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scheme to ensure the tunnel’s stability while nearby works (excavation, pile driving, and dewatering) were underway. Each of those activities changes the stress state of the surrounding subsoil, and each raises the risk of tunnel deformation. Precise movement monitoring was required to meet regulatory obligations to the operator. This is, in practical terms, the hardest version of the
monitoring problem: an operational asset that cannot be closed for long, sitting directly next to work that is actively changing the ground around it, where both lateral movement and convergence need to be tracked together, continuously, for as long as the adjacent works continue. Automated total stations are the industry’s established
answer for continuous coverage on sections like this, but installing one is rarely straightforward. Tunnel operators need to preserve vehicle clearance and are understandably reluctant to place bulky equipment inside a live tunnel, and total stations still require regular maintenance visits to stay calibrated and operational: each one a site visit, a permit, a safety plan. Academy Geomatics installed TotaLite’s A1.0 sensor
alongside an existing automated total station, both observing the same set of optical targets across three tunnel sections, five prisms per section, reconstructing the tunnel’s shape from x, y, and z displacement data at each point. Over three months of continuous, unattended
monitoring, TotaLite held accuracy within 2mm of the automated total station’s readings, with better than 95% uptime for the full deployment, and needed no manual site visits to get there. Installation itself was completed on schedule, fitted around a short tunnel shutdown that was already carrying other maintenance work. The result that matters most is where accuracy
was achieved: in a section where the automated total station’s footprint was itself a constraint. As Mark Anderson, Director of Academy Geomatics, put it: “Given its size and accuracy it can be used where an automated total station cannot.” That combination (total-station-grade accuracy, in
a footprint an automated total station can’t occupy, without recurring site visits) is what makes continuous monitoring viable on exactly the sections where it’s hardest to justify today: live, operational tunnels next to active construction, where both lateral movement and convergence carry real consequences for asset performance and public safety if they go unnoticed.
A GENTLER COMPARISON: POST- EXCAVATION CONVERGENCE It’s worth being clear-eyed about where the evidence is strongest and where it isn’t. A twelve-month deployment at Andra’s Underground Research Laboratory near Bure, France, tracked post-excavation convergence 500 metres underground and held a repeatability of 0.5mm per five-minute reading, a genuinely strong precision result. But Bure’s tunnel is a controlled research environment, not a live construction site: less dust, fewer line-of-sight obstructions, none of the equipment and foot traffic that interrupts sightlines on an active project.
October 2026 | 23 Dust and blocked sightlines are real factors that reduce
sensor effectiveness in rougher, more typical excavation conditions, so this case supports a narrower claim (that the instrument can hold precision over time) rather than the harder claim that it performs equally well in the messiest environments tunnelling actually works in.
WHERE THE REAL OPPORTUNITY SITS Solving the instrumentation problem is what makes the next step possible, not the end goal itself. Once continuous, accurate, non-invasive monitoring can be deployed at scale (across many operational tunnels, by many different contractors and asset owners), the more valuable question becomes what happens when that data is no longer siloed. Movement patterns under similar construction conditions, across dozens or hundreds of monitored sections, are exactly the kind of dataset that machine-learning approaches could use to flag early risk signatures before a threshold is crossed, rather than after. That requires data to move between organisations and projects in a way it currently doesn’t. That’s a harder problem than any single sensor
deployment, and no one case study solves it. But cases like this one point at what a shared library of monitoring data, built project by project and published honestly (including where the method’s evidence is stronger and where it’s weaker) could eventually support: monitoring that protects asset performance and public safety not just on the tunnel being watched, but on the next one, because of what was learned on this one.
Below: TotaLite’s A1.0 sensor tracks the same survey prisms as the automated total station, running unattended for three months with no manual site visits.
Above: TotaLite’s continuous readings held within 2mm of the automated total station’s measurements throughout the three- month deployment.
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