11 November 2022
Sacyr iChallenges was created with the objective of identifying proposals in the ecosystem that help us respond to our challenges. Our philosophy is based on transforming these proposals into new products, services and even new business models.
During the more than five years that the program has been running, we have developed great projects all over the world. Projects that are based on some of the most disruptive technologies, that have great potential for results and allow us optimize our operation all around the world.
The objective of the project is to optimize the use of electrical energy by means of artificial intelligence and Machine Learning algorithms in the Moncloa interchange facilities.
Design and implementation of a technological platform that allows us to automatically and reliably calculate the impact of our activities on natural capital.
A solar energy collection system has been implemented from flexible organic panels, on the roofs of Moncloa’s transport interchanger.
Solution focused on the anticipation of ground behavior, analyzing dozens of ground variables, rainfall and satellite radar, building static and dynamic models of geotechnical behave and predicting possible slope movements.
The carbon footprint associated with Sacyr’s main activities has been measured and reported while analyzing the data transmitted by suppliers in scenarios in Chile and Spain.
Tool based on big data for automated analysis of dozens of information sources and early identification of business opportunities.
We use artificial vision algorithms based on neural networks to count and classify vehicles in conditions of reduced visibility in order to reduce waiting times and improve safety in our tolls.
An IoT technology solution has been installed to monitor in real time the routines of dependent people belonging to the HAS (Home Assistance System) of Coslada (Madrid, Spain) in order to improve the quality of life of people in their own homes.
Artificial vision system for automatic detection of defects or cracks in the pavement and analysis of the condition of road signs.
Tool based on machine learning models for the prediction of the deterioration of roads’ pavement in the long term.
