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.

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Availability of 3D underground information models is key to designing and managing urban infrastructure construction projects. Buried utilities information is often registered by using different types of location data with different uncertainties. These data variances are, however, not considered in many existing visualization approaches. This study, therefore, aims to enable 3D-representation of uncertainties in underground utility data. To this end, the research team identified four different location parameters (unknown, standard, estimated, surveyed location), and explored how these could be integrated into models that visualize utilities in 3D. As proof of concept, we developed a first 3D uncertainty model for an urban road intersection and implemented this in a handheld augmented reality application. As a next step, we designed an improved 3D visualization model that incorporates standard, estimated, and surveyed location data to estimate utility locations, and to generate cylinder shapes that represent the uncertainty buffer around a visualized utility. The presented approach eventually allows city engineers to better estimate design and construction workspaces. We conclude the paper by elaborating both the contributions and consecutive research steps.@en