Sensing and AI produce data. Structural engineering turns data into knowledge about a structure. Decision methods and data standards turn that knowledge into maintenance decisions across a whole network. Every project at ARIES sits somewhere on that chain, and the chain is the product. Bridges come first, because that is where our flagship program and published work are; the same chain applies to buildings and other structures.
Four layers, each feeding the next. A project is judged by which links of the chain it strengthens.
UAV imagery, LiDAR point clouds, vibration and strain sensors.
Who: Geospatial and Field Operations group · Produces: raw measurements of real structures
Computer vision that classifies damage, digital twins that hold geometry, damage and behaviour in one model, machine-learning estimators of capacity.
Who: Structural Assessment and Digital Twin group · Produces: structured, comparable descriptions of condition
Nonlinear finite-element assessment, random-field models of spatial corrosion, reliability updating from inspection data, durability of materials.
Who: Both groups; multi-hazard and materials groups as they open · Produces: how safe a structure is, and how that changes with time
Network-level prioritization, data standards owner agencies can adopt, training for the engineers who will use them.
Who: For the agencies that own and maintain bridges; at the research stage · Produces: which structure, which action, in what order
| Project | What it does for the mission | Layers |
|---|---|---|
| Digital twin platform for network-level bridge assessment (NRCT, 2026) | Runs the whole chain on a pilot bridge, links it to network-level prioritization, and produces the data standards. | Sensing · Intelligence · Decisions |
| Coastal concrete bridge damage detection and assessment (NRCT, 2025, completed) | Built the UAV + AI + finite-element assessment pipeline that Bridge Intelligence extends. | Sensing · Intelligence · Structural knowledge |
AI proposes, people decide. Every automated detection is reviewed before it changes a rating.
Results are delivered in data formats and procedures that agencies can use without us in the room.
Applied work and academic work are the same work. Each funded research project aims to report in a peer-reviewed venue.
Students, research assistants and agency engineers learn the methods by working on real structures.
Pilot the Bridge Intelligence chain on part of your network, and shape the data standard that comes out of it.
Joint proposals and student exchange on structural reliability, multi-hazard assessment and life-cycle management.
A thesis topic that is one link in the chain: survey, detection, twin, reliability or materials, on a real bridge.
Training for inspection engineers, and collaboration on materials, sensing and inspection technology.
