ACTIONABLE FRAMEWORK for CITY DIGITAL TWIN-ENABLED PREDICTIVE MAINTENANCE and SECURITY MANAGEMENT SYSTEMS

Abstract

The growing potential offered by the use of ICT (Information Communication Technology)-based approaches for process management in the built environment is increasingly used to configure effective digital twins of products and processes. Focusing on Smart Cities, the realization of integrated three-dimensional BIM (Building Information Modeling) and GIS (Geographic Information Systems) provides microscopic and macroscopic geometric databases containing static, dynamic, geometric and semantic data representing the information centre for an efficient management of the lifecycle in vertical and horizontal systems. Therefore, processing data and information about the lifecycle of city assets through a Digital Twin Model ensures considerable support to the process management of built environments. Through the integration of these models with Artificial Intelligence (AI) systems it becomes possible to increase the optimization and progressive functional automation of the activities interconnected to the assets’ lifecycle. The proposed research presents a full-digital actionable framework based on pervasive and ubiquitous systems for security and facility management based on City Digital Twins. An integrated digital system for security management is introduced through the use of self-learning systems for unmanned security based on image recognition and AI. Finally, even maintenance operations can benefit from the configuration of predictive maintenance systems aimed at reducing costs and failures through a digital information management system combined with analytics based on data flows from sensors and historical data.