Institutional-Repository, University of Moratuwa
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Recent Submissions
item: Thesis-Full-text
Evaluating non-structural interventions for dam safety : insights from hydraulic simulation, stakeholder perspectives, and community-focused prototypes
(2025) Rahubadda, RVAD; Kulatunga, U; Thayaparan, M; Dassanayake, S; De Silva, C
Dam failures pose severe threats to both human life and socio-economic stability, often resulting in catastrophic losses. This study sets out to critically examine the socio-economic impacts of dam failures and to investigate innovative non-structural measures for risk reduction, with a focus on enhancing community preparedness. A mixed-methods approach underpinned by a pragmatist philosophy was adopted, integrating narrative and systematic literature reviews, hydraulic modelling, GIS-based analysis, agent-based modelling (ABM), stakeholder interviews, and gamification. Literature reviews synthesized global evidence on dam failure impacts and identified gaps in existing preparedness strategies. A case study of the Kantale Dam in Sri Lanka was conducted using 2D HEC-RAS modelling to simulate potential dam-break scenarios, producing detailed inundation maps and hazard parameters. These outputs were combined with socio-economic data and validated through semi-structured interviews, which provided insights into vulnerabilities and adaptive capacities of local communities. To translate complex risk information into accessible and participatory formats, the study developed a serious board game as a gamified non-structural intervention. Iteratively refined through pilot studies, the game successfully engaged participants, improved their knowledge of dam-related challenges, and fostered collaborative decision-making. The findings confirm that integrating technical modelling with community-based educational tools significantly enhances disaster preparedness. The research contributes to knowledge by advancing hybrid methodologies, demonstrating the value of ABM in simulating human behavior under disaster conditions, and pioneering a gamified approach to dam safety education. These contributions provide a replicable framework for policymakers and practitioners to strengthen dam safety management and reduce disaster risk in vulnerable regions.
item: Thesis-Full-text
Assessing the efficacy of physics-informed neural networks for enhanced flood routing simulation
(2025) Jayawardane, JMPM; Rajapakse, RLHL
Flooding has emerged as one of the most destructive natural hazards, causing extensive damage to infrastructure, livelihoods, and ecosystems. The intensifying impacts of climate change, particularly altered rainfall patterns and increased frequency of extreme precipitation events, have further amplified the severity and recurrence of both riverine and localized urban flooding. These conditions often overwhelm existing channel capacities, highlighting the need for accurate flood modeling to support critical decisions related to urban resilience and disaster preparedness. While traditional hydrodynamic models such as HEC-RAS remain widely used for flood simulation, the recent advancement of data-driven methods, particularly Machine Learning (ML), offers promising alternatives. This study explores the application of Physics-Informed Neural Networks (PINNs) as a novel approach for 1D flood routing and peak flow prediction within urban canal networks, especially in data-scarce environments. The PINN framework integrates physical laws, as partial differential equations (PDEs), directly into the model, enhancing the ability to address data scarcities. The 1D Saint-Venant equations were used as the guiding PDE within this particular model. Observed water level data, canal cross sections and rainfall data from the Urban Water Management Division of the Sri Lanka Land Development Corporation (SLLDC) were used as model inputs. The methodology was implemented on three major canal systems in Colombo, Sri Lanka: Madiwela East Diversion, Dehiwala-Wellawatta-Kirulapone Canal System, and Parliament Lake- Kiththampahuwa-Kinda-Dematagoda Canal System. The performance of the PINN model was evaluated by comparing its predicted water levels against observed data at multiple water level monitoring stations along the mentioned canals, using Nash- Sutcliffe-Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. In parallel, HEC-RAS 1D hydraulic models were developed for each canal system using the same boundary and terrain data. These models served as a benchmark for comparison with the PINN approach. The results revealed that the PINN model consistently achieved higher accuracy, outperforming HEC-RAS at intermediate water level validation locations. PINN model predictions exhibited NSE values exceeding 0.98 at training locations and above 0.95 at validation points, with corresponding RMSE values below 0.10 m (10 cm). In comparison, while HEC-RAS also demonstrated acceptable performance with NSE values above 0.90 in most cases, its RMSE values were comparatively higher, ranging from 0.10-0.17 m in some locations. These findings highlight higher accuracy of the PINN model, particularly in predicting flood peaks and spatial water level profiles. The integration of physics-based constraints with machine learning allows PINNs to outperform traditional models like HEC-RAS, especially in urban flood scenarios with sparse data availability.
item: Thesis-Full-text
Integrating remote sensing and unsupervised machine learning techniques for runoff prediction in ungauged catchments through spatiotemporal catchment similarity analysis
(2025) Madhumal, PVRP; Wijayaratna, TMN
Predicting streamflow in ungauged basins is a significant challenge in hydrology. This study presents a framework that integrates remote sensing and machine learning to predict runoff for ungauged catchments by analyzing catchment similarity and evaluating whether training on hydrologically similar catchments outperforms training on all available streamflow and precipitation data across catchments. The study analyzed 30 catchments across Sri Lanka's wet, intermediate, and dry zones using two decades of streamflow and precipitation data. An initial suite of 14 hydrological signatures derived from daily river discharge and rainfall time series was refined via correlation analysis (|r| ≥ 0.7) into six non-redundant signatures (Runoff Ratio, Baseflow Index, Flow Duration Curve Slope, Recession Constant, Precipitation Elasticity, Rising Limb Density) that together represent the complete unit hydrograph. For classification, multiple algorithms (K- Means, K-Medoids, Hierarchical) and distance metrics (Euclidean, Manhattan, Cosine) were evaluated. Hierarchical clustering with cosine distance proved optimal, forming seven distinct clusters with a high Silhouette Score of 0.525. Additional validation using satellite-derived data confirmed strong hydrological coherence (Hydrological Coherence Index = 1.48). For ungauged applications, signatures were predicted from satellite data, achieving a pairwise accuracy of 87.4% in replicating cluster relationships, although exact membership accuracy was lower (60%) due to borderline catchments. The prediction compared three approaches for pseudo-ungauged basins: Monaragala (dry zone) and Ellagawa (wet zone). In both the Dry and Wet zones, the traditional HEC-Hydrologic Modeling System parameter transfer failed to deliver sufficient predictions (Monaragala: Nash-Sutcliffe Efficiency (NSE) = -0.407; Ellagawa: NSE = 0.425). Long Short- Term Memory (LSTM) models were universally superior, but the optimal strategy was regime dependent. In the Wet Zone, the Regionalized LSTM (trained on similar donors) performed best (Ellagawa: NSE = 0.709, Root Mean Square Error (RMSE) = 4.20 mm/day). Conversely, in the Dry Zone, the Global LSTM (trained on all 30 catchments) was more effective (Monaragala: NSE = 0.535, RMSE = 2.88 mm/day), suggesting that data volume aids learning of complex arid hydrology, though low-flow prediction remained a challenge for all models. The findings mandate a regime-specific prediction strategy: hydrological similarity guides optimal donor selection in wet zones, while a global data approach is more effective in dry zones.
item: Thesis-Full-text
Investigating the effect of under sleeper pads and ballast mats in attenuation of train induced ground vibrations
(2025) Marasinghe, MMGC; Damruwan, HGH; Lewangamage, CS
First and foremost, I extend my heartfelt gratitude to my principal supervisor, Dr. H.G.H. Damruwan, from the Department of Civil Engineering at the University of Moratuwa, for his steadfast guidance, encouragement, and insightful mentorship throughout this research. His expertise, patience, and unwavering support have been pivotal in shaping the direction and quality of this thesis. I also wish to sincerely thank my co-supervisor, Prof. C. S. Lewangamage, for providing valuable guidance on various aspects throughout this study. I am grateful to the technical officer and supporting staff at Structural Dynamics and Health Monitoring Lav for their assistance during the field studies. I also wish to sincerely thank the Progress Review Committee, especially Dr. Lakshitha Fernando, for his critical feedback, thoughtful suggestions, and consistent support, which helped to enhance the depth and clarity of this work. I am also thankful to the academic staff of the Department of Civil Engineering, University of Moratuwa, including Prof. H.R. Pasindu, the MSc Research Coordinator, and especially the academic staff members of the Structural Division, for their continued academic guidance and encouragement throughout the MSc program. To my fellow researchers and colleagues, thank you for your collaboration, encouragement, and knowledge-sharing that enriched this journey and made it a truly collective effort. Finally, I am deeply grateful to my family and friends for their constant motivation, patience, and unwavering belief in me. Their love and emotional strength carried me through every challenge I faced along this path.
item: Thesis-Abstract
Energy efficiency of waste-based masonry materials
(2025) Thoradeniya, BRWMD; Jayasinghe, C; Ariyaratne, KPIE
The construction industry has been recognised as a major contributor to several environmental challenges, mainly due to rapid urbanisation and economic growth that have driven a substantial increase in housing demand. This demand has heavily relied on energy-intensive masonry materials, including cement sand blocks, and fired clay bricks, typically manufactured using depleting natural resources. Consequently, industrial growth often accompanies economic development, resulting in vast quantities of waste, much of which is disposed of in landfills, further exacerbating environmental concerns. In this context, applying circular economy principles to industrial waste by repurposing it into building materials can optimise the use of scarce natural resources and reduce the embodied energy involved in manufacturing processes. To address this, the energy efficiency of two waste-based masonry products, including autoclaved aerated concrete (AAC) blocks and expanded polystyrene (EPS) blocks, in comparison with conventional cement sand blocks, has been evaluated, which are readily available in Sri Lanka. Embodied energy was systematically quantified through a process-based analysis including raw material extraction, transportation, and manufacturing. Operational energy was compared using thermal simulations of a single-storey residential building, utilising empirically measured thermal properties of the materials. Although the waste-based masonry materials exhibited a comparable embodied energy to the conventional reference, the operational energy reductions observed throughout the lifespan of the buildings built using these materials demonstrated clear potential for net savings of energy. Therefore, waste- based masonry units emerged as viable solutions to reduce the total energy consumption in tropical climates and promote circular economic principles.