The transport infrastructure of a country facilitates the development and growth of its economy and improves the
quality of life of its inhabitants. Increasing its resilience to different types of risks to improve performance is
becoming more important. In the current context of climate change, natural hazards are more severe and
frequent. In this article, we focus on rockfall as a natural hazard for roads that occurs in small areas in the vicinity
of natural or cut slopes, causing road safety problems by invading part of the road.
This article aims to inventory the slopes along the road, identifying the area of the road which would be
invaded in case of a rockfall. A methodology divided into two blocks is proposed. First, for slope detection and
inventory, an algorithm is developed based on open LiDAR point clouds analysis. The second block consists of
estimating the invaded road area if a rockfall occurs on each of the inventoried slopes, using a combination of
RockGIS software and the Monte Carlo method. The methodology was applied in five case studies: three sections
on motorways and two sections on national roads. The results obtained for slope detection show higher rates in
the case studies analyzing motorways, with a precision of 100%, a recovery rate of greater than 93.4%, and an F1
score of greater than 0.96. The results in the invaded area of the road show that 11 slopes would cause a total cut
of the motorway in one of the directions if a rockfall occurs.
These results are useful for infrastructure managers to remotely obtain an inventory of road slopes and know
which of them would affect road safety. Also, the results can serve as input for the Intelligent Transportation
System and allow the exchange of information under the Building Information Model approach.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I00Ministerio de Ciencia, Innovación y Universidades | Ref. PRE2020-096222European Commission | Ref. H2020, n. 95533
