CHALLENGE 2: ANTICIPATING GEOTECHNICAL BEHAVIOUR

Brief

ABOUT THIS CHALLENGE

Challenge:

Predict, anticipate and warn operators about the geotechnical behaviour of a specific area found in a civil works environment.

Background: 

Sacyr Engineering and Infrastructure and Sacyr Concessions design and plan their construction projects in accordance with the geotechnical characteristics of the land.

In the design phase of the work, it is essential to know the specifics of the terrain with the greatest precision without having to resort to an extensive geotechnical campaign.  

In the operations and maintenance phase, once the work has been completed, the geotechnical behaviour must always be anticipated , considering the different factors that influence it. 

Objectives: 

Identify technological solutions that help to:

  1. Obtain geotechnical characteristics of the terrain and design parameters quickly and without the need for previous geotechnical campaigns.
  2. Detect activities in those factors that might condition terrain stability and land movements.
  3. Anticipate the evolution of the terrain through modelling and simulation.
  4. Automatically interpret data and ground movements (obtained by satellite and/or other technologies) to generate recommendations, establish early warnings in case of danger, and be able to take preventive measures at critical points (hillsides, slopes, tunnels...).

We look for:

  • Solutions that allow to know in advance the geotechnical characteristics of an area of interest without having to carry out an extensive in situ study.
  • Technologies for the integration, processing and interpretation of satellite data.
  • Systems to capture information from the ground and the environment (on site and remotely).
  • Technologies to monitor and interpret the parameters that influence the geotechnical behaviour of a work zone. 
  • Reliable predictive models of the behaviour of the ground before, during and after a construction site.
  • Solutions that generate recommendations and early warnings based on the evolution of parameters and predictive models. 

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