Researchers have developed an automated technology to analyse the potential for rock falls from cliffs, which could improve risk evaluation and enhance public safety.
The system, dubbed a ‘Rockfall Activity Index’, was developed by researchers from the University of Washington, Oregon State University and the University of Alaska Fairbanks.
It is based on the ability of light detection and ranging (LIDAR) technology, which should accelerate a traditionally time consuming process that determines how dangerous a cliff is to people, vehicles, roads or structures below.
Rock falls are a multi million-dollar problem for transport planners, particularly in the United States’ Pacific Northwest region which has several mountain ranges, heavy rain, unstable slopes and erosion of steep cliffs.
The system could replace the need to personally analyse small sections of a cliff at a time to look for cracks and other hazards. It is able to map large areas in a short period of time, which are then analysed via computer.
Studies on the system, based on examples from Alaska, found that it could evaluate rock falls in ways that closely matched dangers actually experienced.
It produces data on the ‘energy release’ to be expected from a given cliff a year which could be used to identify the cliffs and roads at highest risk and prioritise available mitigation budgets to cost effectively protect public safety.
“This should improve and speed assessments, reduce the risks to people doing them, and hopefully identify the most serious problems before we have a catastrophic failure,” Michael Olsen, associate professor of geomatics in the College of Engineering at Oregon State University, and co-author of the rockfall activity index report, said.
The researchers are now looking at ways to adapt the technology for drones to expand the data that can be gathered.