Search Results (2)

Sustainable design in large-scale infrastructure projects, such as airports, is crucial for minimizing environmental impacts while ensuring long-term financial feasibility. This study focuses on selecting the most sustainable pavement solution for airport construction, using Tabuk Airport in Saudi Arabia as a case study. The purpose of this study is to evaluate four pavement alternatives using a multi-criteria decision-making approach to identify the optimal solution in terms of sustainability, cost-effectiveness, and feasibility. The alternatives were assessed based on nine key criteria, including environmental impact, durability, cost, and maintenance. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks the alternatives, while the Fuzzy Analytic Network Process (FANP) calculates the criteria weights, addressing uncertainties and interdependencies. Geographic Information System (GIS) is integrated to incorporate spatial factors affecting pavement sustainability. The results show that the alternative using recycled materials (A4) is the most suitable, offering the best balance of sustainability and cost. A4 achieved the highest ranking in the evaluation, making it the recommended choice for the upcoming Tabuk Airport project. This study demonstrates the effective application of decision-making tools, such as TOPSIS, FANP, and GIS, in guiding sustainable infrastructure development and providing a replicable framework for similar projects worldwide. Full article

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The ‘FGSC’ blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The ‘FGSC’ blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications. Full article