CoDEC: Connected Data For Road Infrastructure Asset Management
Abstract Road infrastructure asset management is rapidly transforming into a digital environment where data accessibility, effective integration and collaboration and accessibility from different sources and assets are key. However, current asset management processes are not yet fully integrated or...
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Published in | IOP conference series. Materials Science and Engineering Vol. 1202; no. 1; pp. 12002 - 12011 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Bristol
IOP Publishing
01.11.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Abstract
Road infrastructure asset management is rapidly transforming into a digital environment where data accessibility, effective integration and collaboration and accessibility from different sources and assets are key. However, current asset management processes are not yet fully integrated or linked, and there are incompatibilities between various systems and platforms that limit the ability to integrate asset management with BIM. The CoDEC project has sought to understand the current status of information management for assets, including inventory, condition and new data sources such as sensors and scanning systems, to identify the challenges and needs for linking and integrating different data sets to support effective asset management. As a result, CoDEC has developed a data dictionary framework to help link/integrate static and dynamic data for the “key” infrastructure assets (road pavements, bridges, tunnels). This will enable BIM and Asset Management Systems (AMS) to exchange data and help optimise and integrate data management across systems and throughout the different asset lifecycle phases, from build to operation. This work will be followed up with three pilot projects to demonstrate the feasibility of integrating asset data from various sources through linked data/semantic web technology to build the connection between AMS and BIM platforms. |
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ISSN: | 1757-8981 1757-899X |
DOI: | 10.1088/1757-899X/1202/1/012002 |