Prediction of time to slope failure: a general framework

The prediction of time to slope failure (TSF) is a goal of major importance for both landslide researchers and practitioners. A reasonably accurate prediction of TSF allows human losses to be avoided, damages to property to be reduced and adequate countermeasures to be designed. A pure “phenomenological” approach based on the observation and interpretation of the monitored data is generally employed in TSF prediction. Such an approach infers TSF mainly from the ground surface displacements using regression techniques based on empirical functions. These functions neglect the rheological soil parameters in order to reduce the prediction uncertainties. This paper presents an overlook of the methods associated with this approach and proposes a unique expression encompassing most of the previously proposed equations for TSF prediction, thus offering a general framework useful for comparisons between different methods. The methods discussed in this paper provide an effective tool, and sometimes the only tool, for TSF prediction. The fundamental problem is always one of data quality. A full confidence in all assumptions and parameters used in the prediction model is rarely, if ever, achieved. Therefore, TSF prediction models should be applied with care and the results interpreted with caution. Documented case studies represent the most useful source of information to calibrate the TSF prediction models.