Research Papers - Department of Civil Engineering
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/598
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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 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.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 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, UPervious 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.Publication Embargo 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, PThe 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.Publication 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.UThe 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.Publication Open Access A cross-category analysis of high impact low occurrence (HILO) disasters(Elsevier Ltd, 2026-03-19) Samaraweera, U; Kulatunga, U; Dias, PThis 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.Publication 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, CDapped-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.Publication Embargo 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.PInelastic 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.