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Trustworthy digital twinning data platform for power infrastructure construction projects using blockchain and semantic web

Author(s):
Medium: journal article
Language(s): English
Published in: Frontiers in Built Environment, , v. 10
DOI: 10.3389/fbuil.2024.1440513
Abstract:

Power infrastructure projects are characterized by complex supply chain structures and numerous stakeholders, presenting significant challenges in maintaining data integrity and ensuring seamless integration of project information. Previous Digital Twins (DTs) and Building Information Modeling (BIM) collaboration methods lack robust mechanisms for data traceability and immutable storage, leading to potential risks such as data loss or tampering. Furthermore, existing project information exchange and data management methods do not adequately integrate diverse data types, such as project documentation, onsite environment monitoring IoT sensor readings and CAD/BIM-based design information. This research introduces a novel DT data platform prototype, utilizing Blockchain and Semantic Web technologies, to establish a trustworthy DT data environment for power infrastructure projects. This system collects heterogeneous data, including manual inputs and IoT-generated data, and processes them into RDF format on dedicated devices. The integrated data is then stored on a Permissioned Blockchain, ensuring traceability and immutability. The framework incorporates Distributed File Systems to enhance storage efficiency and features a semantic gateway that transforms heterogeneous data into RDF graphs, fostering interoperability and the potential for automated data linkage. The efficacy of this prototype was demonstrated through a case study, testing data consistency and showcasing prototype queries enhanced by Semantic Web, thus substantiating the platform’s capacity to support multidisciplinary project management.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3389/fbuil.2024.1440513.
  • About this
    data sheet
  • Reference-ID
    10812641
  • Published on:
    17/01/2025
  • Last updated on:
    17/01/2025
 
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