Abstract

Abstract. In this paper we address the issue of unreliable subsurface utility information. Data on subsurface utilities are often positionally inaccurate, not up to date, and incomplete, leading to increased uncertainty, costs, and delays incurred in underground-related projects. Despite opportunities for improvement, the quality of legacy data remains unaddressed. We address the legacy data issue by making an argument for an approach towards subsurface utility data reconciliation that relies on the integration of heterogeneous data sources. These data sources can be collected at opportunities that occur throughout the life cycle of subsurface utilities and include as-built GIS records, GPR scans, and open excavation 3D scans. By integrating legacy data with newly captured data sources, it is possible to verify, (re)classify and update the data and improve it for future use. To demonstrate the potential of an integration-driven data reconciliation approach, we present real-world use cases from Denmark and Singapore. From these cases, challenges towards implementation of the approach were identified that include a lack of technological readiness, a lack of incentive to capture and share the data, increased cost, and data sharing concerns. Future research should investigate in detail how various data sources lead to improved data quality, develop a data model that brings together all necessary data sources for integration, and a framework for governance and master data management to ensure roles and responsibilities can be feasibly enacted.

Highlights

  • Driven by a persistent and growing need to develop infrastructure above and below the surface, planners, engineers and contractors rely on information on the presence and location of unseen subsurface utilities

  • Standards and guidelines such as the Specifications for Utility Survey in Singapore (Singapore Land Authority, 2017) describe how utilities are recorded in absolute positions and with predefined positional accuracies. They prescribe the techniques, observation standards, or competencies and skills required to ensure that location information is captured with sufficient accuracy and the data attributes that are to be provided. Such improvements address the recording of utilities directly after being built - typically when they are still exposed and direct or line-of-sight observations are possible - and do not cover the recording of pre-existing infrastructure, leaving legacy data quality issues unaddressed

  • 3.1.2 3D ground penetrating radar data capture of large areas: Ground penetrating radar (GPR) is a non-destructive technique that can be used to detect and locate subsurface utilities using electromagnetic waves that are sent into the ground

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Summary

THE NEED FOR RELIABLE INFORMATION ON SUBSURFACE UTILITIES

Driven by a persistent and growing need to develop infrastructure above and below the surface, planners, engineers and contractors rely on information on the presence and location of unseen subsurface utilities. The degree of quality is typically unknown, leading to increased uncertainty, costs, and delays incurred by infrastructure projects due to the need for verification. Programs and platforms such as the Danish Register of Underground Cable Owners (LER) in Denmark (SDFE, 2021), the Cables and Pipes Information Centre (KLIC) in The Netherlands (Kadaster, 2021), and the National Underground Asset Register in the United Kingdom (Geospatial Commission, 2020) have been established to make data on subsurface utilities available in a standardised, digital format, addressing data availability and uniformity. While legislative instruments may specify the required accuracy of utility records, it is unclear how compliance to such requirements is verified or how data owners can improve the accuracy of their data, in particular for legacy data representing utilities that were installed in the past

LEGACY RECORDS
INTEGRATION OF HETEROGENEOUS DATA SOURCES
Cases of potential utility data sources
CHALLENGES IDENTIFIED IN PRESENTED CASES
CONCLUSSION AND FUTURE WORK
Investigate specific data quality improvement scenarios
Development of a data model
Development of a framework for governance and master data management

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