In recent years, the management and maintenance of urban infrastructure have faced significant challenges, including increasing system complexity, asset deterioration, and limitations in financial and human resources. Traditional maintenance approaches, which are largely reactive and implemented after failures occur, are no longer capable of effectively addressing these challenges. In this context, digital twin technology has emerged as a promising solution, offering substantial potential to enhance monitoring, analysis, and decision-making processes in urban infrastructure management. However, a review of existing studies indicates that most prior research has focused on fragmented applications or limited simulations, while comprehensive and integrated conceptual frameworks for urban infrastructure maintenance remain insufficiently explored.



The objective of this study is to develop a layered conceptual model for digital twins with a specific focus on the management and maintenance of urban infrastructure. The research methodology is based on a systematic literature review, analysis of existing frameworks, and the design of an integrated conceptual architecture. The proposed model consists of key layers, including the physical layer, sensing and Internet of Things (IoT) layer, data management and processing layer, digital twin modeling layer, artificial intelligence and machine learning layer, and the decision-making and urban governance layer. This architecture enables real-time monitoring, predictive analysis of infrastructure conditions, and effective support for managerial decision-making.



The results of the conceptual analysis demonstrate that the proposed model, by establishing a coherent linkage between real-time data and intelligent analytics, enhances predictive maintenance, optimizes resource allocation, and improves the resilience of urban infrastructure systems. By providing a comprehensive conceptual framework, this study can serve as a foundation for the development of intelligent infrastructure management systems and for future applied and empirical research.