سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

Self-Adapting Urban Digital Twins: A Cost-Effective Optimization Approach for Long-Term Infrastructure Management

نویسندگان :

Shohreh Ajoudanian¹ Maryam Nooraei Abadeh² Hamid Reza Aboutalebi³

1- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran 2- Department of Computer Engineering, Arvand International Branch, Islamic Azad University, Abadan, Iran. 3- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Cost-effectiveness،Infrastructure optimization،Self-adoption،Smart city،Urban digital twin

چکیده :

Urban digital twins (UDTs) have emerged as transformative tools for managing urban infrastructure by integrating real-time data with advanced modeling techniques. For long-term infrastructure management, their high implementation costs, computing requirements, and lack of adaptability to changing urban systems, however, restrict their general acceptance. This paper proposes a novel framework for self-adapting UDTs, designed to optimize cost-effectiveness while enhancing resilience and efficiency in urban planning and operations. Leveraging modular architectures and adaptive data processing techniques, the proposed approach minimizes resource consumption and enables continuous updates to reflect dynamic urban conditions. Based on an investigation of current UDT implementations, this paper highlights important issues including data quality, interoperability, and scalability and tackles them using a self-adaptive system that strikes a compromise between economic feasibility and usefulness. Over long times, the results imply that self-adapting UDTs can greatly lower maintenance costs and enhance decision-making for sustainable urban infrastructure management.