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Publication details for Dr Nick Rosser
Rosser, N.J. & Petley, D.N. Monitoring and modeling of slope movement on rock cliffs prior to failure. Landslides and Engineered Slopes: From the Past to the Future, Vols 1 and 2. 2008;1265-1271.- Publication type: Journal papers: academic
- View online: Online version
Author(s) from Durham
Abstract
In this paper we examine the use of terrestrial laser scan data for monitoring large rock cliffs in order to assess the dynamics of the slope condition prior to failure. The assessment presented uses a combination of this terrestrial data and air-borne mapping and imagery to explore the degree to which small-scale precursors to slope failure can be identified, and ultimately used to predict failure. The degree to which data is projected from any given perspective which represents the development of the slope is suggested to be critical. Here we present data that has been collected for four years at monthly intervals from over 35,000 m(3) of near-vertical coastal rock face. This high-resolution data is used to identify characteristic spatial and temporal patterns in rockfall activity notably in the periods leading to large-slope failures. To date in excess of 60,000 discrete events have been recorded ranging from 0.00001 m(3) to 2,500 m(3). The patterns in the data show similarity with time-dependant models of failure mechanisms, which may enable the prediction of failure occurrence both in time and in space. This interpretation is only reached however with appropriate and, in terms of survey, unconventional treatment. Analysis of the data derived from the laser scanning suggests that given sufficient measurement precision, precursory behavior can be identified and monitored but that the relative viewing angle of the observation compared to the vector of deformation is critical. In this instance pre-failure deformations are manifest as the rate of rock fall activity prior to failure, in addition to the direct measurement of the accumulation of the strain field across the failing rock face. The monitoring data implies a time-dependent sequence in the occurrence of rock falls in the period leading to the largest failures recorded, often mirroring the hyperbolic increases in movement witnessed in standard displacement monitoring or laboratory simulation prior to failure. The implication is that combining this data with models of failure mechanisms may allow failure time to be forecast from wide-area monitoring of precursory behavior. This ultimately means that a small number of instruments can be employed to monitor large expanses of rock face, providing both spatial and temporal data. These approaches, combined with the time-dependant modeling of precursory activity, have implications for the management of potentially unstable slopes, the understanding of slope failure mechanisms and potentially feed into a new generation of slope failure warning systems applicable to both natural and man-made situations.