Information about the location, condition and interdependencies of the infrastructure and geology below our feet is poor or lacking. Even though information about what is below ground is revealed by every excavation, it is rarely shared. At the December Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, Josh Lieberman of the OGC presented an overview of the progress of OGC's underground information initiative, with the appropriate acronym MUDDI, which is intended to provide an open standards-based way to share information about the below ground.
Unreliable information about what is underground including infrastructure and supporting structures and their interdependencies; soil, sediment, fill, and interstitial water and air; and physical, biological, and chemical properties leads to risk of injury to workers and the public and produces a significant drag on the construction sector. Josh quoted an estimate from the the Institution of Civil Engineers (ICE) that about 50% of cost and time over-runs on civil engineering projects are caused by 'unforeseen ground conditions. If we are going to optimize where and how we build, we need much better information about what is below ground at the planning stage. To give a practical example close to home, in the middle of the Ottawa LRT project, a huge sink hole which unexpectedly appeared in the middle of a major street in Ottawa was blamed for a significant delay in the project as well as serious traffic disruptions.
There are a number of data models for different types of underground infrastructure and geology which raises the question; why do we need another ? The answer is twofold; in the context of a smart city we need to be able visualize and analyze all this information together, not just one network at a time. Secondly MUDDI is not an attempt to replace existing standards, but to build on and augment existing standards to create a unified model supporting multiple perspectives.
Use cases
The MUDDI project has identified several different broad use cases that the model is intended to support including routine street excavations (EX), emergency response (ER), utility maintenance programs (OM), large scale construction projects (AE), disaster planning and response (DP), and smart cities programs (SC). For each of these, several basic requirements that the model needs to satisfy have been identified. For street excavations the requirement is location of all entities with high horizontal, medium vertical accuracy (2.5D) of underground infrastructure; for large construction projects, detailed 3D geometry of underground infrastructure and detailed 3D geology; for emergency response, interdependencies between different networks; for utility maintenance, network topology and facility location and condition; and for smart cities the ability to monitor and relate streams of data from sensors.
To provide a way for the model to be used by different types of users, the concept of profiles has been introduced. Profiles have been used for other OGC standards and allow for different levels of complexity for different domains and applications. The proposed profiles include asset, excavation, emergency, planning and integration profiles.
Reference standards
The MUDDI model is intended to build on and be compatible with many existing reference/target models. For infrastructure these include CityGML with Utility Network ADE (Application Domain Extension) , INSPIRE Utility Networks, IMKL (Information model for cable and pipes), BIM-IFC, Land and Infrastructure Conceptual Model (LandInfra), Singapore Underground Geospatial Model, PipelineML, Underground Pipeline Information Management System, CIM (Common Information Model), Multispeak, ESRI Utility Model, and GEOfeature. For geology, the reference/target models are GeoSciML, INSPIRE Geology, GroundwaterML, BGS National Geological Model, EarthResourceML, GeoTOP, SoilEML, IFC Geotechnical Extension, MINnD, and BoreholeIE.
Next steps
Josh outlined the next steps in the development of the MUDDI model which include implementations, experimental and prototype deployments with real world data, and estimates of returns on investment (ROI).
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