Search

Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

Left in place pillars of abandoned mines are subject to weathering (e.g action of water, bacteria) that degrades their mechanical strength and eventually leads to collapse. A simple weathering model is proposed, that is governed by two parameters: the rate of progression of weathered front and the rate of degradation of the compressive strength with time. Both plane strain and axisymmetric analyses are performed and closed form solutions of the variation with time of the bearing capacity of the pillar are given. Experimental data of the tests conducted on gypsum and anhydrite specimens attacked by water are presented. It is shown that in order to fit the experimental data a third parameter must be introduced. New closed form solutions are given and the data are used for estimating the time to failure of abandoned gypsum mines in Northern Italy. © 2007 Springer-Verlag.