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The destruction of Amatrice and the surrounding villages in Central Italy after the 2016 seismic sequence was so impressive that engineers, authorities and local communities started sharing the common feeling that historical stone masonry buildings were too below current safety standards. The severe damage caused by the earthquakes led to a general distrust of traditional building techniques, leading to the conclusion that there is nothing to do but demolish and rebuild, perhaps with a false antique. Is there an alternative? Is there a way to combine safety and preservation of architectural heritage? This paper aims contributing to the understanding of the seismic behaviour of stone masonry by reproducing, through simulation on a shake table, the progressive loss of compactness of a real scale rubble masonry wall up to the ruinous collapse with the separation between the two external leaves. The laboratory simulation allowed to evaluate the decrease of the fundamental frequency with increasing damage and estimate the maximum displacement profile and the amount of cracking that the wall is able to sustain before failing. Eventually, two modelling strategies based on finite and discrete element methods were proposed and applied to verify the capability of simulating the out-of-plane seismic response and the failure mechanisms of rubble masonry.

Objective: Current approaches of routine outcome monitoring (session-by-session measures) expect that trajectories of change should move on a standard track. Patients moving out of standard tracks are assumed to be at risk of deterioration. From a nonlinear dynamic systems perspective, there is not any assumption regarding a supposed standard track a patient should follow. Individual trajectories should be more complex than averaged tracks, highly individual, and characterised by pattern transitions. Method: We tested if high-frequency (daily) trajectories of change are moving on standard tracks, if there are different complexity levels of high- versus low-frequency time series, if ‘not on track' dynamics will be correlated with poor outcome and if complexity peaks representing the critical instabilities of a process will be correlated with the outcome. The patients included in the data analysis (N = 88) used the Therapy Process Questionnaire (TPQ) for daily self-assessments and the ICD-10-based Symptom Rating (ISR) for outcome evaluation. Results: High-frequency trajectories are not running on standard tracks and are not necessarily correlated with poor outcome. Locally increased complexity may be associated with good outcome. Conclusion: It may be useful to move beyond the concept of standard tracks and expected treatment outcomes. Routine feedback procedures should use the information that is given by the nonlinear dynamics of multiple change criteria.