Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
item: Conference-Abstract
Optimisation of UHPFRC-strengthened square bridge piers for enhanced impact resistance
(Department of Civil Engineering, University of Moratuwa, 2025) Thulakshan, R; Fernando, PLN; Baskaran, K; Mallikarachchi, C; Damruwan, H; Fernando, L; Herath, S
The increasing vulnerability of waterway bridges to vessel collisions underscores the urgent need for intrinsic structural enhancements to improve impact resistance. Historical bridge failures such as the Sunshine Skyway Bridge (1980), I-40 Bridge (2002), and more recently, Francis Scott Key Bridge (2024) demonstrate the catastrophic consequences of ship–bridge collisions, including structural collapse, economic loss, and casualties. Conventional reinforced concrete (RC) piers, though cost-effective, exhibit brittle failure modes such as crushing, spalling, and shear cracking under high-energy impacts. Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) has emerged as a promising material due to its superior compressive strength, tensile resistance, ductility, and energy absorption capacity compared to conventional concrete. However, casting entire bridge piers with UHPFRC is not economically feasible due to its high material cost. Instead, optimised jacketing techniques that use UHPFRC strategically in critical regions have gained increasing research interest. The objective of this study is to evaluate the structural performance of square-shaped bridge piers strengthened with UHPFRC jackets and to propose strengthening configurations that maximise impact resistance with minimal material usage. In this study, a detailed numerical modelling approach was adopted using the advanced finite element software LS-DYNA. The baseline model consisted of a 3.1 m × 3.1 m square pier, 15 m in height, subjected to collision with a fully loaded Jumbo Hopper barge of mass 1,723 tons travelling at 8 knots (4.11 m/s or 14.8 km/h). The pier was designed as an RC column with typical reinforcement detailing, while nonlinear material models were defined to capture the dynamic behaviour of concrete, steel, and UHPFRC under high strain rates. The model was validated against published studies by replicating a similar barge–pier collision scenario. Next, two jacketing strategies were investigated. Scheme-1, the conventional full-ring jacketing method, involved wrapping the pier surface with continuous UHPFRC layers of varying thicknesses (200 mm, 400 mm, 600 mm) and lengths (2 m, 3.5 m, 5 m, and full height). Scheme-2, the proposed corner-only method, applied triangular UHPFRC jackets at the four corners of the pier, with side lengths of 0.75 m, 1.15 m, and 1.55 m across similar jacketing lengths. A comprehensive parametric study assessed the influence of these variables on pier displacement, stress distribution, strain energy absorption, and damage mitigation. The results showed that in Scheme-1, increasing jacket thickness improved impact resistance more effectively than increasing length, with a thickness of 400 mm yielding optimal performance. Scheme-2 demonstrated superior material efficiency, as uniform corner-only jacketing along the full pier height achieved minimal damage with a 12% usage of UHPFRC by volume. The study concludes that strategic application of UHPFRC, particularly through corner-only jacketing, provides an economical and effective solution to enhance pier resilience against barge impacts. Findings highlight that optimised geometric distribution of UHPFRC is more critical than overall volume, offering practical guidance for both retrofitting and new bridge design.
item: Conference-Abstract
Investigating the residual design resistance of steel members under pitting corrosion
(Department of Civil Engineering, University of Moratuwa, 2025) Yasantha, KHJ; Herath, HMST; Baskaran, K; Mallikarachchi, C; Damruwan, H; Fernando, FernandoL; Herath, S
Steel is a widely used material in civil engineering and construction, considering its various advantages, including strength, design flexibility, safety, cost-effectiveness, and construction efficiency. However, their long-term performance is often compromised by material deterioration mechanisms, such as pitting corrosion, which can severely impact structural integrity. The reduction in cross-sectional area, material loss, and weakening of steel sections caused by corrosion-related deterioration impact the structural integrity and load-bearing capacity of steel members. Unlike uniform corrosion, pitting corrosion is a highly localized deterioration process that produces small surface cavities in steel, which in turn causes a considerable loss of structural strength. Although extensive research has addressed the impact of pitting corrosion on the residual strength of Circular Hollow Sections (CHS), limited attention has been given to Square Hollow Sections (SHS), leaving a notable gap in current knowledge. This research investigates how pitting corrosion affects the residual design resistance of SHS members through experimental testing and numerical modelling. This study examines the influence of critical pitting parameters, including pitting intensity, location, and distribution, on the structural integrity of steel members. The experimental investigations were carried out on SHS with artificially introduced local pits on the surface of the members. These controlled imperfections enabled a systematic evaluation of how varying pit characteristics influence the residual strength and failure behaviour of the members. Axial compression tests were performed on the SHS specimens to evaluate how localised corrosion affects their loadbearing capacity. SHS specimens with both uniformly arranged and randomly distributed pits were examined, while ensuring an equivalent Degree of Pitting intensity (DOP) across specimens. Finite Element (FE) modelling was employed to replicate the tested specimens, providing a numerical framework to validate the experimental results. The accuracy of the simulations was confirmed by comparing numerical results and experimental data. Following validation, the FE models were used to conduct a detailed study of the behaviour of members with randomly distributed pitting. Statistical analysis was included to measure the impact of variability in pit characteristics on the residual structural performance. The validated numerical results revealed that randomly distributed pitting led to greater variability in strength and distinct differences in failure patterns compared to uniformly pitted members. The findings indicate that uniform corrosion reduces strength by 14%, while random pitting reduces the strength by over 30% for the specific pitting intensity considered in this study. As the pitting intensity increases, the strength reduction may range from 7% to over 50%. The maximum strength reduction occurs when the pits form in the middle of the specimen, indicating a critical vulnerability in the structural integrity under pitting corrosion. In summary, this research emphasises the significance of pitting corrosion in assessing the residual strength and longterm performance of steel structures.
item: Conference-Abstract
Artificial intelligence techniques in basin hydrological and material flow modelling: insights from dry and wet zone river basins in Sri Lanka
(Department of Civil Engineering, University of Moratuwa, 2025) Chamika, KHT; Rajapakse, RLHL; Baskaran, K; Mallikarachchi, C; Damruwan, H; Fernando, L; Herath, S
Water resource management has become a serious global challenge due to climate change, urbanization, and climate-induced irregular rainfall patterns. Traditional hydrological models, which depend on physical laws and long-term records, often struggle in regions with limited or inconsistent data. These models also face difficulty in capturing the non-linear, complex behaviors of real-world hydrological systems. Sri Lanka, with its diverse climate and topography, is especially affected. The Kelani River Basin in the wet zone and the Malwathu Oya Basin in the dry zone face unique and different water-related problems, such as pollution, flooding, and drought. Artificial Intelligence (AI) provides a novel solution that can handle data gaps and complex relationships. This research aims to study how effective different AI techniques are in predicting streamflow, filling missing rainfall data, and modelling nutrient levels in Sri Lankan river basins. The main objective of this study is to assess the suitability of AI models for improving hydrological and material flow analysis in Sri Lankan river basins. Daily rainfall and streamflow data (1990–2019) and monthly water quality data were collected and cleaned using standard methods like single and double mass curves. Models used in this study, suggested by the literature, include Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Time Series Transformer (TST), which was implemented using the Python programming language. Data were split into 80% for training and 20% for testing. For rainfall imputation, artificial gaps of 10% and 20% were created to test model accuracy. Nutrient modelling used monthly average rainfall, streamflow, and water quality parameters such as pH, turbidity, dissolved oxygen, and temperature. Model performance was evaluated using Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Pearson correlation coefficient (r). For streamflow prediction, LSTM gave the best results in the Kelani Basin (test NSE = 0.71), while XGBoost overfitted the training data. In the Malwathu Oya Basin, simpler models like MLR and TST performed better, achieving test NSE above 0.80. The RF model proved to be the most reliable method for imputing missing rainfall data in both basins. While MLR showed slightly higher accuracy in some cases, RF gave more stable results across different deletion levels. However, here using complex models did not bring much improvement over simple statistical ones. Nutrient modelling was more challenging. RF showed promise for predicting chloride levels (test NSE = 0.40), but nitrate predictions were poor in all models (NSE < 0.15). And, identified a weak correlation with nutrient flow and hydrological inputs. In conclusion, AI techniques are highly useful for improving streamflow prediction and filling data gaps. However, predicting nutrient concentrations needs more input variables, such as land use and pollution data. Future research should explore hybrid models that combine AI with physical processes for more accurate and reliable water resource management.
item: Conference-Abstract
Investigating the variability of precipitation and its relationship with climate modes in different regions of Sri Lanka
(Department of Civil Engineering, University of Moratuwa, 2025) Kawshalya, MKKP; Dissanayake, BC; Baskaran, K; Mallikarachchi, C; Damruwan, H; Fernando, L; Herath, S
Understanding precipitation variability is vital for water resource management, agriculture, and disaster preparedness in Sri Lanka, a country highly sensitive to climate fluctuations. Sri Lanka, situated in a region influenced by complex atmospheric circulation patterns, is particularly susceptible to the impacts of various large-scale climate modes. These atmospheric phenomena, including the El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) exert significant influence on the country's weather and climate systems. Understanding how ENSO and IOD affect rainfall patterns in Sri Lanka is critical for sustainable water resource management and agricultural planning, making this research essential for the nation's economic and environmental sustainability.
This study explores the influence of two major climate drivers, ENSO and IOD on Sri Lanka's rainfall patterns from 1999 to 2015, addressing a critical gap in understanding the simultaneous impacts of these phenomena on precipitation variability. ENSO events were identified using the Oceanic Nino Index (ONI), while IOD events were determined using the Dipole Mode Index (DMI) based on established meteorological thresholds. Both indices were normalized using the min-max method to ensure statistical comparability and robust analysis. Precipitation data from representative meteorological stations strategically distributed across the wet, intermediate, and dry zones were analysed using advanced statistical techniques to evaluate correlations with these climate indices.
Warm sea surface temperatures in the equatorial eastern Pacific are referred to as the El Nino phase, while its cooler counterpart is considered as the La Nina phase. El Nino events generally suppressed rainfall in the wet and intermediate zones, creating drought-like conditions, while La Nina events enhanced precipitation in these regions, often leading to above-normal rainfall. Positive IOD events were typically associated with increased rainfall across most regions, whereas negative IOD events corresponded to drier conditions, though notable regional variations were observed throughout the study period. The findings underscore the complex yet significant influence of ENSO and IOD on Sri Lanka's regional rainfall patterns, demonstrating that these climate oscillations play crucial roles in determining seasonal and annual precipitation variability. This research fills a critical knowledge gap by examining the simultaneous impacts of ENSO and IOD on precipitation patterns and evaluating the ability of statistical models to forecast rainfall anomalies under varying climate conditions. Understanding their combined effects is essential for improving climate predictions and informing evidence-based decision-making in key economic sectors. Future research should incorporate more recent data and employ machine learning approaches to explore complex interactions among multiple climate drivers.
item: Conference-Abstract
Assessing sustainability of water releases from the lunugamwehera reservoir in Sri Lanka
(Department of Civil Engineering, University of Moratuwa, 2025) Nethmini, MHN; Gunasekara, NK; De Silva, PKC; Baskaran, K; Mallikarachchi, C; Damruwan, H; Fernando, L; Herath, S
Water security is an increasingly critical concern in many regions across the globe. This is due to the rising demand for freshwater, driven by population growth, climate change, and agricultural expansion. Sri Lanka, particularly its dry zone, faces significant water scarcity issues. In these regions, water availability varies greatly. Reservoirs play a vital role in storing and regulating water for irrigation, domestic supply, and hydropower. However, the sustainability of these reservoirs' water releases is under threat. This study seeks to assess the sustainability of water released from the Lunugamwehera reservoir in Southern Sri Lanka. The reservoir, which was established in 1986, caters for the needs of over 5,200 families in the area. The agricultural sector is the largest consumer of water, particularly for paddy cultivation. Ensuring that water releases from reservoirs such as Lunugamwehera are aligned with the demand is essential, and increasingly a matter of concern. This is crucial for safeguarding agricultural productivity, public health, and ecological balance. The study evaluates the Lunugamwehera reservoir’s ability to meet its water demands amidst these challenges. The Hydrologic Engineering Center’s Hydrologic Modelling System (HEC-HMS) is used to simulate runoff and estimate inflows to the reservoir. It uses daily rainfall and streamflow data from 2012 to 2023. A water balance approach is then employed to assess the relationship between water inflows, outflows, and storage variations within the reservoir. To evaluate the sustainability of water releases from the reservoir, several indicators were selected. These include annual water balance (AWB), storage variation (SV), irrigation water demand satisfaction (IWDS), domestic water demand satisfaction (DWDS), spill efficiency (SE), and reservoir utilisation (RU). These indicators were normalised and then weighted using the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method. The resulting Sustainability Index (SI) is calculated through the additive aggregation method. This method combines the weighted values of the indicators to produce an overall measure of sustainability for each year from 2012 to 2023. The results of this study reveal significant fluctuations in the sustainability of the Lunugamwehera reservoir over the years. SI showed the highest value in 2012. However, subsequent years experienced declines, particularly from 2016 to 2020. However, slight improvements in sustainability were observed in the later years. This research contributes valuable insights into the water management practices of Sri Lanka’s reservoirs. The study's findings highlight the need for improved water management strategies. This can help policymakers and water resource managers make informed decisions for ensuring long-term water security.