Browsing by Author "Wijesundara, H"

Now showing 1 - 2 of 2

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.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
Open Access
Sustainable food waste management: A cross-country study of Australian and Sri Lankan hotel sector
(Elsevier Ltd, 2025-12) Jayasuriya, N; Wickramaarachchi, C; Wijesundara, H; Sriyananda, U; Rathnayake, V; Liyanage, T
Food wastage constitutes a critical global issue, with an estimated one-third of the food produced worldwide being wasted annually. The hotel sector represents a key contributor to this problem; however, it has received limited attention in the existing body of research. Therefore, this study seeks to undertake a comprehensive analysis of the underlying drivers of food wastage, the challenges encountered, and the strategies implemented to mitigate this issue within the hotel industry. Addressing the different contexts in developed and developing countries, this study has selected hoteliers in Australia and Sri Lanka. Data was collected from 20 hotel employees from both countries who are responsible for food handling and were analyzed thematically. The findings identified transportation waste, kitchen waste, and consumer waste as critical points of food wastage. Additionally, the role of technological equipment, combined with food safety precautions and regulatory measures, emerged as pivotal in managing food waste. These aspects are examined in detail alongside proposed mitigation strategies. Even though hospitality sector is largely contributed to these issues, the studies conducted on this sector in relation to the food wastage is very limited. Thus, this study focuses on filling the void in the literature by conducting an in-depth investigation on this topic.