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
A Review on the effects of artificial lighting on marine ecology in seaport terminals
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Wanniarachchi, SS; Perera, PM
In recent years, excessive or improper application of artificial lighting has had resulted serious environmental consequences, scientifically known as Light Pollution. The night sky and ocean close to transportation terminals are illuminated by a variety of artificial lights, including high-mast lights, navigation lights, security/emergency lights, crane and equipment lighting, LED floodlights, and dock lighting, in addition to the primary natural light sources of sunlight, moonlight, stars, and bioluminescent light. These high-intensity lights have been shimmering along the coastlines, creating a pathway for light pollution and its effects. Research on optical oceanography and ALAN (Artificial Light at Night) impact is expanding globally. However, there is a great dearth of research regarding light pollution related to seaport terminals in Sri Lanka as ALAN is relatively a new area of analysis. The aim of this narrative review paper is to address the question, “what are the ecological impacts of artificial lighting from seaports on marine life” and to raise policymakers’ awareness on potential threats marine ecology. Scopus, Google Scholar databases and IMO publications were used for the scientific literature search. Terminal lighting is crucial factor in a seaport for safety, security, and efficiency of movement of people and commodities during around-the-clock operations and the vertical and horizontal light in buoys and beacons are crucial for hazard avoidance in ships. Standards of IMO and International Commission on Illumination (CIE), minimum illumination levels, color temperature levels, and Color Rendering Index are maintained at sea ports for safer navigation and operations. Terminal light emissions beyond shorelines, disrupt delicate underwater ecosystems in pelagic zones and coastal areas. According to literature, around 2 million km² of the world's ocean at a depth of 1 m are affected by light pollution. ALAN impacts include changes in predator- prey interaction patterns of fish, coral spawning, zooplankton diel vertical migration (DVM), and seabird / sea turtle navigation. LED lights’ white illumination, adversely affect the recruitment and colonization of marine epifaunal communities like sessile and mobile invertebrate species. Regulations mandate light intensities, luminous intensity, and spectral power distribution units, focusing on human vision, but similar visual metrics should be developed considering the marine and nocturnal animals for more sustainable and environmentally friendly practice. The scientists have developed the Light Pollution Index (LPI) to measure the ALAN impact at night sky. Additionally, strategies like WWF earth hour, green space management and light flow control also used to mitigate the ALAN impacts in infrastructure development. Effective mitigation strategies further include proper placement/direction of vertical beam spread, removing excess lights, reduce uplighting & residual effects, installing timers or dimmers, and using shielded lighting fixtures. Additionally, raise awareness on light pollution, as the DarkSky International, have shown measurable impacts on urban light levels. Inarguably the night sky and marine ecosystems are at threat due to ALAN impact caused by seaports and nearby urban structures. There’s a growing need to include the effects of light pollution analysis in port master plans. This study emphasizes that careful analysis and preventive strategies exist to constrain and to reduce the ALAN impact of seaports while ensuring safe and efficient operation.
item: Conference-Abstract
Utilization of varied grades of coir fibers to enhance the subgrade strength of rural roads constructed on soft soils
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Divyangani, U; Hettiarachchi, C
With the continuous development of the urban areas in Sri Lanka, the shortage of land for the construction of pavements has become as a major issue. In such instances, Engineers are supposed to resort to constructing pavements in areas with soils that are known to be problematic. These problematic soils are most commonly known to be clays and silts due to their high swelling and shrinkage characteristics along with poor load-bearing behavior. The current method of "excavation and replacement" for altering the subgrade, is unquestionably expensive and time-consuming. Therefore, alternatives should be considered in modifying such subgrade. Such alternatives in modifying the subgrade are natural fiber reinforcement, synthetic reinforcement of the soil. Glass fibers, nylon fibers, steel fibers, polyvinyl alcohol fibers (PVA), polyester fibers (PET), polypropylene fibers (PP), and polyethylene fibers (PE) are examples of geosynthetics that are currently in use for subgrade modification. Natural fibers like palm, coir, sisal, jute, hemp, bamboo, and kenaf are becoming increasingly popular since they are more affordable and environmentally beneficial. The durability of natural fibers in subgrade enhancement is known to be 15 to 30 years in time for it to biodegrade fully, according to the soil condition it is reinforced with. The objective of this paper is to study the load bearing characteristics of soft soil when reinforced with different grades of coir fiber. Various studies have been conducted on soft soils such as clay or silt, reinforced with fibers. The most common length of fiber adopted for subgrade modification was seen to be within the range of 1cm to 5cm whereas the composition of fiber varied between 0.2% to 1.5% of the weight of soil. Hence, current studies on fiber reinforced subgrades were used to adopt the length and percentage of fiber in this study. Two commonly available grades of brown coir fiber, which are Mattress fiber and Bristle fiber were used in this study, with composition varying from 0.25% to 0.75% (with 0.25% increments) and lengths varying from 1cm to 3cm (as short fibers in random arrangement). The soil, that was collected from the flood prone area around the Kelani River basin was initially tested for the physical and mechanical properties with the aid of several laboratory experiments such as sedimentation analysis, Atterberg limits and Standard Proctor Compaction (SPC) tests where the soil was classified; this was considered as the control sample. The soil was classified as a fat clay (CH) with a liquid limit of 76% and a plasticity index of 41.34%. The compaction test results showed that the clay bears an optimum moisture content of 27.4% and a maximum dry density of 1.39g/cm3. The 96-hour-soaked California Bearing Ratio (CBR) test on the unreinforced sample showed a low CBR value of 1.21%. The clay was then reinforced with both mattress fibers and bristle fibers and were tested against a soaked CBR test. The sample of fat clay possessed a CBR value of 1.77% with 0.25% bristle fiber which was 1.46 times greater than that of the control sample. 0.50% composition of bristle fiber gained a strength of 2.09 times with a CBR value of 2.54%. The inclusion of 0.75% bristle fiber recorded a 2.61% CBR value by a factor of 2.15 times of unreinforced clay. In the inclusion of mattress fiber, the 0.25% fiber sample recorded a CBR value of 1.68%, 0.50% fiber sample with a CBR value of 1.80% and 0.75% fiber sample with a CBR value of 1.98%. the strength gained were 1.39, 1.49 and 1.64 times respectively. The optimum percentage of fiber was observed as bristle fiber of 0.75% due to its stiff texture and good load distribution characteristics in the clay fiber matrix. The mattress fiber was as effective, in lower quantities due to its smooth texture. From this study, it was concluded that the use of bristle fiber from coir can be used to enhance the load bearing characteristics of soft soils to a considerable extent, with more room for improvement.
item: Conference-Abstract
Development and evaluation of an interactive road safety education module for secondary school students in Sri Lanka
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Gunathunga, LP
Vehicular accidents remain a major cause of fatalities and serious injuries worldwide, affecting both developed and developing nations. According to the World Health Organization (2024), annual road traffic fatalities have slightly declined to 1.19 million. Despite this reduction, road crashes remain the leading cause of death for children and young adults, with over 3,200 fatalities occurring daily. A significant proportion of child fatalities are linked to high-risk road use behaviors, exhibited either as pedestrians or vehicle occupants. This global issue is also evident in Sri Lanka, where, according to the Sri Lanka Police (2024), 24,589 road accidents were recorded, resulting in an average of six fatalities per day. Notably, 80% of these fatalities involved vulnerable road users. Among these, approximately 838 pedestrian fatalities were reported, averaging two deaths per day. In total, 2,368 accidents resulted in fatalities, with many more causing severe and often lifelong injuries. These statistics highlight the urgent need for targeted interventions to mitigate high-risk behaviors and improve road safety. “Age” has been identified as a significant factor influencing road accident involvement, underscoring the importance of early education in promoting road safety in Sri Lanka. One of the most effective strategies to address this issue is the implementation of a school-based road safety education program, which fosters safer behaviors among young drivers and vulnerable road users. This study focuses on secondary school students (ages 14–16), a key demographic for early intervention. Data samples will be collected from selected schools within the Colombo District to assess the impact of the educational initiative. The study aims to evaluate students' existing knowledge and attitudes toward road safety, introduce an interactive road safety education module, and assess its effectiveness using pre- and post-assessments. A preliminary literature review was conducted, followed by structured interviews with three road safety professionals and three educational experts/psychologists. The interviews, conducted in a semi-structured format using questionnaires, allowed for in-depth discussions on key road safety challenges, educational gaps, and potential interventions. Responses were analyzed using thematic analysis and the Relative Importance Index (RII) method to identify key themes and priorities. Based on these findings, a web-based road safety education (RSE) module was developed, focusing on the following key areas: Introduction to Road Safety, Knowledge of Traffic Rules & Symbols, Safe Driving & Passenger Habits, and Emergency Response Skills As part of the pilot study, the module was distributed to 50 students, and feedback was collected to refine and optimize the content before commencing the full-scale study. Six schools in the Colombo District were selected based on willingness to participate and student population size, ensuring a diverse representation of students with varying exposure to road safety risks. Students from three schools were designated as the “Control Group,” and students from the remaining schools as the “Treatment Group.” Both groups were initially granted access to the module, and responses were recorded through a pre-assessment survey. After this phase, only the Treatment Group retained full access, allowing them to engage with interactive content and activities over the designated period. The Control Group had no further access beyond the initial assessment, ensuring a comparative analysis of knowledge retention and behavioral changes. After one month, a final questionnaire was administered via the web module, allowing participation from both groups to assess the impact of the intervention. Participants' responses were analyzed using statistical methods such as mean, median, and standard deviation, enabling a comprehensive interpretation of the findings. The outcomes of this research are expected to provide valuable insights for educational and road safety authorities in Sri Lanka, contributing to potential enhancements in the secondary school curriculum to improve road safety education.
item: Conference-Abstract
Using a machine learning approach to develop a macroscopic passenger flow model for departure passengers at an airport terminal
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Rathnayake, O; Adikariwattage, V; Senanayake, C
Effective passenger flow management is a cornerstone of airport terminal operations, directly impacting service quality, resource allocation, and traveller satisfaction. As global air traffic demand continues to grow, airport systems must contend with increasingly complex, dynamic, and high-volume environments. Traditional modelling methods—such as queuing theory, regression models, and discrete event simulation (DES)—have provided valuable frameworks for analysing terminal processes. However, these methods often struggle to deliver scalable, flexible, and real-time solutions required for modern operational decision-making. In particular, simulation-based approaches, while robust, are computationally intensive and often impractical for day-to-day operational use. This study introduces a hybrid methodology that integrates discrete event simulation with machine learning to develop a macroscopic passenger flow model focused on departure processes. The aim is to provide a predictive, data-driven framework capable of capturing the temporal and behavioural complexity of passenger movement through an airport terminal. The research begins with an extensive literature review to identify the key determinants of congestion and flow disruptions. These include temporal arrival patterns, check-in modalities (e.g., counter, kiosk, online), counter allocation strategies, processing times, and passenger characteristics such as group size and baggage quantity. A comprehensive DES model was developed using Simio software, simulating the full departure journey—from curb side check-in to security clearance at the boarding gate. Grounded in empirical data collected from Bandaranaike International Airport, Sri Lanka, this simulation replicates realistic system behaviour and serves as a high-fidelity synthetic data generator. This approach overcomes a common limitation in aviation analytics: the lack of consistent, large-scale real-world datasets. The synthetic data generated through DES was used to train and evaluate machine learning algorithms, with a focus on forecasting short-term fluctuations in passenger flow and terminal congestion. Among the models tested with a sample data set, Long Short-Term Memory (LSTM) networks outperformed both Support Vector Regression (SVR) and Random Forest (RF) in terms of predictive accuracy, generalization capability, and robustness against overfitting. The LSTM model achieved the lowest mean squared error and root mean squared error, demonstrating strong potential for operational deployment. The proposed DES-ML framework offers a scalable, interpretable, and efficient solution for airport stakeholders. It enables near real-time delay prediction and flow estimation, supporting more agile resource planning and enhancing passenger service levels. Moreover, the modular nature of the framework allows for adaptation to varying airport configurations and operational policies. In conclusion, this research demonstrates the value of integrating simulation and machine learning to develop a macroscopic, predictive passenger flow model that bridges the gap between high-fidelity analysis and real-world applicability. The hybrid model not only advances academic understanding of terminal dynamics but also provides a practical decision-support tool for improving operational performance in airport environments.
item: Conference-Abstract
Cost-effective prioritization of road networks for post-flood recovery: a network robustness framework
(Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025) Kulathunga, M; Pasindu, HR
An increase in the occurrence and intensity of flooding events is posing significant damage to worldwide road infrastructure. Their consequences include reduced movement, loss of accessibility, and lower economic activity, which disrupt both community and regional transportation systems. For a swift post-flood recovery, it is important to decide the best repair actions for damaged roads and to keep the network strong, within the budget levels. The study introduces a new framework that helps choose the best repair options for flood-damaged roads by prioritizing the robustness of the network. It helps transportation agencies find the least-cost set of links to repair while maintaining a specified level of post-repair network robustness. The authors suggest a three-part approach for determining which sections of a road network should be recovered first. To start, various flood cases are defined by progressively dropping some road sections, symbolizing the extent of the flood. Second, network robustness is measured by computing the proportion of the Giant Connected Component (GCC) is with the whole population of nodes. It refers to the required level of network operations after repairs. A genetic algorithm (GA) is used to ensure the selection of the best links for repairing the network so that the target robustness can be achieved at the lowest overall cost. The optimization model aims to minimize the total reconstruction cost while ensuring the repaired network meets the robustness threshold. The approach forms the problem as a binary combinatorial optimization, where each road segment is assigned a bit (1 for reconstruction and 0 otherwise). The GA procedure starts by choosing a random initial population and then assesses fitness by summing reconstruction cost and network robustness, selects individuals based on a tournament process, performs crossover and mutation operations and finishes when the population converges or reaches the set number of generations. All road links are assumed to have uniform costs for reconstruction per kilometer. The model was used on artificial networks as well as actual road networks with differences in density and structure. In synthetic networks, reconstruction costs were shown to rise with both the extent of damage and the target’s strength. Due to the less redundancy, sparse networks were more easily affected by disruptions and required more investment when damage was moderate. However, comparatively denser networks kept functioning strongly even when large parts were destroyed, so the reconstruction cost was lower. Despite the difference in value scale, actual road networks emerged with similar patterns against the damage levels and the target robustness levels. The model identified cost-effective repair strategies for both hypothetical and actual networks, validating its applicability to real-world infrastructure planning. Notably, the results revealed exponential cost growth with increasing damage levels, particularly at higher target robustness values, where small increments in damage led to disproportionately higher costs. This makes it clear that early improvements or upgrades can prevent large expenses in recovery after a disaster. This study aims to provide an effective approach for selecting cost-effective repair methods for roads damaged by floods by analyzing the impacts of different kinds of damage on the road network. Using the model, transportation authorities can detect vulnerable parts of the system and calculate the minimal budget necessary to provide suitable repair strategies after a flood. Results reveal that proactive measures are important because the cost of reconstruction rises fast with each level of damage. Under fiscal constraints, authorities can select appropriate robustness targets that balance adequate functional restoration with available resources. The framework provides a systematic approach for developing scenario-based contingency plans, enabling more effective decision-making in post-flood road network recovery.