A study of the coupling of physical and digital assets and resources across the lifecycle stages of the built environment.

Abstract

The rapid digitization of the built assets industry is driving a closer coupling of physical and digital asset and resource information. This coupling of information enables stakeholders to unlock opportunities for considerable gains in terms of performance and the value generated by integrated information management. In the context of cyber-physical systems (CPS) and digital twins (DT), a growing number of studies are focusing on the technical aspects of CPS and DT. However, there are as yet no common definitions, dimensions, characteristics or concepts for linking life cycle information. To take full advantage of these concepts, new constructs, principles and mechanisms are needed to define and relate the various elements that make up the coupling of physical and digital asset and resource lifecycle information. This research project aims to fill existing coupling gaps and problems with a taxonomy of built asset lifecycle information pairs and a framework for instantiating coupling actions that provide the dimensions and characteristics framing the physical-digital coupling of assets and resources in the built asset industry. The proposed taxonomy and its instantiation framework contribute to the effort to structure the knowledge domain of life cycle information management by coupling the physical and digital worlds in the built environment.

Areas of research : Artificial intelligence, Digital twin.

Key words : Digital twins, Information management, Lifecycle management.

Project results

A taxonomy of the coupling of information on the life cycle of built assets and resources has been proposed in order to identify the dimensions and characteristics of coupling needed to conceptualize asset coupling in the built asset industry. The taxonomy proposes eight high-level dimensions covering various aspects of information coupling. The proposed dimensions are: information pairs, coupling states, coupling results, coupling impacts, coupling actions, coupling measures and coupling facilitators. Dimensions and their characteristics can be used to reduce the complexity of asset coupling and enable stakeholders to unlock the value generated by integrated information management. They also support the effective implementation of digital information technologies throughout the asset lifecycle. In order to operationalize and instantiate the key dimensions of the proposed taxonomy, a framework for instantiating coupling actions has been developed as a design product of this research.

The framework has been articulated around the dimensions of the taxonomy and the main data categories that highlight significant data for coupling actions. In addition, a coupling mechanism has been proposed to extract meaningful data from potential information containers and link them to targeted, constructed coupling actions. This mechanism served as a matrix for the integration of data into the defined coupling actions, in order to generate various types of information pairs across the life-cycle stages. Evaluation of the proposed taxonomy and its instantiation framework has demonstrated that a large volume of dispersed data collected from physical assets can potentially be harmonized and coupled with their digital assets at different states of information. Systematically organizing this data and linking it to physical assets can improve collaboration between parties and enable stakeholders to make more informed decisions about the asset’s actual condition.

Project contributions

In response to the current trend towards digital matching of physical and digital built assets, the proposed artifact was not intended to replace any given taxonomy in the context of PCS. Instead, it has extended and framed existing concepts and theories in practice while addressing current asset coupling issues in the built goods industry. The use of taxonomy features can help decision-makers identify which data needs to be linked at the earliest stages of a project. The result is better information management, reduced time and costs during the construction process, and efficient extraction of data from built assets for reuse or recycling. Indeed, system automation relies heavily on taxonomies. Without taxonomy, it would be difficult to identify what is needed for automation.