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An alternative way to represent data regarding internal damage processes, specifically the data provided by the acoustic emission (AE) technique and the pressure stimulated currents (PSCs), is attempted in this study, in terms of log-periodic power-law models. Both monitoring techniques have been already successfully considered widely, either independently or in juxtaposition to each other, in case of standardized laboratory experiments with specimens of relatively simple geometry and moderate dimensions made of a single material. The innovation of the present study is that the AE and PSC data modelled were recorded during structural tests with specimens of complicated geometry made of three different materials, ie, marble, cement paste/mortar, and titanium. It was clearly concluded that both AE and PSC data can be quite successfully modelled in terms of log-periodic power-law models. Moreover, it was indicated that in case of complex experimental set-ups, log-periodic power-law models approach experimental data according to a more accurately manner than pure power-law models do, thus opening up new possibilities of studying the internal damage processes and detecting perhaps useful pre-failure indicators. © 2020 John Wiley & Sons, Ltd.

The deformation of solid materials up to the point of fracture is followed by acoustic emission (AE) signals. The cumulative acoustic energy released during such a process seems to generally follow a power law form, which is compatible with the view that the fracturing process is a phenomenon with developing critical dynamics. In the present work, the cumulative energy released by the AE signals emitted during the deformation of LiF samples has been calculated and fitted by two different fitting functions, which are both power laws decorated with log-periodic corrections. The comparison of the produced results would lead to the conclusion of whether each one of the two equations can produce valid acoustic energy release modeling, which can describe the fracturing procedure of solid materials and its underlying mechanics. It should be noted that one of the models managed to fit the experimental data more accurately in the majority of the cases studied. We show that both of the studied log-periodic models managed to acceptably fit the data, which means that this field of study could possible lead to new methods of predicting the time point of a material's failure. In conclusion, this comparative study reveals that these models could be a valuable tool in cases of monitoring materials or structures being under load at the region of their mechanical tolerance. © 2018 Elsevier Ltd