Titled “Modelling the behaviour of tunnelling machines and impact on their environment”, the thesis of Mehdi Mahmoudysepehr, a doctoral student at the Construction 4.0 Chair in partnership with Centrale Lille engineering school, endeavours to analyse big data collected on the ground in order to better anticipate and solve technical issues. And also to use statistical learning methods (machine learning and deep learning) to model the tunnelling machine’s behaviour and optimize its control.
is the number of innovations planned in the Lab TP roadmap
What is machine learning?
Machine learning is an artificial intelligence technology that allows computers to learn without having to be programmed specifically for this purpose. To learn and develop, computers nevertheless need a lot of data (big data) to analyse and learn from.
Did you know?
A big data application is in use at the Eole site
It models the tunnelling machine’s behaviours and provides a terrain-machine interaction indicator to help teams control the machine. This provides a data-driven approach to make strategic decisions in real time.
What is the principle of TopoEveryWhere?
Connecting from a single base to several networks of sensors autonomously measuring the impact of tunnelling projects and displaying the measurements on dashboards. An actual control room to track the behaviour of neighbouring structures.