Digital Twin & Infrastructure Asset Management

Project Overview

We built a digital twin for infrastructure asset management — a live, data-rich virtual model of the client’s physical assets, bringing BIM geometry and geospatial context together in one place. It gave the asset owner a continuously updated view of their infrastructure to support condition monitoring, maintenance planning and long-term investment decisions.

The Challenge

The client’s asset data was fragmented across the systems that happened to create it. Design models sat with the consultants who produced them, condition records in a maintenance database keyed on asset IDs that did not match the models, and location in a GIS that knew where things were but nothing about what state they were in. Answering a question that crossed those boundaries — which assets in this corridor are approaching end of life — meant someone reconciling three exports by hand.

The practical consequence was reactive maintenance. Interventions were triggered by failure or by inspection cycle rather than by condition, because condition was not visible early enough to act on.

Our Approach

We started by reconciling identity: a single asset register that the BIM models, the GIS layers and the maintenance history all resolve against. That unglamorous step is what makes everything after it possible, and it is where most of the effort went.

On top of that register we assembled the twin — BIM geometry georeferenced into its real-world position, so an asset can be found either by clicking it in the model or by navigating to it on the map. IoT and sensor feeds were then attached to the assets they measure, so condition arrives against the asset rather than against a standalone time series someone has to correlate later.

Open formats throughout, so the twin is not hostage to one vendor’s platform, and so the client’s own team can extend it without coming back to us.

What We Delivered

The integrated digital twin with the asset register underneath it, the BIM-to-GIS pipeline that keeps geometry and location in step, live sensor integration against the assets, and a condition dashboard the asset team works from day to day. Handover included the documentation and training needed for the client to run and extend it in-house.

Key Outcomes

Maintenance planning moved from cycle-driven to condition-driven, and questions that previously took a day of manual reconciliation became a query against a single model.