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Intelligent and Digital Technologies in the Construction Objects Technical Diagnostics

Author(s):


Medium: journal article
Language(s): Russian
Published in: International Journal for Computational Civil and Structural Engineering / Международный журнал по расчету гражданских и строительных конструкций, , n. 2, v. 17
Page(s): 22-33
DOI: 10.22337/2587-9618-2021-17-2-22-33
Abstract:

The intelligent and digital technologies implementation into the civil engineer expert activities is able to provide alternative solutions to different qualifications specialists. This will increase the speed of data processing and the reliability of the expert opinion on the technical condition of the operated construction objects as well as will allow assessing their real residual resource for making a decision on the possibility of further exploitation. The ontology and the original technology for the main system components determination are necessary to achieve the goal (the confinement model) used for the structural and functional analysis of the knowledge system. The entire technologies set for the architectural and construction objects (digital documentation, scan results, thermal imager data, non-destructive survey methods data), data about structure’s defects and damage, appropriate software as well as intelligent technologies (fuzzy logic, neural networks) are used for more thoroughly diagnose certain construction parts and transmit digital information to determine the technical condition category. Also, this data set can be used for following situations: a control of the dynamics of changes in the technical state of a construction object, an improvement the accuracy of determining the scope of repair work, an enhancement the quality of project documentation, methods for assessing the quality of restoration work and measures for the conservation of architectural monuments, etc.

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: 10.22337/2587-9618-2021-17-2-22-33.
  • About this
    data sheet
  • Reference-ID
    10613657
  • Published on:
    09/07/2021
  • Last updated on:
    09/07/2021
 
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