Architecture Engineers Award

  In the global effort toward achieving carbon neutrality, the building sector stands as one of the largest contributors to energy consumption and emissions. Enhancing the energy efficiency of buildings, particularly during their operational phase, has therefore become a central focus of modern architecture and sustainability research. The integration of Artificial Intelligence (AI) and Digital Twin technologies presents a promising path forward, enabling data-driven insights, real-time control, and predictive energy management. This research investigates the role of AI-driven Digital Twins in optimizing building performance, aligning technological innovation with sustainable design principles. Research Motivation and Objectives Buildings account for a significant portion of global energy demand, and inefficiencies in operation often stem from the lack of real-time monitoring and predictive control systems. Traditional energy management approaches fall short in dynamically adapt...

  Urban areas face increasing parking challenges due to rising vehicle density, limited land availability, and operational inefficiencies in existing parking facilities. Traditional manual vehicle parking (VP) methods often result in underutilization and poor management of parking resources. This research explores the integration of Digital Twin (DT) technology , Building Information Modeling (BIM) , and machine vision to create an automated, intelligent parking management system. By leveraging real-time data and advanced detection algorithms, the study aims to enhance parking efficiency, monitoring, and visualization within urban built environments. Vehicle Detection and License Plate Recognition Integration A crucial aspect of the system is the use of YOLOv7 for vehicle detection (VD) and Deep Text Recognition–Scene Text Recognition (DTR-STR) for license plate recognition (LPR). These technologies allow for precise tracking of vehicles entering and exiting parking faciliti...