From Digital Twin to Intelligent Asset Management: Integrating GIS, 3D, Maintenance and AI for Infrastructure Networks

Managing infrastructure assets increasingly means making decisions based on information spread across multiple systems: GIS platforms, technical documentation, 2D and 3D models, inspection records, work orders, condition data and maintenance histories. The challenge is not simply to collect more data, but to determine which information you actually need and how you can combine it to support better asset management decisions.

This becomes even more complex when you manage existing infrastructure. Asset information may be incomplete, data quality can vary significantly and creating detailed digital representations of large networks can require considerable effort. At the same time, you need to prioritise inspections, identify critical assets, understand risk and decide where maintenance, rehabilitation or replacement will create the greatest value.

Digital twins and AI can help address these challenges, provided that technology starts from clear asset management and operational requirements rather than becoming an objective in itself.

In this webinar, you discover how MACE Qatar combines asset information, GIS, 2D and 3D representations, condition and maintenance data and AI within a practical infrastructure asset management approach. Drawing on experience from the management of large-scale drainage and wastewater infrastructure in Qatar, the session demonstrates how integrated information can support criticality and risk assessment, inspection prioritisation, maintenance optimisation and longer-term investment decisions. 

• Building a useful digital representation of existing infrastructure
  • Determining which asset information is actually required
  • Addressing incomplete or fragmented legacy information
  • Selecting the appropriate level of digital representation rather than automatically creating a full 3D model
  • Rapid reality capture with drone LiDAR and 3D laser scanning: over 200 million points captured in around two hours, with GIS-ready models in under an hour
• Integrating asset information across different systems
  • Combining GIS, BIM, 2D and 3D information
  • Connecting technical asset information with maintenance and work-order data
  • Integrating condition, inspection and contextual information
  • Capturing and validating field data in real time with GeoMACE_v2, closing the “data latency” gap between site and office
  • Viewing Maximo work orders inside 3D facility models, with above-ground facilities and buried networks in a single view
  • Governing information to ISO 19650 and ISO 55001 to keep a “golden thread” of trusted asset information
• AI-supported criticality and risk assessment
  • Combining information from multiple sources to assess asset criticality
  • Applying criticality standards and risk criteria
  • Visualising criticality and risk geographically
  • Accelerating analyses that previously required extensive manual work
  • Using GeoMACE_v2, MACE Qatar’s in-house GIS and asset management platform with integrated AI, for predictive analytics and decision support
• Moving from reactive towards proactive infrastructure management
  • Learning from failures, emergency interventions and historical work orders
  • Identifying locations with an increased probability or consequence of failure
  • Prioritising inspections and interventions across extensive infrastructure networks
  • Simulating rainfall and blockage scenarios to plan flood mitigation ahead of the rainy season
• Optimising maintenance based on actual asset behaviour
  • Combining inspection, condition, maintenance and failure information
  • Using asset performance evidence to recommend maintenance frequencies
  • Avoiding unnecessary maintenance interventions that may themselves contribute to asset deterioration
• Supporting rehabilitation, replacement and investment decisions
  • Combining condition, criticality, failure and maintenance information
  • Identifying assets where deterioration, recurring problems or capacity constraints require intervention
  • Creating an evidence base for rehabilitation and capital investment priorities
• Applying the approach at infrastructure scale
  • Practical experience from a long-term drainage operations and maintenance environment in Qatar
  • Managing more than 3,000 kilometres of surface water, foul sewer and treated water networks and over 40 facilities, including Doha West Treatment Plant and 47 pumping stations
  • Digital adoption at scale: 931 portal users, 422 mobile users and more than 346,000 field reports generated to date
  • Keeping engineering and asset management requirements at the centre, with digital technology as an enabler

You learn how integrated asset information, digital twins and AI can move beyond visualisation and contribute directly to criticality assessment, risk-based prioritisation and maintenance decision-making. You also gain insight into how engineering, GIS, BIM and AI can be combined to turn dispersed infrastructure data into actionable information. The session draws on the MACE Qatar Drainage O&M Portal, an Operational Digital Twin that unifies GIS, BIM and real-time telemetry within an ISO 55001 and ISO 19650-aligned framework, and on GeoMACE_v2, MACE Qatar’s in-house GIS and asset management platform with integrated AI. It also traces the journey from no GIS in 2020 to the Operational Digital Twin in use today.