Observation and assessment of engineering geology controls and failure mechanisms on rock slope behaviour utilizing remotely sensed data

Abstract:

Remote sensing monitoring of rock slopes is being developed by a number of research groups around the world, using LiDAR, photogrammetry and other remote sensing techniques. The quality and quantity of data collected permits detailed assessment of the influence of the geological setting and engineering geology properties of the rockmass, the effect of conditioning and triggering factors, and in some cases the detection of precursor events or accurate application of time to failure assessments related to the rate and magnitude of deformation.

Work to date in this area has focussed on several areas of endeavour, including: development of data collection and processing workflows, considering the unique characteristics and capabilities of each remote sensing method, and benefitting from the rapid development of software and hardware for this work; critical evaluation of the dense and detailed data provided to understand integration of different data types from a variety of ranges and vantage points; accuracy, precision and limitations of the various data sources; and now with numerous years of time sequential data sets, a detailed assessment of the failure processes and rates of change in rock slopes. Work is ongoing to develop semi-automated methods to detect important semantic features of the rock slopes to support risk based decision making, and assess the quality and roughness of the failure planes involved in slope deformation and failure.

Evaluation of several field sites where rockfall hazards affect linear infrastructure will be discussed, to demonstrate the range of rock slope failure modes related to their geological setting and to identify the relationship between failure mode and our ability to detect and forewarn of these impending hazards prior to failure. This is an important consideration in risk management and evaluation for these rock slopes.

The advantages of these data sets, in terms of data density, both spatially and temporally, will be discussed, while highlighting opportunities for future development of detailed analyses within a semi-automated work flow, and emphasizing the benefits and limitations of using this data in risk based infrastructure management.