ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume VI-4/W1-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W1-2020, 69–76, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-69-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W1-2020, 69–76, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-69-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Sep 2020

03 Sep 2020

DATA MODELING FOR OPERATION AND MAINTENANCE OF UTILITY NETWORKS: IMPLEMENTATION AND TESTING

F. Fossatti1, G. Agugiaro2, L. olde Scholtenhuis1, and A. Dorée1 F. Fossatti et al.
  • 1Department of Construction Management & Engineering, University of Twente, The Netherlands
  • 23D Geoinformation group, Department of Urbanism, Faculty of Architecture & the Built Environment, Delft University of Technology, The Netherlands

Keywords: Utility Networks, CityGML, Utility Network ADE, Operation & Maintenance

Abstract. The organisational data models that support the information needs of utility network managers are proprietary and domain-specific, while the emerging national standards in this field often lack lifecycle data representation capabilities. However, multiple types of utility networks can be comprehensively represented with the free and open-source Utility Network Application Domain Extension (ADE) of the international standard CityGML. The Operation & Maintenance (O&M) Domain Ontology is a proposed extended version of the Utility Network ADE that allows for consistent and comprehensive processing, storage and exchange of O&M-related utility network data. So far, this ontology has not yet been implemented in a spatial-relational database. Consequently, the support it offers during routine utility asset management tasks has remained untested. This paper, therefore, tests the support of the O&M domain ontology for asset management and proposes a database implementation of this data model. To this end, it models and loads two utility networks from the campus of the University of Twente, the Netherlands. It tests the ontology’s support for asset management by simulating a street reconstruction project and retrieving necessary project information in relation to a utility’s (a) maintenance history and performance, and (b) site conditions and valve locations. Results show that the implemented model supports projects with rapid, comprehensive, and consistent information about semantic details of utilities. Such data needs yet to be collected and registered systematically to enable future data-driven asset management practices.