## 5 Discussion
Based on the analysed dataset, the highest severity levels that could correspond to alert signals are also related to the strongest and clearest cross-correlation evidence in the case of the SBA, such as in the cases of the air-to-rock temperature, rock temperature-to-strain, and, secondly, man-induced vibrations-to-strain. Therefore, the OBA and SBA output the same screening severity levels. Nevertheless, the advantage of the SBA with respect to the OBA is automatically providing the temporal lag, during which time a destabilising factor influences the deformational response of the rock mass. Thus, the OBA allows justification of the observed cause-to-effect relations in terms of a physical model of the monitored  
Figure 13: Results from the SBA experienced at the Acuto test-site with associated severity levels determined by cross-correlating causative forcing actions and the induced effects. system, i.e., by associating specific actions (such as temperature or vibrations) relative to the realistic elements (such as rock mass joints) that record the induced effect (i.e., strain). The SEA was revealed to be more suitable for evaluating long-term behaviour, taking into account cumulative inelastic strain effects due to several destabilising actions (i.e., considered as the net resulting effect over time of _predisposing_ and _preparatory factors_, _sensu_ [PERSON] et al. [12]) which singularly act over specific time windows at the block-to-slope scale. In particular, at the Acuto test-site, the long-term behaviour mainly results as an effect of loading/unloading thermal cycles acting on the rock mass and the accumulating inelastic strains along joints as the sum of daily net deformations. Based on the comparison analysis performed on dataset by the multi-parametric monitoring system at the Acuto test-site, the three considered approaches for multi-parametric data analysis can be regarded as more suitable and effective for different time windows, over which several forcing actions can induce objective rock mass strains. The OBA was more suitable for recognizing deformations induced over a shorter time window, i.e., those due to continuous or cyclical forcing actions, as well as impulsive and dynamic input (i.e., hourly to seasonal and instantaneous). The SBA was more suitable for recognizing continuous or cumulative trend variations responsible for strains over time, highlighted by differences in the cross-correlation coefficient or by a lack of it taking into account the same forcing actions, of the deformation response over medium- to long time windows (i.e., daily tending towards seasonal). The SEA was more suitable for detecting deformational trends over very long time windows (i.e., plurennial), i.e., those due to the cumulative effects of different forcing actions characterised by cyclical or continuous occurrence (Figure 14). By considering a long-time window, the cumulative effects of several continuous, cyclic or transient forcing actions can generate a deformative response of the rock mass that leads the physical system, whether a natural or anthropogenic rock slope, towards failure conditions. These strain effects can occur in a rock mass both through the simultaneous action of several forcing actions and through the continuous action over the time of the same forcing. The existent relations among the forcing actions and the directly related deformative responses can be investigated by applying a specific data analysis approach from those presented above. For each considered forcing action, the efficiency of each  
Figure 14: Summary table showing the applicability of the proposed approaches for detecting factors influencing the rock mass stability at the Acuto test-site. The clocks indicate the time windows (i.e., from short- to long-term) over which specific factors are effective. The loop arrows indicate that the considered processes are cyclical. approach was evaluated in terms of its ability to detect the related strain effects.