Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open 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.PThe 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 environmentsPublication Embargo Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks(Pergamon, 2020-08-15) Dewapriya, M. A. N; Rajapakse, R. K. N. D; Dias, W. P. SAdvanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction. A part of the data required to model the shallow networks was obtained by developing an analytical solution based on the Bailey durability criterion and the Arrhenius equation. Molecular dynamics (MD) simulations were also used to obtain some data. Sensitivity analysis was performed to explore the features learnt by the neural network, and their behaviour under extrapolation was also investigated. Subsequently, deep convolutional neural networks (CNNs) were developed to predict the fracture stress of graphene samples containing random distributions of vacancy defects. Data required to model CNNs was obtained from MD simulations. Our results reveal that the neural networks have a strong ability to predict the fracture stress of defective graphene under various processing conditions. In addition, this work highlights some advantages as well as limitations and challenges in using neural networks to solve complex problems in the domain of computational materials design.