Recent Submissions
Construction Dynamics And Digitalization
(Faculty of Engineering, 2025-09-09) Premachandra, P.N
The construction industry is at the edge of a decisive transformation, moving away from fragmented, paperbased practices toward an era defined by intelligent digitalization. At the center of this shift is the Digital Twin- a living, data rich model that synchronizes the physical and virtual realms of construction. By Integrating Building Information modeling (BIM), Internet of Things (IoT) sensors, artificial intelligence, and cloud computing, Digital Twins enable Projects to move from reactive monitoring to proactive, predictive control. This paper examines their influence on Construction dynamics, demonstrating how 4D scheduling with Primavera P6 and intuitive dashboards guided by PMBOK-7 principles elevate visibility, collaboration, and decision making. A case study of the Maldives International Airport new terminal
illustrates tangible outcomes: real-time clash detection, optimized sequencing, energy efficient design, and measurable carbon emission reductions. Beyond showcasing benefits, the study outlines a pragmatic
roadmap for Sri Lanka, stressing the importance of regulatory reform, academia-industry partnerships, and pilot implementations. The findings suggest that Digital Twins are not distant aspirations but present-day necessities for sustainable, data driven construction.
A Data-Driven Approach to Predicting Ischemic Heart Disease Risk in Monaragala: Integrating Lifestyle and Symptom Factors with Machine Learning
(Faculty of Engineering, 2025-09-09) Meddepola, M.A.R.L.; Wickramasinghe, B.M.G.S.T.S.K.
Ischemic Heart Disease (IHD) remains a leading cause of mortality worldwide and presents a critical challenge in underserved rural areas such as Monaragala, Sri Lanka. Traditional IHD prediction methods predominantly depend on clinical diagnostics like ECGs and blood tests, which are often unavailable or inaccessible in such regions. This study aims to bridge this gap by developing a machine learning-based prediction model that utilizes only lifestyle and symptom-related data, eliminating the need for invasive clinical procedures. A dataset comprising lifestyle habits (e.g., diet, smoking, alcohol use, exercise) and symptom indicators (e.g., chest pain, fatigue, dizziness) was collected via surveys. Feature selection using Logistic Regression identified the top eight most relevant predictors. Five machine learning algorithms, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest, were trained and evaluated. Among them, the Random Forest model achieved the highest performance with an accuracy of 83.5%, precision of 0.86, recall of 0.78, and F1- score of 0.81, demonstrating strong predictive capability based solely on non-clinical features. In addition, a web-based self-assessment tool was developed to make the model accessible to the public, particularly targeting individuals in rural areas with limited healthcare access. The tool enables users to input basic lifestyle and symptom information and receive a real-time risk assessment. The findings confirm that the model leveraging lifestyle and symptom data can effectively identify individuals at risk of IHD. This approach supports the development of scalable, low-cost, and user-friendly screening tools that can enhance early detection and preventive care, especially in rural and resource-constrained settings.
Predictive Modeling for Personalized Cancer Therapy Using Reinforcement Learning
(Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne, J H M S M
Adaptive therapy is transforming cancer treatment by enabling dynamic, patient-specific interventions that adapt to tumor progression and individual variability. Unlike traditional fixed-dose regimens, adaptive therapy leverages the evolutionary dynamics of tumors to extend treatment effectiveness and delay resistance. Reinforcement Learning (RL), an area of artificial intelligence focused on sequential decision-making, offers a robust framework for optimizing these adaptive strategies. RL can learn optimal treatment policies by interacting with computational models of tumor growth and drug response, continuously adjusting regimens based on observed tumor states, resistant cell populations, and biomarkers. This approach allows for the creation of personalized therapies that maintain long-term tumor control while minimizing toxicity and the emergence of resistance. The integration of RL into predictive modeling for cancer therapy represents a paradigm shift, enabling smarter, safer, and more effective treatments that are dynamically tailored to each patient’s evolving disease. This paper reviews the foundational concepts of adaptive therapy and RL discusses tumor modeling approaches, examines RL algorithms, and addresses current challenges and future directions in the field.
Development of Low-cost Slipper by using NR/EVA Blend with Recycling Materials for reducing Environment Pollution in Footwear Industry
(Faculty of Engineering, 2025-09-09) Randika K.G.; Perera K.P.M.; Gunaratne R.D.
This study aims to develop a low-cost slipper compound by blending low grade Natural Rubber (NR), Ethylene Vinyl Acetate (EVA), and recycled LDPE plastics granules with crumb rubber in different phr
(parts per hundred rubber) amounts. Low grade natural rubber (off grade brown scrape) and ethylene vinyl acetate (19 wt.% of vinyl acetate) were used during formulation in order to reduce cost. During
this compounding process, polymeric material and other chemical ingredients were masticated by using a kneader and two roll mills then sheet was prepared by using calendaring techniques, eventually curing was performed by using a compressing molding method. Blowing agents were used to obtain the Slipper sheets’ inter cellular structure. Peroxide curing system has used due to natural rubber blend with ethylene vinyl acetate. When preparing compound batch, different phr amount of crumb rubber and recycled LDPE plastics granules blended. Firstly, crumb rubber sheets which were punctured and waste scrap sheets were obtained, then converted into 30 mesh size small particles by using grinding and crush method. Hardness and Abrasion tested of prepared slipper. After curing process higher hardness value observed when increasing crumb rubber phr. As particle size increases, there was a tendency of asymmetrical spread of compounding ingredients through the mixture and this was mitigated by additions of processing oils which increased the dispersion of the particles. In summary, through this work, ideal compounding formulation with phr values was able to determine that can be used to manufacture to low-cost slipper sheet at industrial scale.
Enhancing Modern Education Through an AI-Integrated Learning Management and Support System (LMSS)
(Faculty of Engineering, 2025-09-09) Rashminda, J
The rapid advancement of educational technologies has underscored the limitations of conventional Learning Management Systems (LMS) in effectively supporting the evolving demands of learners and educators. While traditional LMS platforms primarily focus on content delivery and administrative tasks, they often lack the capacity to foster active engagement, facilitate meaningful collaboration, and promote participation in broader learning experiences. This paper presents the design and functional implementation of a prototype for a Learning Management and Support System (LMSS), an AI-enhanced platform built to address these limitations by offering a more holistic and student-centred approach to digital education. LMSS integrates course management with interactive features that encourage student collaboration, peer-to-peer communication, and involvement in academic and extracurricular events. These capabilities are designed to support a more engaging and socially connected learning experience while also simplifying instructional workflows for educators.
The system incorporates adaptive learning tools and real-time insights to better align learning processes with individual needs and institutional goals. This paper reviews the existing literature, highlights gaps in current LMS implementations, and details the development methodology, architecture, and feature set of LMSS. The system’s anticipated impact is grounded in established research findings demonstrating that adaptive learning approaches can significantly enhance student engagement, AI-driven early intervention can improve retention rates among at-risk learners, and realtime analytics can reduce instructor workload related to feedback provision. By integrating these evidence-based practices into a unified platform, LMSS is designed to foster learner motivation, deepen engagement, and support teaching effectiveness. Ethical considerations such as user privacy and data governance are also addressed to ensure responsible and transparent use.
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