RoadAI: Reducing emissions in road construction

Authors

  • Signe Riemer-Sørensen SINTEF
  • Helga Margrete Bodahl Holmestad SINTEF
  • Lars Horn Skanska Norge AS
  • Katarzyna Michalowska SINTEF
  • Bjørn-Jostein Singstad
  • Sabry Razick University of Oslo
  • Jacob Christian Døskeland Ditio
  • Birte Malene Tangeraas Hansen NORA

DOI:

https://doi.org/10.5617/nmi.10315

Keywords:

Artificial Intelligence, Machine Learning, Deep Learning, robotics

Abstract

This challenge aims to reduce CO2 emissions from Norwegian construction machines by utilizing data-driven approaches.
Skanska Norge AS provides a wealth of data from their construction sites, including GPS data, machine data, vibration data, and drone maps. The challenge focuses on using this data to optimize road construction processes for increased sustainability. Participants are tasked with developing feasible solutions to reduce idle time, optimize dump truck flow, minimize unnecessary driving, and improve driving styles. The evaluation criteria consider the novelty of the algorithms, feasibility of implementation, and the sustainable impact. The competition encourages innovative approaches to leverage data for sustainable construction practices.

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Published

2023-08-22