Faculty of Engineering SCOPUS2

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Open Access
A cross-category analysis of high impact low occurrence (HILO) disasters
(Elsevier Ltd, 2026-03-19) Samaraweera, U; Kulatunga, U; Dias, P
This paper explores six High Impact Low Occurrence (HILO) disasters, generating insights from five different categories associated with them, namely causes (geophysical, technological, biological, sociological), phases (preparedness, response, recovery), dimensions (socio-economics, governance, equity), sectors (health, education, infrastructure, economy) and national contexts with differing levels of economic development. The process involved the generation of a questionnaire, based on a literature review; and the subsequent analysis and discussion of the questionnaire responses made by six experts nominated by six academies of science in Asia. The findings highlight the limitations of probabilistic, frequency-based risk models for HILO disasters and instead emphasise the importance of scenario-based (worst-case) analyses; mechanisms that preserve inter‐generational knowledge, institutional continuity and community‐based early‐response networks; strengthening community resilience while ensuring equity; and making appropriate investments for increasing preparedness, if not through structural interventions, at least through sustained awareness programs and periodic drills. Theoretical contributions include arguments that institutional capacity, governance quality, and social resilience are more decisive determinants of HILO event outcomes than probabilistic risk analyses; and that effective preparedness depends more on anticipatory planning, scenario-based training and institutionalised memory rather than experiential learning; thus advancing HILO theory beyond event-centred and frequency-driven interpretations towards a more governance- and resilience-oriented understanding.
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, R.S; Wijesinghe, R.E; Herath H.M.L.P.B.; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, U; Manatunga, D.C
The 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.
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, R.S; Wijesinghe, R.E; Herath H.M.L.P.B; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, U
The 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.
Open Access
Automated design of reinforced concrete dapped-end connections using hybrid deep learning and generative AI augmentation
(Elsevier Ltd, 2026-04-15) Dharmawansha, S; Herath, S; Fernando P.L.N; Meddage D.P.P.; Rajapakse, C
Dapped-end connections, also known as half-joints or Gerber beams, are widely used yet structurally vulnerable elements in precast concrete structures due to high stress concentrations near the re-entrant corner. Therefore, a comprehensive assessment of the load-bearing capacity of dapped-end connections is important to ensure structural integrity and mitigate the risk of failure. Although prior studies have explored their behaviour through analytical and experimental methods, the application of data-driven approaches remains limited due to the availability of limited experimental data, which constrains the predictive accuracy and generalisation of Machine Learning (ML) models. This study presents a novel approach that integrates numerical simulation with Conditional Tabular Generative Adversarial Network (CTGAN)-based data augmentation to enhance prediction accuracy and model generalisation. A numerical database containing 720 results was developed, which was expanded with 680 augmented data using CTGAN. The combined dataset of 1400 instances was used to train Artificial Neural Network (ANN), Genetic Algorithm-ANN (GA-ANN), and Particle Swarm Optimisation-ANN (PSO-ANN) models. The hybrid models outperformed the standalone ANN, with GA-ANN achieving the highest accuracy (testing R2 = 0.961). The trained models were separately validated using 64 unseen experimental datasets, which shows the improved generalisation of the models through augmentation. Shapley Additive Explanations analysis reveals that the GA-ANN model predictions aligned with the principles underlying the compatibility of deformations of dapped-ends. Further, a novel ML-assisted design model was developed, which predicts multiple solutions for a given design problem, assisting in the optimisation of connection design.
Open Access
Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study
(Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, H
In palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.
Open Access
Circular Valorization of Post-Industrial Textile Waste in Thermal-Insulating Cementitious Ceiling Sheets
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02-27) Fernando, K. V; Dodangodage, C.A; Seneviratne, V.M; Jayasinghe, S.M; Dharmaratne, D.D; Gamage, G.N; Halwatura, R. H; Gunasekera U.S.W; Halwatura, R.U
The construction sector faces increasing pressure to reduce the embodied energy of building materials while valorizing industrial waste streams. This study evaluates the direct incorporation of post-industrial textile waste (100% cotton and cotton–polyester blends) in its native form to develop high-performance cementitious ceiling sheets. Composites were fabricated under a controlled hydraulic compaction pressure of 2.0 MPa, optimized to achieve matrix densification while preserving the integrity of the fibrous network. Viscoelastic recovery of the compressed fibers induced a hierarchical double-porosity architecture characterized by macro-voids and hollow fiber lumens. This microstructural evolution reduced thermal conductivity to 0.091 W/m·K, approximately 50% lower than commercial cement–fiber benchmarks—without compromising mechanical compliance. Scanning Electron Microscopy (SEM) revealed a mechanistic decoupling between water absorption and dimensional stability. Although the CP15 formulation (15 wt.% cotton–polyester) exhibited high moisture uptake (~21%), thickness swelling remained limited to 1.35%. This dimensional stability is attributed to the hydrophobic polyester framework, which bridges microcracks and constrains hygroscopic expansion within the cellulosic phase. The optimized CP15 composite achieved a Modulus of Rupture (MOR) of 8.75 MPa, exceeding ISO 8336 Category C, Class 2 requirements. Despite increased thickness, the areal density (10.84 kg/m2) remains compatible with standard gypsum-grade suspension systems, eliminating the need for structural modification. These findings establish a scalable, direct-valorization strategy for circular construction materials delivering enhanced thermal insulation and robust performance under tropical climatic conditions.
Embargo
Enhancing the effectiveness of satellite precipitation products with topographic and seasonal bias correction
(Elsevier B.V., 2026-02) Wanniarachchi, S; Sarukkalige, R; Hapuarachchi, H.A. P; Gomes, P.I.A; Rathnayake, U
Estimating precipitation distribution across large regions is crucial for understanding water availability, planning infrastructure, and forecasting flood hazards. Traditional gauge-based methods face challenges, particularly with sparse gauge networks. In response, satellite-based, near-real-time (NRT) precipitation data has gained popularity, especially in poorly gauged watersheds. However, satellite precipitation data quality is often compromised by latency, atmospheric complexities, and topographic effects, resulting in nonlinear errors. To overcome the research gap, this study introduces the Heavy Rain Peak Adjustment (HRPA) method alongside the well-established Seasonal Autoregressive Integrated Moving Average (SARIMA) model for satellite precipitation bias correction. The analysis utilised Global Satellite Mapping of Precipitation (GSMaP-NRT) data and hourly precipitation records from 31 rain gauges in the Ovens River region of Australia. On average, the mean residual of observed and GSMaP-NRT precipitation was −0.02 mm. Additionally, the HRPA method yielded better linear regression R2(0.911), NSE (log) (−0.847), and RMSE (0.628) compared to SARIMA. The results indicate that HRPA outperforms SARIMA, particularly at lower elevations, whereas SARIMA struggles at higher elevations, underscoring its limitations in those areas. Additionally, autocorrelation and partial autocorrelation plots for some stations in hilly areas show significant wave-like patterns, indicating greater uncertainty in satellite precipitation estimates over complex terrain. For several stations, autocorrelations at 24 and 48-hour lags suggest a systematic influence of past residuals on future ones, emphasizing the need for further refinement in satellite precipitation correction methods for these regions.
Embargo
Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study
(Taylor and Francis Ltd., 2026) Fernando, W. J; Silva, N; Perera, C
Timely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.
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Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study
(Taylor and Francis Ltd., 2026-02-18) Fernando, W. J; Silva, N; Perera, C
Timely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.
Embargo
Evaluating and prioritizing quality culture elements in the tire manufacturing industry: A case-based DEMATEL approach
(Taylor and Francis Ltd., 2026-01-16) Silva, N; Bandara, S; Perera, C
In today’s competitive manufacturing environment, developing a robust quality culture that supports continuous improvement and defect prevention is critical for long-term operational excellence and customer satisfaction. This study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate and prioritize the key elements shaping quality culture in a leading tire manufacturing company in Sri Lanka, offering a rare empirical application of DEMATEL in this context. Using a two-phase approach, Phase 1 involved a survey of 127 employees to identify seven critical elements, while Phase 2 analyzed expert evaluations from 16 professionals to establish causal relationships among them. The findings reveal that leadership (R + C = 14.40) and employee empowerment (R + C = 13.31) are the most influential drivers, followed by teamwork (R + C = 12.67), while focus on customer satisfaction, planning for quality, improvements and innovation, and standardized processes approach are primarily dependent elements. The built-in quality maturity framework and relative importance index were applied to assess the current implementation level of each element and identify performance gaps. The gap analysis highlights that leadership and employee empowerment are underperforming relative to expected levels, requiring immediate strategic enhancement. Managers should prioritize leadership and empowerment development to strengthen organizational quality culture and sustain competitiveness.
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Fly-Energy Ecosystem: A Game-Theoretic Hybrid SWIPT Framework for UAV-Assisted Rural Wireless Systems
(Institute of Electrical and Electronics Engineers Inc., 2026) Sooriarachchi, V.P; Jayakody, D. N.K; Muthuchidambaranathan P.
The increasing use of IoT and related solutions in rural environments brings the growing need for energy-efficient and energy-aware solutions. This paper proposes a novel Stack-elberg game-theory-assisted hybrid wireless energy harvesting approach for unmanned aerial vehicle (UAV), which incorporates SimultaneousWireless Information and Power Transfer (SWIPT) systems designed specifically for remote and rural environments with conventional wireless power transfer (WPT). A multi-UAVs, multi-user scenario is considered where UAVs collect information from ground-level users while simultaneously providing WPT to the users. The proposed framework enables sustainable operation of remote monitoring systems in rural areas where conventional power infrastructure is limited or unavailable, contributing to more resilient and energy-efficient IoT deployments in challenging environments. The simulation results show that the proposed method achieves scalable performance and significant improvements in SINR and energy harvesting efficiency.
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Infill Masonry Strut Models in Reinforced Concrete Frames: Multilevel Reliability Analyses for Predicting In-Plane Responses
(John Wiley and Sons Inc, 2026-03-10) Raheem, S; Thamboo, J; Mallikarachi, C; Wijesundara, K; Dias, P
The moment-resisting reinforced concrete (RC) frame infilled with masonry walls is a common form of construction for low- to medium-rise buildings. The importance of considering the infill masonry walls (IMW) in seismic analysis is accentuated due to the interaction between infills and the surrounding frame. Several analytical IMW models have been proposed to model IMW as equivalent diagonal struts, and the appropriateness of those models has been justified through experimental and numerical calibrations. However, the reliability of those analytical models is not well substantiated. Therefore, the reliabilities of five different analytical models have been evaluated herein using the First-Order Reliability Method (FORM). The stochastic uncertainties involved in predicting the in-plane capacities of IMW-RC frames have been incorporated in the reliability analyses. Subsequently, reliabilities of IMW models have been ascertained using experimental data sets compiled at two different scales, namely (1) single story–single bay and (2) multistory IMW-RC frames. 120 experimental data sets of single story–single bay IMW-RC frames tested under in-plane loading and three multistory IMW-RC frames tested on shake-tables were used to assess the reliabilities of IMW models. The results showed that the IMW models considered have predicted the in-plane behavior of IMW-RC frames (single or multistory) to certain levels of accuracy. The predicted reliability indices (β values) of the models vary between 1.03 and 4.13. The reliabilities differ when different aspects of the predictions are being considered, such as peak or ultimate load and drift capacities of single story–single bay frames or base shear and story drift of multistory frames. Therefore, depending on the requirement (strength- or displacement-based design), the IMW models should be selected appropriately to carry out the seismic analyses of IMW-RC buildings.
Open Access
Integration of industry 4.0 technologies to overcome lean manufacturing barriers in Sri Lanka’s apparel sector
(Emerald Publishing, 2026-02-09) Silva, N; Hettiarachchi, D. I; Perera, P; Perera, C
Purpose – 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.
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Investigation of inelastic response ratios for buildings with damping subjected to near-fault ground motions using numerical simulations and transformer-based models
(Elsevier Ltd, 2026-03-09) Konara, L; Deshika, T; Gobirahavan, R; Alahakoon, Y; Ekanayake I.U; Meddage D.P.P
Inelastic responses are used in seismic design to estimate inelastic seismic demand from known elastic demand, yet current provisions remain limited, especially when damping and displacement ductility are considered. This study investigated the inelastic displacement ratio and inelastic velocity ratio for single degree of freedom (SDOF) systems subjected to near-fault ground motions, with particular focus on the effects of fling-step and forward-directivity motions. For numerical modeling and analysis, an extensive nonlinear response history analysis (NLRHA) was conducted on SDOF systems incorporating parametric variations in dynamic characteristics of structural systems such as elastic period, displacement ductility, and viscous damping under different ground motion conditions. From numerical modeling, empirical equations are proposed to express the inelastic displacement ratio ((Formula presented) ) and inelastic velocity ratio ((Formula presented) ) using elastic period, viscous damping ratio, displacement ductility, and the type of ground motion. In parallel, neural networks are trained on a dataset of 36,456 samples using additional variables, including the predominant period of the ground motion, moment magnitude, and closest rupture distance. Neural network models achieved (Formula presented) (for (Formula presented) ) and (Formula presented) (for (Formula presented) ) for unseen data, indicating the highest accuracy. Model explanations indicated that the predictions adhere to the domain knowledge. Comparative assessments reveal that while empirical equations capture general trends for design purposes, neural network models accurately predict even minor variations in inelastic responses. These data-driven methods provide a complementary approach in predicting the inelastic response compared to empirical equations.
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Lean maturity model for the Sri Lankan construction industry: investigation of key model assessing components
(Taylor and Francis Ltd., 2026-02-27) Jayanetti J.K.D.D.T.; Perera B.A.K.S; Waidyasekara K.G.A.S.; Siriwardana, M; Ranadewa K.A.T.O
Implementing lean practices in the construction industry remains challenging, particularly due to the lack of effective mechanisms to assess lean construction maturity. Despite the presence of limited literature on lean maturity models, no model has been developed specifically for the Sri Lankan construction sector. Addressing this gap, the present study takes an initial step toward developing a Lean Construction Maturity Model tailored to the Sri Lankan context by identifying the essential components required for its assessment. Adopting a pragmatic stance, the research employed the qualitative Delphi technique, involving 73 expert interviews conducted over three iterative rounds, followed by five validation interviews. Directed Content analysis was used to extract key elements for the model. The study identified three core components necessary for assessing lean construction maturity: attributes, process areas, and indicators. Specifically, eight attributes were revealed including Production Efficiency, Waste Elimination, Quality Management, People, Customer Focus, Lean Leadership, Transparency, and Lean Philosophy. These attributes are supported by 28 process areas and 140 indicators. Together, these elements form a structured, layered framework for assessing lean maturity. The study contributes original insights by considering the cultural, economic, and institutional dynamics influencing lean implementation in Sri Lanka. While the findings establish foundational components, further research is needed to develop and validate a complete maturity model. Practically, the study enables a more systematic and locally relevant approach to lean adoption, supporting improved industry performance. Socially, it promotes resource efficiency and project success, contributing to more responsible and sustainable construction practices in the Sri Lankan context.
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Long-term recovery from the 2004 Indian ocean tsunami in two Sri Lankan east coast municipalities
(Elsevier Ltd, 2026-01) Thamboo, J; Josiah, R; Saja, A; Salah, P; Rossetto, T; Dias, P
Sri Lanka was the second most affected country after Indonesia, in the 2004 Boxing Day Indian Ocean tsunami (IOT). A study mission was therefore carried out twenty years after the 2004 IOT to assess the recovery of the affected regions, especially in the Eastern region of Sri Lanka, focusing on two of the most affected municipalities, i.e. Kalmunai and Batticaloa. The social and infrastructure characteristics of resettlements/relocations/new settlements in the affected regions, presence of critical infrastructure, preparedness and early warning systems installed have been assessed. It was observed that similar approaches have been adopted to plan the community relocation in both of these municipalities, while the significant reemergence of residential and commercial developments in the coastal stretches of Kalmunai municipality have been noted. Exposure analyses have revealed that there are still some critical infrastructure situated in the tsunami hazard zones. It can be construed that these municipalities have recovered from the physical losses incurred, and spatial planning is in place for future developments considering the tsunami risk. Challenges and opportunities from their differing geographical contexts appear to have been judiciously handled. However, shortcomings are noted in actual implementation due to various reasons, such as limited resources, availability of funding and preference of communities to live close to their original lands. Improving the resilience of infrastructure by designing against the expected tsunami hazard and multi-hazards, regular verification of the early warning systems and evacuation procedures are emphasized to mitigate the impacts from future tsunami.
Open Access
Nature-Based Urban Drainage Solutions Using Industrial Waste-Incorporated Pervious Concrete Pavements
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-03-11) Ratnapala, N; Miguntanna, Nandika; Miguntanna, Nadeeka; Rathnayake, U
Pervious concrete pavements have gained increasing attention as a sustainable stormwater management solution due to their ability to reduce runoff volume and improve water quality through infiltration. This study investigates the stormwater runoff treatment potential and performance efficiency of pervious concrete pavements incorporating industrial waste materials, namely recycled concrete aggregate (RCA), ceramic waste (C), and waste tires (T), as partial replacements for natural coarse aggregates. Concrete mixes were prepared by replacing 10%, 20%, and 30% of the coarse aggregate volume with each waste material, and the results were compared with normal pervious concrete. Stormwater runoff treatment performance was evaluated by analyzing key water quality parameters, including total suspended solids (TSSs), pH, turbidity, color, and electrical conductivity (EC), using collected urban runoff samples. In addition, mechanical properties (compressive, tensile, and flexural strength) and hydraulic properties (porosity and infiltration rate) were assessed to ensure structural and functional suitability. The results demonstrate that pervious concrete pavements incorporating industrial waste materials exhibit effective pollutant removal while maintaining acceptable mechanical performance in accordance with ASTM standards. Among the investigated pervious concrete types, pavements containing 10% recycled concrete aggregate and 10% ceramic waste showed superior reductions in TSS, turbidity, and color compared to other waste-based and normal pervious concrete mixes. This study demonstrated significant reductions in particulate pollutants (TSS, turbidity, and color), while increases in pH and electrical conductivity highlighted early-age ion leaching from the concrete matrix, underscoring both the treatment benefits and the need for long-term monitoring under realistic deployment conditions. Overall, the findings highlight the potential of industrial waste-based pervious concrete pavements as an environmentally sustainable and effective solution for urban stormwater management.
Open Access
QPred: A Lightweight Deep Learning-Based Web Pipeline for Accessible and Scalable Streamflow Forecasting
(Tech Science Press, 2026) Makumbura, R.K; Wijesundara, H; Sajindra, H; Rathnayake, U; Kumar, V; Duraibabu, D; Sen, S
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. Copyright
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Receiver-Centric Waveform Design: A New Frontier in SWIPT
(Institute of Electrical and Electronics Engineers Inc., 2026-01-15) Vithanage, G. S; Jayakody, D. N.K; Krikidis, I
In this work a receiver-centric waveform design technique for simultaneous wireless information and power transfer (SWIPT) is proposed, eliminating the traditional trade-off between energy harvesting (EH) efficiency and information transfer (IT) integrity. By injecting pulses into the receiver, the peak-to-average power ratio (PAPR) of the received signal is increased, using diode nonlinearity to enhance EH without affecting IT. Particle swarm optimization (PSO) is used to tune the pulse parameters to obtain the maximum harvest power under practical constraints. The Monte Carlo simulation results demonstrate superior EH performance compared to existing waveform optimization schemes. The method remains robust under common IT optimizations, such as selective mapping (SLM) and partial transmit sequence (PTS), confirming its compatibility and scalability for real-world SWIPT systems.
Open Access
Self-starting characteristics and dynamic response of a free-spinning cross-flow air turbine for oscillating water columns under irregular wave conditions
(Elsevier Ltd, 2026-02-24) Baddegamage B.H.B.P.D; Bae, S.J; Gunawardane S.D.G.S.P.; Lee, Y.H; Kim, K; Yoon, M
The cross-flow air turbine (CFAT) has been proposed as a self-rectifying device for oscillating water column (OWC) wave energy converters as an alternative to conventional Wells and impulse turbines. While previous studies have primarily focused on steady or regular flow conditions, the self-starting behavior and transient response of a free-spinning CFAT under irregular, bidirectional inflow representative of realistic sea states have not yet been investigated. This study presents a fully transient computational fluid dynamics analysis of a free-spinning CFAT operating under irregular airflow conditions derived from the JONSWAP spectrum. The simulations were performed under no-load conditions to isolate the intrinsic aerodynamic torque generation and evaluate self-starting capability. The effects of significant wave height and spectral peak period on turbine startup and unsteady aerodynamic response were systematically examined in both the time and frequency domains. The CFAT consistently initiates rotation without external assistance and reaches quasi-steady operation within 25–30 oscillation cycles. For significant wave heights ranging from 0.0375 m to 0.05 m, the mean instantaneous efficiency varies between 0.24 and 0.52, while efficiencies between 0.30 and 0.59 are obtained for spectral peak periods from 1.50 s to 1.88 s. Furthermore, wave-grouping effects play a decisive role in accelerating the turbine toward its equilibrium speed. Torque and pressure fluctuations closely follow the inflow velocity profile, with hysteresis-like behavior observed during flow reversals. These findings confirm the CFAT's suitability for practical OWC applications, demonstrating robust self-starting and stable performance under irregular conditions.