Lab TP uses data to help with decision-making

A Bouygues Travaux Publics service created in 2016

Lab TP aims to design predictive models to help with the management and maintenance of tunnelling machines and improve safety and productivity when digging tunnels. With better understanding of the internal and external interactions of tunnelling machines, certain technical issues can be anticipated and resolved, increasing the rate of progress in digging. At most of our tunnelling sites, data generated by the tunnelling machines is centralised at a worksite data hub and then analysed by a data scientist and engineer. The benefits of this are more accurate tracking of productivity, real-time management of site activity and improved feedback. Raw site data is also sent automatically to the central data platform, which identifies the data and stores it securely over the long term. This data is then made available to data scientists to model and create predictive algorithms by means of machine learning, which will further improve our productivity, just like the many decision-making tools using big data already in operation at our sites.

Thesis proven!

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.


Further information