Browsing by Author "Dharmawansha, S"

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    PublicationOpen 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.
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    PublicationOpen Access
    Hybrid neural network methods to model the external wind pressure on a low-rise flat-roofed building in an irregularly shaped urban environment
    (Elsevier Ltd, 2025-06-23) Sajindra, H; Dharmawansha, S; Wijesundara, H; Herath, S; Rathnayake, U; Meddage D.P.P
    The present study used hybrid artificial neural networks to model the wind pressure (mean and fluctuating) on a flat-roofed, low-rise building in an irregularly shaped urban environment. Four neural networks, each combined with an artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimisation (PSO), and independent component analysis (ICA), along with an individual artificial neural network (ANN) model and a convolutional neural network (CNN), were used for the wind pressure predictions. The data was obtained from Tokyo Polytechnic University’s boundary layer wind tunnel and was used to train the neural network models. The results revealed that all models accurately captured the wind pressure on the low-rise building in a dense urban environment. Specifically, the genetic algorithm-artificial neural network (GA-ANN) model outperformed the remaining models, achieving good prediction accuracy for test data (coefficient of determination (R²) = 0.96 for mean pressure R² = 0.84 for fluctuation pressure). The use of machine learning explainability methods confirmed the consistency of GA-ANN with the fundamentals of wind engineering. Notably, the GA-ANN approach accurately modeled the special flow features on the building surface, such as flow separation, vortex formation, and pressure gradients, to a greater extent compared to the wind tunnel results. Therefore, the authors propose this method as an complementary approach for predicting wind pressure on low-rise buildings in complex urban environments