Faculty of Engineering Q1
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/5285
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Publication Embargo 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. MThis 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.Publication Embargo 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.COcular 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.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 Embargo Improving Pipe Degradation Modeling Using Artificial Intelligence Models with Age Plus Other Variables(American Society of Civil Engineers (ASCE), 2026-10-01) Salman F.M; Micevski, T; Dias W.P.SThis paper uses data on stormwater pipes from 21 distinct ages ranging from 3 to 110 years, the degradation of which had been originally modeled using a Markov scheme (using only age as an explanatory variable); in order to establish whether artificial intelligence (AI) models can improve the fitting and prediction of degradation levels. The AI models comprise (1) an artificial neural network (ANN) with only age as a variable; (2) another ANN model with pipe material, pipe diameter, exposure condition, and soil type as variables in addition to age; and (3) a random forest (RF) model with the same five variables. The multiparameter models, especially the RF one, were found to perform much better (e.g., R2 value of 0.98) than those using only age as the independent variable (with R2 values around 0.24). The RF model also showed better performance with respect to time stationarity. Pipe material was found to be an explanatory variable almost as dominant as age, with concrete pipes deteriorating much less than vitreous clay ones. The fluctuation of the observed overall damage rating with age was reflected in a similar pattern of fluctuation for the proportion of clay pipes in the samples obtained at the different ages.Publication Open Access Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction(Elsevier Ltd, 2026-10-01) Perera, M; De Hoog, J; Bandara, K; Weeratunge, H; Halgamuge, SAccurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with attention that focus on important regions of an image have been applied with success in many computer vision applications. However, whether such methods provide meaningful benefits for cloud movement forecasting, and how such improvements propagate through to downstream solar generation forecasting accuracy, remains under-explored. In this study, we conduct a large-scale empirical investigation of the impact of attention-based cloud forecasting on solar generation forecasting, addressing a gap that has been overlooked in the literature. To this end, we develop a pipeline that incorporates an attention-enhanced convolutional long short-term memory network and an existing self-attention-based video prediction method to forecast cloud movement using satellite imagery. The effectiveness of the resulting cloud forecasts is evaluated through their downstream impact on solar forecasting across 50 PV sites in Australia. We further provide insights into the cloud conditions under which attention-based cloud forecasting methods yield the most significant improvements in downstream solar forecasting accuracy. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.Publication Open Access Integration of industry 4.0 technologies to overcome lean manufacturing barriers in Sri Lanka’s apparel sector(Emerald Publishing, 2026-12-14) Silva, Niranga; Hettiarachchi, D. I; Perera, P; Perera, Curpose – This study aims to examine how Industry 4.0 (I4.0) technologies can enable Lean Manufacturing (LM) practices in Sri Lanka’s apparel industry. Although LM has been widely adopted to improve efficiency and reduce waste, persistent barriers such as frequent product changes, limited real-time visibility and infrastructural constraints have restricted its full potential. The purpose of this research is to explore how advanced digital solutions, including Internet of Things (IoT), real-time analytics and augmented/virtual reality (AR/VR), can address these barriers and enhance the competitiveness and sustainability of apparel manufacturing in a dynamic global market. Design/methodology/approach – A qualitative single-case study design was used to provide an in-depth understanding of digital–lean integration. The research was conducted in collaboration with a leading Sri Lankan apparel manufacturer. Data were collected through on-site factory observations, semi-structured interviews with managers and employees and examination of company records. Using Yin’s (2018) case study methodology as a guiding framework, the study analyzed how selected I4.0 technologies were implemented alongside lean tools and how these interventions addressed identified operational inefficiencies. Findings – The study found that I4.0-enabled solutions significantly enhanced lean practices by improving production workflow transparency, defect detection and downtime reduction. Tools such as IoT-linked dashboards, electronic Kanban systems and automated performance monitoring minimized non-value-adding activities and reduced bottlenecks. AR/VR applications demonstrated potential for training and machine setup, while predictive maintenance improved equipment reliability. However, the research also identified persistent shortcomings, including data confidentiality issues, workforce adaptability challenges and high capital investment requirements. The findings highlight both the opportunities and practical limitations of integrating digital technologies into lean environments. Research limitations/implications – The research was limited to a single case study of a large apparel manufacturer in Sri Lanka, which constrains the generalizability of findings. Data confidentiality policies restricted access to detailed financial information, preventing quantitative analysis of productivity gains and return on investment. Future studies could extend this research by including multiple firms across varying scales and geographies, enabling comparative insights. Broader empirical studies that quantify the financial outcomes of digital–lean integration would provide further validation and support for industry-wide adoption. Practical implications – For practitioners, the study offers a roadmap for integrating I4.0 technologies with lean practices in apparel manufacturing. The evidence suggests that digital lean tools can enhance transparency, improve workflow efficiency and support more accurate decision-making. Managers should prioritize investments in IoT-enabled monitoring, predictive maintenance and digital visual management systems while addressing workforce readiness through training programs. Attention must also be given to cybersecurity and change management to ensure sustainable implementation. These findings are particularly relevant for resource-constrained firms seeking to maximize operational efficiency while navigating global competitive pressures. Social implications – The integration of I4.0 and LM in Sri Lanka’s apparel sector holds broader social benefits by safeguarding employment in a critical export industry that provides livelihoods for over 300, 000 workers. Enhanced productivity and competitiveness contribute to economic stability and foreign exchange earnings. Moreover, digital lean practices can reduce waste, contributing to environmental sustainability and aligning with global sustainable development goals. By strengthening the resilience of the apparel sector, these advancements can help sustain jobs and improve working conditions, particularly in developing country contexts where apparel remains a cornerstone of industrial growth. Originality/value – This study provides one of the first in-depth examinations of how I4.0 technologies can act as enablers of LM in the Sri Lankan apparel industry. Unlike prior studies that treat lean and digital transformation as separate trajectories, this research highlights their synergies and tradeoffs in practice. By capturing both the benefits and shortcomings of digital lean tools, the paper contributes to theory by extending understanding of lean–I4.0 integration in emerging economy contexts. It also offers practical value by providing industry-specific insights that can inform managers’ strategic decisions on digital transformation.Publication Open Access Influence of ageing of graphene oxide on the properties and morphology of cement mortar(Nature Research, 2025-12-02) Suganthiny,G; Thambiliyagodage, C; Perera, S. V. T. J; Rajapakse, R. K. N. DPast studies show that Graphene Oxide (GO) enhances the structural properties of cement composites. However, GO reduces its chemical characteristics with ageing. This study determines the effects of the age of commercial and laboratory-produced GO on cementitious composites. The study considered GO of up to 35 weeks of age, and specimens were chemically characterised using various techniques. The ageing effects were evaluated using consistency, initial setting time, compressive strength, splitting tensile strength, and water absorption. The composite’s thermal resistance was also tested. GO was found to have a shelf life of 13 weeks from production to achieve favourable results. The morphology of the cement mortar was studied to determine the reason for the change in performance with GO age. This study confirms that the carbon-to-oxygen ratio (C/O) and the disorder of graphene oxide sheets (ID/IG ratio), along with the number of GO layers, govern the performance of GO-incorporated cement composites. Both ratios increase with GO age. Aged GOs in mortar increased the mean pore radius and reduced the surface area. Mortar samples with aged GOs have ettringite peaks, while early-age GO-containing samples lack ettringite peaks. Despite reduced mechanical performance with age, all mortar samples remained thermally stable at higher temperatures.