From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry

Abstract

The widespread adoption of Building Information Modeling (BIM) and the recent emergence of Internet of Things (IoT) applications offer several new insights and decision-making capabilities throughout the life cycle of the built environment. In recent years, the ability of real-time connectivity to online sensors deployed in an environment has led to the emergence of the concept of the Digital Twin of the built environment. Digital Twins aim to achieve synchronization of the real world with a virtual platform for seamless management and control of the construction process, facility management, environment monitoring, and other life cycle processes in the built environment. However, research in Digital Twins for the built environment is still in its nascent stages and there is a need to understand the advances in the underlying enabling technologies and establish a convergent context for ongoing and future research. This paper conducted a systematic review to identify the development of the emerging technologies facilitating the evolution of BIM to Digital Twins in built environment applications. A total of 100 related papers including 23 review papers were selected and reviewed. In order to systematically classify the reviewed studies, the authors developed a five-level ladder categorization system based on the building life cycle to reflect the current state-of-the-art in Digital Twin applications. In each level of this taxonomy, applications were further categorized based on their research domains (e.g., construction process, building energy performance, indoor environment monitoring). In addition, the current state-of-art in technologies enabling Digital Twins was also summarized from the reviewed literature. It was found that most of the prior studies conducted thus far have not fully exploited or realized the envisioned concept of the Digital Twin, and thus classify under the earlier ladder categories. Based on the analysis of the reviewed work and the trends in ongoing research, the authors propose a concept of an advanced Digital Twin for building management as a baseline for further studies.

Construction 4.0: what we know and where we are headed?

“…A key aspect of facilities management is the sanctity of the digital data layer to that of the physical layer. Such synchronization of the digital and physical world is achieved through controlled feedbacks, optimization, and interactions between the digital twin model and the constructed asset (Deng, Menassa and Kamat, 2021). Several researchers have proposed different frameworks to implement digital twins in the construction 4.0 paradigm.…”

A Conceptual Framework for Estimating Building Embodied Carbon Based on Digital Twin Technology and Life Cycle Assessment

“…From a static BIM to a web-based digital twin, Deng et al [51] developed a five-level ladder taxonomy. The five levels are Level 1-BIM; Level 2-BIM-supported simulations; Level 3-BIM integrated with IoT; Level 4-BIM integrated with AI for predictions; Level 5-ideal digital twins, including not only real-time visualization and prediction but also automatic feedback and control of the built environment.…”

Digital Twin conceptual framework for improving critical infrastructure resilience

“…Digital Twins have been applied to several types of infrastructures, such as transportation [30,7], buildings [31], energy [30,7], and water [32]; mainly in the context of city, smart city, or building information modeling (BIM) [6,33,30,7].…”

“…In the domain of supply chains, Digital Twins have been discussed as a tool to increase supply chain performance and resilience [37]. The corresponding data structure and modeling framework of a 'digital supply chain twin' follows four major methodological principles [31], which show similarities to resilience capacity design: 1) decision-making support as a viable system model comprised of pre-disruption, disruption, and post-disruption stages -basically following the resilience cycle as described in Section 2.3; 2) integration of physical and cyber data sources with online supply chain modeling -formulating the twinning requirement of a Digital Twin; 3) supply chain models as an integration of physical and cyber networks -modeling supply chains as complex networked systems; 4) data-driven supply chain risk analytics systems support the use of data for learning and disruption pattern recognition -integrating a learning component.…”