About this Research Topic

Background

Cyber-physical systems (CPS) comprising interconnected and integrated smart systems can transform the Architecture Engineering Construction (AEC) industry and contribute to the development of Construction 4.0. Digital Twin (DT) construction is a new mode for managing production in construction that leverages the data streaming from a variety of site monitoring technologies and artificially intelligent functions to provide accurate status information and to proactively analyze and optimize ongoing design, planning, and production. DT integrates historical and real-time data obtained from physical systems with physics-based models and advanced analytics to create digital counterparts with high integrity, awareness, and adaptability to provide predictive services to planning and construction entities. It enhances transparency and feasibility of functions in CPS and facilitates real-time monitoring, simulation, optimization, and control of cyber-physical elements. A DT based CPS should constantly acquire, integrate, analyze, simulate, and synchronize data across multiple stages of the product life cycle to provide on-demand predictive services to different users in both physical and cyber spaces.

Although the current level of advancement in enabling technologies and the state-of-the-art applications and implementations of DT in the AEC industry regarding real-time monitoring, performance prediction, and decision-making, there still exist several key knowledge gaps that must be addressed through continued research to make DT more capable, reliable, and practical for real-world applications. Even if a considerable amount of engineering data increases unprecedently in the planning and construction of a project, the adoption of DT, IoT and AI techniques still lag behind the process in other industries. Therefore, there is immense interest in implementing a variety of DT, IoT and AI methods in the construction engineering and management domain to seize the valuable opportunity of digital evolution for better performance and profitability.

This Research Topic provides knowledge into Cognitive DT models for facilitating Construction 4.0 focusing on challenges and opportunities. This article collection comprises DT incorporating cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making in smart planning and construction. This Research Topic introduces the Knowledge Graph (KG)-centric framework for AI enhanced DT involving process modeling and simulation, ontology-based Knowledge Graph, analytics for process optimizations, and interfaces for data operability. This article collection offers contributions of AI enhanced DT for the incorporation of IoT, Big data, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities.

This Research topic offers the essential material to enlighten pertinent research communities of the state-of-the-art research and the latest development in the area of Cognitive Digital Twins. Scholars and practitioners are encouraged to submit original research articles, and systematic and evidence-based reviews and case studies on topics including, but not limited to, the following:
- Challenges and opportunities for developing Cognitive Digital Twins in Construction 4.0
- AI enhanced Digital Twins Towards Construction 4.0
- Cognitive Modelling for Enhancing Digital Twins in Construction 4.0
- Cognitive Digital Twins for System of Systems in Construction 4.0
- Ontology and Knowledge Graph Modelling in Cognitive Digital Twins
- Big Data for Cognitive Digital Twins in Construction 4.0
- Cognitive Digital Twins for Building Lifecycle Management
- Use Cases of Cognitive Digital Twins in Built Environment and Infrastructure.

Keywords: Cognitive Digital Twins, Artificial Intelligence, Simulation, Optimization, Construction 4.0

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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