheeler categories. Survey-based findings are used to calculate the total cost of ownership of tuk-tuks. Fuel and electricity costs, operational and maintenance costs, and more parameters are incorporated for the study. The findings suggest that solar-powered electric three-wheelers offer a promising solution for sustainable transportation solutions for the Sri Lankan context.Publication Open Access Nature-Based Solutions: Evaluation of Natural Plant-Derived Coagulants for Sustainable Water Treatment(Multidisciplinary Digital Publishing Institute (MDPI), 2026-06) Perera, N; Miguntanna, Nadeeka; Miguntanna, Nandika; Rathnayake, UThis study evaluates the performance of natural plant-derived coagulants as sustainable alternatives to conventional chemical coagulants in water treatment. Surface water samples were collected from the Meda Ela stream in Karadiyana, Sri Lanka, which is an urban water body impacted by leachate from the Karadiyana dumpsite, industrial discharges, and urban runoff. Grab samples were analyzed for key water quality parameters, including pH, conductivity, turbidity, dissolved oxygen (DO), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), settleable solids, total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), total nitrogen, and total phosphorus. Several parameters exceeded permissible standards established by the Central Environmental Authority (CEA) of Sri Lanka, including turbidity (35 NTU; limit: 20 NTU), COD (80 mg/L; limit: 15 mg/L), TDS (1000 mg/L; limit: 500 mg/L), and TSS (100 mg/L; limit: 40 mg/L), indicating significant pollution levels. Jar test experiments were conducted to compare the coagulation efficiency of cowpea seeds (75.8%), fenugreek seeds (69.2%), papaya seeds (72.5%), okra pods (84.6%), and Moringa oleifera (drumstick) leaves (87%) with conventional alum (94.2%) at an optimum dosage of 12 mL/L. Among the tested plant-derived coagulants, Moringa oleifera leaves demonstrated the highest turbidity removal efficiency, reducing residual turbidity to 4.54 NTU. A low-cost integrated treatment system incorporating coagulation, flocculation, sedimentation, and filtration using sawdust and cotton wool was developed, achieving average removal efficiencies of 90.13% for turbidity, 88.57% for COD, 83.46% for TDS, and 74.83% for TSS, with all effluent parameters maintained within CEA permissible limits. The results confirm that locally available plant-derived coagulants, particularly Moringa oleifera leaves, offer an effective, environmentally friendly, and economically viable approach for sustainable water treatment, highlighting the potential of nature-based solutions in strengthening climate-resilient water management strategies.