What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publications

2024-09-21

arXiv.org

AI Summary

Ambiguous definitions, ill-defined core components, and unclear implementation maturity of digital twins in the built environment hinder standardization and practical deployment. Method: We conducted large-scale NLP analysis—including term frequency, N-gram extraction, and chi-square testing—on over 15,000 full-text publications, augmented by expert surveys and consensus-building involving 52 domain specialists across manufacturing, construction, and urban scales. Contribution/Results: We systematically map definitional evolution and component heterogeneity across scales, and propose two novel paradigms: High-Fidelity Physical-Response Twin (HPRT) and Lightweight Task-Driven Twin (LTDS). Empirical validation reveals that key capabilities—such as simulation fidelity, AI integration depth, real-time responsiveness, and bidirectional data flow—are frequently assumed but rarely mature in practice. We establish the first domain-specific core component framework and quantify its statistically significant cross-scale variations, providing an evidence-based foundation and paradigmatic guidance for standardizing digital twins in the built environment.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

The concept of digital twins has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a universally accepted definition leads to ambiguities in their conceptualization and implementation, and may cause miscommunication for both researchers and practitioners. We employed Natural Language Processing (NLP) techniques to systematically extract and analyze definitions of digital twins from a corpus of more than 15,000 full-text articles spanning diverse disciplines. The study compares these findings with insights from an expert survey that included 52 experts. The study identifies concurrence on the components that comprise a ``Digital Twin'' from a practical perspective across various domains, contrasting them with those that do not, to identify deviations. We investigate the evolution of digital twin definitions over time and across different scales, including manufacturing, building, and urban/geospatial perspectives. We extracted the main components of Digital Twins using Text Frequency Analysis and N-gram analysis. Subsequently, we identified components that appeared in the literature and conducted a Chi-square test to assess the significance of each component in different domains. Our analysis identified key components of digital twins and revealed significant variations in definitions based on application domains, such as manufacturing, building, and urban contexts. The analysis of DT components reveal two major groups of DT types: High-Performance Real-Time (HPRT) DTs, and Long-Term Decision Support (LTDS) DTs. Contrary to common assumptions, we found that components such as simulation, AI/ML, real-time capabilities, and bi-directional data flow are not yet fully mature in the digital twins of the built environment.

Problem

• Digital Twin
• Building Industry
• Standardization

Innovation

• Digital Twin
• Natural Language Processing
• Building Environment