## 4 Analyses of the Applications of Digital Twin Models
### Digital Twin in Other Domains
Regarding the comparative study results of hyperparameters, it was observed that the DCNN-LSTM network exhibited the highest prediction accuracy, and the 2 CONV+1 LSTM network combination provided the best performance. By introducing elastic module data, the model's prediction capability was enhanced. Integrating the Digital Twin concept in this context can contribute to monitoring and managing such structures by creating a virtual model of the bridge. This virtual model will use real-time-acquired data from temperature and displacement sensors, considering the structural and functional characteristics of the bridge. The construction of the model will integrate the DCNN-LSTM network with elastic module fusion, which accurately determines bearing displacements, identifying possible structural changes caused by temperature variations. Through real-time simulations and analyses, a continuous and precise evaluation of the entire system is generated, identifying potential risks and mitigation methods. Implementing warning systems built using prediction data provided by the Digital Twin facilitates continuous communication with intervention and management teams. The data analysis capability of collected sensor data and their integration into artificial intelligence algorithms contribute to optimizing maintenance and repair strategies, extending the bridge's lifespan. The Digital Twin will be continuously updated by real-time sensor data collection and modeling, ensuring highly accurate results. Smart city Digital Twins were addressed in [110], where the functional integration of data from Building Information Modeling (BIM) and Geographic Information Systems (GISs) was presented, as well as the realization of experiments regarding data conversion between the two systems. Data integration has been developed to achieve semantic integration, functional interoperability, and information sharing. Geographic Information System (GIS) data provide cities with geospatial data, spatial analysis, and applications that are very important for urban environment design. Building Information Modeling (BIM) involves the active application of models to the engineering, architecture, and construction industries throughout the life cycle, along with the passive presentation of micro-digital information regarding real entities. The sustainable design of smart cities can be achieved through the support of urban DT technology by combining the two technologies, GIS and BIM. In the field of Architecture, Engineering, Construction, and Operation (AECO), [PERSON] et al. [111] highlighted the need for detailed studies to implement and identify platforms that facilitate interoperability and efficient collaboration among involved stakeholders. This stems from the importance of finding a common language for data acquisition,storage, transmission, and modeling. In the preservation of Built Cultural Heritage (BCH), the authors emphasize that the integration of the Digital Twin is essential. The use of the Digital Twin involves not only the use of specialized simulation software but also the application of predictive analyses through statistical methods and artificial intelligence tools. From this perspective, the need for continuous collaboration among experts from various scientific fields to implement concrete case studies is emphasized. These studies will form the basis for evaluating and validating holistic approaches aimed at developing a complex and integrated perspective on performance-oriented management. In ref. [112], [PERSON] et al., highlighted the necessity of defining a methodological framework for implementing the Digital Twin in the conservation of Mobile Cultural Assets (BCH), aiming for its full potential exploitation. Additionally, the importance of conducting a systematic comparison between the concepts of Digital Twin and HBIM (Historic Building Information Modeling) was emphasized, thereby supporting the advancement of practical implementations in the field of structural engineering and BCH conservation.