Construction and maintenance of urban underground infrastructure with digital technologies

Urban underground infrastructure is a critical component in cities to provide essential services to residents. Research efforts have been made to facilitate different activities of underground infrastructure projects using various methods, particularly digital technologies. To obtain deeper insights from existing research and provide directions for future research, this study conducts a comprehensive review of research on underground infrastructure construction and Operation & Maintenance (O&M) with a focus on digital technologies. The in-depth review was conducted based on 145 publications from the perspective of locating and mapping, construction and coordination, as well as O&M. Consequently, critical limitations and challenges are revealed, such as the lack of as-built and as-is information, the requirement of data quality and quantity for deep learning methods, the lack of fully automated robotic systems, etc. Afterwards, a status matrix was presented to identify the level of different digital technologies being studied and their future application potential for key activities of underground infrastructure projects. In the end, future research trends are proposed, including (1) digital twinning of underground infrastructure, (2) quality and uncertainty of inspection data, (3) data generation and semi-supervised learning, (4) predictive maintenance, and (5) fully automated robotic systems for inspection and maintenance. This study contributes to the body of knowledge by identifying the challenges and limitations of existing studies through a systematic review, providing a clear view of the achievements and potentials of digital technologies for underground infrastructure, and proposing future research directions to facilitate digital transformation in this area.

• Record URL:
  https://doi.org/10.1016/j.autcon.2022.104464
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0926580522003375
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
• Supplemental Notes:
  • © 2022 The Authors. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Mingzhu
  • Yin, Xianfei
• Publication Date: 2022-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 104464
• Serial:
  • Automation in Construction
  • Volume: 141
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0926-5805
  • Serial URL: http://www.sciencedirect.com/science/journal/09265805

Subject/Index Terms

• TRT Terms: Infrastructure; Literature reviews; Maintenance; Underground construction; Urban areas
• Subject Areas: Construction; Data and Information Technology; Maintenance and Preservation;

Filing Info

• Accession Number: 01858704
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 23 2022 1:13PM