Abstract
Functionality and serviceability of surface and buried infrastructure play a fundamental role in modern living standards. A substantial portion of urban utilities are buried under the pavements; replacement and maintenance of which require extensive streetworks with both short- and long-term costs. This study proposes an asset management framework to estimate the direct and indirect costs of trenching operations using digital technologies including condition monitoring, image processing, big data collection, and analysis using machine learning techniques. The framework’s cost model element was initially developed using the existing literature, which would then be updated with collected data. This would allow the asset owners to estimate the long-term costs of the trenching method to inform their decision making. Application of the existing technologies was demonstrated through a case study. The framework can ultimately inform data-driven decision-making and supporting sustainable and effective urban infrastructure management practices.
Original language | English |
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Title of host publication | Geo Congress 2024 |
Subtitle of host publication | Geotechnical Data Analysis and Computation |
Editors | T. Matthew Evans, Nina Stark, Susan Chang |
Publisher | American Society of Civil Engineers (ASCE) |
Pages | 464-474 |
Number of pages | 11 |
ISBN (Electronic) | 9780784485347 |
Publication status | Published - 22 Feb 2024 |
Event | Proceedings of Geo-Congress 2024: Geotechnical Data Analysis and Computation - Vancouver, Canada, Vancouver, Canada Duration: 25 Feb 2024 → 28 Feb 2024 https://ascelibrary.org/doi/book/10.1061/9780784485347 |
Publication series
Name | Geo-Congress 2024 |
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Publisher | American Society of Civil Engineers (ASCE) |
Number | 352 |
Conference
Conference | Proceedings of Geo-Congress 2024: Geotechnical Data Analysis and Computation |
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Country/Territory | Canada |
City | Vancouver |
Period | 25/02/24 → 28/02/24 |
Internet address |