Evaluating roadside rockmasses for rockfall hazards using LiDAR data: optimizing data collection and processing protocols |
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| Authors: | Matthew J Lato Mark S Diederichs D Jean Hutchinson Rob Harrap |
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| Institution: | (1) Norwegian Geotechnical Institute, Oslo, Norway;(2) Geological Sciences and Geological Engineering, Queen’s University, Kingston, ON, Canada; |
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| Abstract: | Highways and railroads situated within rugged terrain are often subjected to the hazard of rockfalls. The task of assessing
roadside rockmasses for potential hazards typically involves an on-site visual investigation of the rockmass by an engineer
or geologist. At that time, numerous parameters associated with discontinuity orientations and spacing, block size (volume)
and shape distributions, slope geometry, and ditch profile are either measured or estimated. Measurements are typically tallied
according to a formal hazard rating system, and a hazard level is determined for the site. This methodology often involves
direct exposure of the evaluating engineer to the hazard and can also create a potentially non-unique record of the assessed
slope based on the skill, knowledge and background of the evaluating engineer. Light Detection and Ranging (LiDAR)–based technologies
have the capability to produce spatially accurate, high-resolution digital models of physical objects, known as point clouds.
Mobile terrestrial LiDAR equipment can collect, at traffic speed, roadside data along highways and rail lines, scanning continual
distances of hundreds of kilometres per day. Through the use of mobile terrestrial LiDAR, in conjunction with airborne and
static systems for problem areas, rockfall hazard analysis workflows can be modified and optimized to produce minimally biased,
repeatable results. Traditional rockfall hazard analysis inputs include two distinct, but related sets of variables related
to geological or geometric control. Geologically controlled inputs to hazard rating systems include kinematic stability (joint
identification/orientation) and rock block shape and size distributions. Geometrically controlled inputs include outcrop shape
and size, road, ditch and outcrop profile, road curvature and vehicle line of sight. Inputs from both categories can be extracted
or calculated from LiDAR data, although there are some limitations and special sampling and processing considerations related
to structural character of the rockmass, as detailed in this paper. |
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