Abstract

Denudation processes induced by external loading show scale-independent traits in rocks. Therefore, monitoring of micro-cracking features offers a possibility for assessing the structural health or rock massifs; eventually leading to early-warning systems capable of estimating the risk of catastrophic collapses. This study assesses the behaviour of acoustic emissions monitored while a sandstone sample was subjected to staged monotonic uniaxial compression. Particularly, waveform characteristics were recorded and analysed to identify the most predominant factors for classification. Then an unsupervised k-means algorithm was employed to cluster these parameters into two categories, related to the source being either a tensile or shear dislocation. Clusters showed noticeable differences, whilst results indicate that properties of AE waveforms vary significantly amongst diverse stages of loading, being the rise time the most sensitive parameter. Moreover, it seems that transitions amongst diverse behavioural stages of the sample are preceded by changes in the first lag of the autocorrelation function and the variance of the ratio of maximum amplitude and rise time of the ensemble of time histories observed within each stage. This trend is significantly more noticeable during the last stage, just before the collapse. This trait is in accord with the critical slowdown theory (CSD). This allows for the development of early-warning systems signalling partial collapse of rock masses.

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