Long-time monitoring of the G. Meazza stadium in a pattern recognition prospective

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In recent years, the interest for the automatic evaluation of the state of civil structures is increased. The development of Structural Health Monitoring is allowed by the low costs of the hardware and the increasing of the computational capacity of computers that can analyze considerable amount of data in short time. A Structural Health Monitoring (SHM) system should continuously monitor structures, extracting and processing relevant information, in order to efficiently allocate the resources for maintenance and ensure the security of the structure. Considering the latest developments in this field, great attention has been paid to data-based approaches, especially to autoregressive models; these econometric models, born in the field of finance, are usually used to analyze the vibration time series provided by the sensors applied on the monitored structures. Indexes based on these autoregressive models can be used as features by which the structural integrity can be assessed. This work proposes the application of multivariable analysis, the Principal Component Analysis (PCA), to the parameters of autoregressive models estimated on the vibration responses of a real structure under operational conditions. This approach reduces a complex set of data to a lower dimension, by representing the behavior of the structure through the few variables. This work uses the principal components of the autoregressive model parameters as indicators that can effectively describe some important environmental effects. The strategy is applied for the first time on the data collected by the long-time monitoring system installed on the stands of the G. Meazza stadium in Milan. The results will show that this procedure is effective in representing the status of the structure and can be used in a structural health monitoring prospective.




Datteo, A., Lucà, F., Busca, G., & Cigada, A. (2017). Long-time monitoring of the G. Meazza stadium in a pattern recognition prospective. In Procedia Engineering (Vol. 199, pp. 2040–2046). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2017.09.470

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