Transitioning from BIM to Digital Twin to Metaverse

In the Architecture, Engineering, and Construction (AEC) sector, Building Information Modelling (BIM) has been at the forefront of the digital revolution. BIM makes it easier to create detailed digital models that capture a building�s functional and physical attributes.Throughout the project lifecycle, these models allow stakeholders to exchange insights and make better-informed decisions. But as company demands have increased, likewise has the demand for more advanced digital tools.Digital Twin is a real-time, dynamic digital clone of a system, process, or physical item. Digital Twins combine real-time data streams from sensors implanted in physical assets, in contrast to BIM, which primarily concentrates on static models, enabling continuous monitoring, simulation, and analysis. Digital twins have been used in a variety of ways, providing unprecedented opportunities to improve decision-making and operational effectiveness. For instance, DTs allow for real-time energy usage monitoring, maintenance requirement prediction, and space optimization in smart buildings. Facility managers may reduce downtime and increase the lifespan of building systems by using distributed testing (DT) to identify and prevent possible problems before they arise by modelling various scenarios. Digital Twins provides an entire perspective of assets in infrastructure management, enabling improved operating, maintenance, and planning methods. The quality and dependability of these digital replicas are enhanced by the integration of DTs with current BIM models, which makes them essential tools for contemporary asset management. Digital Twin adoption in the AEC sector is not without obstacles, despite its benefits. The primary hurdle is the lack of compatibility across various software platforms and systems. To be valid, DTs need to work well with other digital tools, IoT devices, and current BIM models�many of which run on different platforms. Privacy and data security are also major issues. Since Digital Twins depend on real-time data, it is critical to preserve sensitive data. The industry now lacks standardization, which is made necessary by the complexity of digital systems growing