AEC Digital Twin Data - Why Structure Matters

André Borrmann, Jonas Schlenger, Nicolas Bus, Rafael Sacks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the increasing adoption of the Digital Twin concept in the construction industry in the operations and maintenance phase, researchers and practitioners are increasingly seeking suitable technological solutions for the design and construction phases. While it is widely accepted that the required platforms hosting the digital twin must be cloud-based to fulfill the requirements of ubiquitous accessibility and centralized consistency, questions regarding the need for data schema remain. Some academics argue that a structure-free organization of data is suitable for realizing digital twins and the data streams from and to the respective platform. Hands-on experience in the BIM2TWIN project supports a counter argument, i.e., that structure-free data is insufficient for most use cases around AEC Digital Twins. The sheer information complexity of construction projects requires well-defined data structures enabling unambiguous and error-less interpretation. This becomes apparent when reflecting on the well-established concept of the data-information-knowledge pyramid describing that raw data must be processed into understandable and meaningful high-level information for human decision makers, subsequently providing the basis for cross-project domain knowledge. Based on this observation, we highlight that object-oriented modeling is a widely recognized information modeling technique that facilitates the structuring of complex domain information. We compare it with ontology-based model concepts that provide a similar, yet more abstract means for information modeling.

Original languageEnglish
Title of host publicationAdvances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1
EditorsSebastian Skatulla, Hans Beushausen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages651-669
Number of pages19
ISBN (Print)9783031353987
DOIs
StatePublished - 2024
Event19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022 - Cape Town, South Africa
Duration: 26 Oct 202228 Oct 2022

Publication series

NameLecture Notes in Civil Engineering
Volume357
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022
Country/TerritorySouth Africa
CityCape Town
Period26/10/2228/10/22

Keywords

  • Data Model
  • Digital Twin
  • Information Model
  • Object-oriented Modeling
  • Ontology

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