Autoregressive Model Applied to the Meazza Stadium for~Damage~Detection

Abstract

Aerospace, civil and mechanical structures are naturally exposed to damages, which could depend on several sources, such as environment degradations, design faults or unexpected natural events. Statistical pattern recognition has recently emerged as an effective technique for structural health monitoring. Its success depends on the possibility to detect unusual operational scenarios just through a statistical data processing of the structural vibration measurements and without the need of a physical model.

In this paper, we present the application of one of these techniques to the Meazza stadium grandstands, in order to detect different operational and environmental conditions of this structure, by extracting sensitive features from vibration time series. We trained an autoregressive model (AR) on the vibrations data acquired for empty stadium conditions, which were considered the “undamaged” status. Then we tested how this statistical model is able to describe the behaviour of the same structure under different environment conditions, for instance at different temperature values. In the end, we used statistical pattern recognition to detect the “damaged” scenarios represented by the events planned in the stadium, football matches and concerts, when the stands was occupied by public.