Faculty of Engineering

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Now showing 1 - 10 of 671
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    Investigating the mechanical and durability properties of locally sourced fly ash-based ternary cement paste to increase OPC replacement
    (Emerald Publishing, 2026-07-07) Gamage, O; Kulasuriya, C; Perera, S V T J
    The mechanical and durability performance of fly ash (FA)-based ternary blended cement pastes was investigated in an attempt to increase ordinary Portland cement (OPC) replacement while maintaining comparable strength and improved durability. This paper provides region-specific experimental data using locally sourced FA from Sri Lanka and imported ground granulated blast-furnace slag (GGBFS). A control mix (100% OPC), two binary blends and six ternary blends were tested for compressive strength at 3, 7 and 28 days, along with water absorption and resistance to sulfuric acid after 28 days. The results showed that OPC specimens achieved the highest early strength, while ternary blends with 25% and 40% OPC replacement achieved comparable 28-day strengths of 82.7 ± 4.8 MPa and 76.2 ± 3.2 MPa, respectively. GGBFS contributed to early strength gain, whereas FA enhanced long-term strength. Durability was improved significantly in the ternary blends. The F15S10 blend, with a higher GGBFS content, showed the greatest sulfuric acid resistance (4.8 ± 0.15% weight loss), while the F40S10 blend, with higher FA content, exhibited the lowest water absorption (6.0 ± 0.18%). Overall, ternary blends with 40% OPC replacement and low FA content are recommended for achieving balanced strength and superior durability in cement paste applications.
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    Joint Resource Allocation and Network Slicing in Hybrid RF/VLC Systems: A Proportional-Fairness Approach
    (Institute of Electrical and Electronics Engineers Inc., 2026-08-01) Rajahrajasingh, H; Jayakody, D. N. K.; Peha, J. M
    This article presents a joint resource allocation and network slicing framework for hybrid radio frequency (RF) and visible light communication (VLC) systems based on a proportional-fairness (PF) objective. The proposed approach formulates a convex optimization problem that jointly allocates bandwidth resources across multiple slices and access technologies to balance aggregate throughput and user fairness. A relaxed PF solution is first derived via projected gradient optimization, followed by two practical rounding strategies: a simple threshold-based method and a greedy assignment heuristic that produce integer allocations with low complexity. Simulation results for a 20 MHz hybrid RF/VLC system (10 MHz/band) demonstrate that the proposed PF-based allocation achieves aggregate sum-rates of approximately 450-500 Mb/s with Jain's fairness indices greater than 0.9, thus outperforming static and rounding baselines by 10%-15% in throughput while maintaining high fairness. The analysis is further extended to asymmetric RF/VLC bandwidth conditions, demonstrating that the proposed PF-based slicing maintains high fairness while scaling throughput effectively. These results confirm that the PF formulation effectively captures the throughput fairness tradeoff and provides near-optimal slicing performance under realistic indoor bandwidth and power constraints.
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    OcupHI: knowledge-driven colorimetric interpretation framework for high-precision real-time ocular pH diagnostics
    (Springer Nature, 2026-08-12) Kahandawala, B, S; Sandaruwan, H. H. P. B; Liyanage, P; Dassanayake, R.S; Costha, N.P; Liyanage, R.N; Wijenayake, U; Wijesinghe, R.E; Silva, B.N; Manatunga, D.C
    Ocular injuries due to chemical spills pose a substantial concern, representing 10–22% of all ocular trauma. Although precise detection of ocular pH is crucial for determining the optimal medical treatment, many existing methods remain invasive, biased, or insufficiently precise. Reliance on subjective visual assessment of subtle color differences limits the objectivity and hinders high-throughput analysis. Therefore, an advanced colorimetric knowledge-driven ocular pH detection method was developed using a biosensor (OcupHI) based on a Clitoria ternatea (Butterfly Pea) anthocyanin sensing agent. The proposed work delivers fast, high-precision, and easily measurable pH prediction across clinically relevant ranges, while supporting real-time decision support for eye physicians. The pH range from 1 to 12 was tested and compared with six different anthocyanin concentrations: 5, 10, 20, 30, 40, and 50 ppm, and five different machine learning models, namely, Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM). The results revealed that the 40 ppm anthocyanin concentration trained with the XGBoost model produced the most accurate ocular pH values, achieving superior performance with an overall accuracy of 96%, a significantly higher F1-score for early detection. Experimental validation clearly demonstrates strong predictive accuracy, robustness, and interpretability, highlighting the potential for next-generation ocular diagnostics. Further research findings support Sustainable Development Goal (SDG) 3 – good health and well-being through a real-time ocular pH monitoring kit, and SDG 12 – responsible consumption and production by optimizing the use of the natural colorant anthocyanin for sensor development.
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    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, U
    Microplastic 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.
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    PublicationOpen 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, T
    Electric 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.
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    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.S
    Variations 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.
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    PublicationOpen 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.K
    This 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.
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    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, N
    This 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.
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    PublicationOpen 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, Sumit
    Accurate 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. C
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    PublicationOpen 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, N
    This 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 algorithm