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Alternative approaches for analyses of a 100,000 m 3 rock slide based on Barton-Bandis shear strength criterion Abstract A data set was derived for the Åknes rock slope, Norway, with the main focus on deriving input parameters for the BartonBandis shear strength criterion. Back-calculations of a 100,000 m 3 rock slide were performed for evaluation of the data set. The limit equilibrium analysis showed that the joint roughness coefficient (JRC) has the greatest effect on the calculated safety factor of the slide. Probabilistic computations showed that the JRC stands out as the most important contributor to the total uncertainty over the whole set of variables and that the computed failure probability of the 1960 slide was very high, which may be interpreted that the input variables and the Barton-Bandis shear strength criterion are reasonable for the slide. JRC was measured on 0.25 m scale and on 1 m scale. The results from the two scales were different.

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This paper introduces the application of a simple and practical method for estimating the risk associated with a potential tsunamigenic rockslide, by assessing quantitatively hazard, vulnerability, and elements at risk. The proposed method introduces empirical relations between the risk components and illustrates the uncertainty propagation through the steps in the risk analysis. A case study is presented, showing the applicability of the method for estimating the risk associated with the tsunamigenic Å knes rockslope in Stranda municipality in western Norway. Results show, preliminary risk estimates that will be of significant value for future policy and decision making.

Rock slope instabilities are discussed in the context of decision making for risk assessment and management. Hence, the state of the slope and possible failure mechanism need to be defined first. This is done with geometrical and mechanical models for which recent developments are presented. This leads with appropriate consideration of uncertainties to risk determination and to the description of tools for risk management through active and passive countermeasures, including warning systems. The need for sensitivity analysis is then demonstrated, and final comments address updating through information collection.

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