Browsing by Author "Rajapakse, C"

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Open Access
Agent-Based Gamified Learning Environments for Data Science Education
(SLIIT Business School, 2023-12-14) Jayalath, N; Rajapakse, C
Because of the rapid advancement of technology and the increasing importance of the inferences that can be drawn from the big data available in organizations, modern organizations require managers and data Analysts who are capable of data-driven decision-making. But data science students need a natural environment when it comes to learning data-driven decisionmaking, especially when it comes to predictive and prescriptive analytics. Due to costs and other associated risks in a natural organisation setting, it is hard for educational institutions to teach these aspects of decision-making for data science students. Even Though gamification has been implemented in the data analysis domain in various forms, the field still requires a suitable environment to learn predictive analytics interactively for the students. Even though Researchers have identified that Gamified learning environments can improve Predictive analytics learning can be improved by 15.8%, still there is the lack of proper implementation of a suitable gamified learning environment. This research focused on identifying drawbacks of existing learning environments and whether Agent-Based Modeling can be used in modelling a suitable gamified learning environment. Therefore, an agent-based prototype model of a parameterized environment that enables data-driven decision-making in a simulated environment was modeled using Agentbased modeling, which depicts real-life donor interactions. Results suggest that fill in blanks This Agent-based model can be used as a learning environment for data analysis. Upon further modification, A game that applies this Agent-based model can be developed.
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, C
Dapped-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.