# Seeking a Definition of Digital Twins for Construction and Infrastructure Management
## 3 Digital Twins Current State of the Art
### Digital Twin Development Frameworks
Users can modify and extend the framework to meet their specific requirements, ensuring suitability for unique system needs. However, challenges such as a steep learning curve and the necessity for familiarity with Vortolang and the Eclipse modelling framework may arise. Additionally, integrating Ditto with diverse systems may demand additional effort to ensure compatibility and performance. Despite these challenges, Ditto's extensive features in state management, access control, and integration make it a robust and valuable framework for creating DTs in construction, BIM, smart cities, and Industry 4.0 applications. By leveraging these facilities, Eclipse Ditto enables the development of sophisticated DT solutions that enhance operational efficiency, predictive maintenance, and real-time monitoring across various domains. Creating a DT with Eclipse Ditto involves several systematic steps. Initially, Vortolang is employed to define the digital twin's properties and operations through information models and function blocks. Integration follows, connecting the DT to physical devices via protocols, such as AMQP, Kafka, and MQTT, ensuring robust data transfer and control. State management is crucial, defining and managing the reported, desired, and current states to ensure synchronisation and the publication of state changes. This allows for precise system control and monitoring, enabling real-time adjustments and predictive maintenance. Secure access control is implemented using Ditto's fine-grained, resource-based access control, securing interactions with the DTs. Finally, visualisation and control are facilitated through Ditto's RESTful web services, enabling real-time monitoring, control, and optimisation of operations. For instance, in a smart city context, this might involve adjusting traffic signals based on real-time data to reduce congestion. OpenTwins [33] is an open-source framework engineered for the development of next-generation compositional DTs, facilitating the creation of sophisticated DTs that interlink individual entities or subsystems into a cohesive higher-order DT. This approach enhances knowledge sharing data relationships, enabling seamless integration with 3D visualisations, IoT data streams, and real-time machine learning predictions. Built on a scalable platform for fault tolerance and high availability, OpenTwins supports the continuous development and integration of DTs, making it adaptable for diverse contexts, including Industry 4.0, construction, and smart cities. In the domain of Industry 4.0, OpenTwins offers substantial benefits by enabling the composition of intricate DTs that can monitor, predict, and optimise various manufacturing processes. This capability is particularly evident in use cases, such as the petrochemical industry, where OpenTwins has been employed to develop a virtual analyser predicting the freezing point of lubricants based on real-time operational data. This application underscores the framework's potential to enhance process control and operational efficiency through continuous monitoring and real-time predictions. OpenTwins excels in the integration of AI and machine learning, providing seamless orchestration with data streams for ongoing optimisation and prediction. The framework incorporates Kafka-ML, an open-source tool for managing the lifecycle of ML/AI applications with continuous data streams. This integration is pivotal for enabling predictive analytics and real-time decision-making in DTs, further augmenting their capabilities. Another notable feature of OpenTwins is its support for 3D visualisation, achieved through integration with Unity. This capability allows for the interactive 3D representations of DTs, offering a more intuitive and comprehensive understanding of the monitored assets. The ability to visualise real-time and historical data in a 3D format enhances the user experience and facilitates superior decision-making. The framework is architected with a microservice approach, increasing modularity, scalability, and reusability.