A Comprehensive Data Schema for Digital Twin Construction

Schlenger, Jonas; Yeung, Timson; Vilgertshofer, Simon; Martinez, J.; Sacks, R.; Borrmann, André

doi:10.7146/aul.455.c194

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Darüber hinaus erlauben Graphdatenbanken die flexible Anpassung des zugrunde liegenden Schemas [12]. Zwar können Graphdatenbanken auch ohne Schema verwendet werden, jedoch sehen die Autoren große Vorteile in der zielgerichteten Abfrage von klar strukturierten Graphen [13, 14]. Aus den Informationen gewonnenes Wissen sind für gewöhnlich zeitabhängige KPIs, welche in einer Zeitreihendatenbank effizient abgespeichert werden können.…”

Section: Erfahrungsbericht Baustellenüberwachungunclassified

Digitaler Zwilling Baustelle – Baustellenüberwachung zur automatisierten Zeit‐ und Kostenkontrolle

et al. 2023

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Während in der Produktions-und Fertigungsbranche bereits mit hoch automatisierten Verfahren gearbeitet wird, tut sich die Bauindustrie schwer, den technologischen Fortschritt zu nutzen [1]. Das begünstigt Ungenauigkeiten bei der Fortschrittskontrolle, was unweigerlich zu Verzögerungen im Bauprojekt führt.Ein etabliertes Konzept, welches v. a. in der Fertigungsindustrie zu einem hohen Grad an Automatisierung geführt hat, ist der Digitale Zwilling. Diese Technologie basiert darauf, ein digitales Abbild der Realität zu schaffen. Der Begriff Digitaler Zwilling wurde erstmalig 2003 von Michael Grieves als "digitales Abbild eines physischen Objektes" verwendet [1,2]. In den letzten 20 Jahren gab es zahlreiche Entwicklungen und Anwendungsfälle dieses Konzepts, der Kern ist jedoch gleich geblieben: Durch den Digitalen Zwilling entsteht eine Wechselwirkung zwischen der physischen und der digitalen Welt. Infolgedessen entstehen viele neue Perspektiven, bspw. beim Einsatz während der Bauausführung, wie in diesem Bei-trag vorgestellt wird. Den Kern bildet dabei die automatisierte Baufortschrittsüberwachung.

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Section: Erfahrungsbericht Baustellenüberwachungunclassified

Digitaler Zwilling Baustelle – Baustellenüberwachung zur automatisierten Zeit‐ und Kostenkontrolle

et al. 2023

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“…The information extraction is increasingly performed using machine learning methods [20]. This information is essential to represent the as-performed construction state by a digital twin [14]. Studies that provide insights into the construction phase based on real-world construction sites are scarce.…”

Section: Construction Monitoringmentioning

confidence: 99%

“…These are classified into four different classes according to their current status to identify the operational steps. To do so, the approach developed by [13] is applied, which is described in further detail in the following paragraph.…”

Section: Fig 2: Object Detection Pipelinementioning

confidence: 99%

“…Based on the expected sequence of statuses, the transitions points from one status to the next are identified and then used to correct the status predictions of the CNN. As the final result, the start and end times of every construction phase of the pillar are provided [13]. Even though this gives more insights into the construction process, it still does not allow to directly measure the time that construction workers actively spent working on individual building elements.…”

Section: Fig 2: Object Detection Pipelinementioning

confidence: 99%

See 1 more Smart Citation

Automated Productivity Evaluation of Concreting Works: The Example of Concrete Pillar Production

Pfitzner,

Schlenger,

Borrmann

2023

Lecture Notes in Civil Engineering

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Site schedules are usually developed by the rule of thumb based on the experience of on-site managers. While this approach can be suitable for smaller job sites, it is challenging to make good decisions for larger projects. Planning errors can result in massive delays and increasing costs. Significant improvements in other industries showed that data-driven productivity analysis of past processes advances the planning and execution of current and future projects. However, in the Architecture, Engineering & Construction (AEC) domain, automated productivity analysis of the construction phase has barely been investigated. To overcome this deficiency, this paper presents a first approach for multi-level productivity analysis of shell constructions. We discuss several state-of-the-art vision-based technologies that serve as a foundation for large-scale evaluation of the progress on a construction site. A complete pipeline is introduced that uses different types of neural networks to extract productivity information from images at various levels of detail. The proposed workflow is demonstrated for the construction process of cast-in-place concrete pillars, implementing the first two layers. Finally, remaining challenges are discussed.

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“…To support our argument, we refer to the ongoing research project BIM2TWIN which aims to provide comprehensive DT representation of constructions projects combining the process and the product view (Schlenger et al 2022). We show how a wellstructured data model helps to reduce complexity and allows to perform the high-level analysis tasks required for decision making.…”

Section: Introductionmentioning

confidence: 99%

AEC Digital Twin Data - Why Structure Matters

Borrmann,

Schlenger,

Bus

et al. 2023

Lecture Notes in Civil Engineering

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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 errorless 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 crossproject domain knowledge. Based on this observation, we highlight that objectoriented 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.

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A Comprehensive Data Schema for Digital Twin Construction

Cited by 8 publications

References 10 publications

Digitaler Zwilling Baustelle – Baustellenüberwachung zur automatisierten Zeit‐ und Kostenkontrolle

Digitaler Zwilling Baustelle – Baustellenüberwachung zur automatisierten Zeit‐ und Kostenkontrolle

Automated Productivity Evaluation of Concreting Works: The Example of Concrete Pillar Production

AEC Digital Twin Data - Why Structure Matters

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