Distributed fibre-optic sensing
Bespoke fibre-optic transducers
The problem. Sub-millimetre movement has to be measured somewhere an off-the-shelf displacement gauge cannot reach the required resolution, bandwidth or survivability.
The approach. Design a purpose-built fibre-optic transducer around the specific measurand, then validate it on an interrogator against a calibrated reference before it goes anywhere near the asset.
What it gives you. Micron-level resolution from a single fibre, with sensors that can be chained along an asset rather than wired back one by one.
Buried utilities · Geotechnical
How a failing pipe damages the road above it
The problem. A deteriorating buried pipe disturbs the ground around it, and that movement shortens the life of the pavement overhead. The two are usually managed by different teams with different budgets.
The approach. A three-dimensional finite-element model of the coupled road, soil and pipe system, calibrated against measured behaviour rather than assumed parameters.
What it gives you. A quantified link between pipe condition and surface distress, so buried-asset risk can be weighed against road maintenance cost. Method published in Results in Engineering (2024).
Pavements · Water infrastructure
Catching a leak before the road fails
The problem. A hidden leak beneath a road develops slowly into visible surface failure, and by the time it is visible the cheap intervention window has closed.
The approach. Instrumented laboratory experiment paired with a numerical model, tracking moisture, ground movement and pavement response together rather than in isolation.
What it gives you. The conditions that turn a slow leak into structural damage, and therefore when intervention is actually worth paying for.
Asset management · Machine learning
Prioritising urban street works with data
The problem. Trenching and street works damage urban assets, and owners need a defensible basis for deciding where to act first.
The approach. A machine-learning model inside an asset-management framework, trained on the recorded impact of trenching in urban environments.
What it gives you. A data-driven ranking of interventions by impact, rather than by whoever complained most recently. Method presented at Geo-Congress 2024 (ASCE).
Monitoring strategy · Data analytics
Getting more from less monitoring data
The problem. Monitoring schemes generate far more data than most budgets can analyse, which raises an awkward question about what is genuinely worth measuring.
The approach. A data-driven framework that optimises how often, and where, performance data is sampled.
What it gives you. Comparable predictive accuracy from a fraction of the data, and a lower running cost for a long-term monitoring programme.
Structural health monitoring
Designing a scheme that answers a question
The problem. A monitoring scheme that records everything and explains nothing is a common and expensive failure mode, and it usually survives for years because nothing about it looks broken.
The approach. Scope the instrumentation around the specific decision an owner needs to make, then choose sensors, locations and sampling to match that decision rather than the catalogue.
What it gives you. Monitoring that pays for itself, because the data it returns maps directly onto an action.
This page describes the problems GeoMonix's methods are built for and how it would
approach each one. It is not a list of client references. Where an underlying method
has been published in the peer-reviewed literature, the citation is given.