Francescantonio Lucà

TL;DR: A multivariable analysis, Principal Component Analysis (PCA), is applied to the set of the autoregressive model parameters estimated on the vibration responses of a real structure under operational conditions and it is shown that this procedure is effective in representing the status of the structure and can be used in a structural health monitoring prospective.

TL;DR: In this article, a vibration-based approach for axially loaded beam-type structures is proposed, where the confounding effect of environmental factors is filtered out by considering more than one vibration mode at a time, thus framing damage detection as a multivariate outlier detection problem.

TL;DR: Real time monitoring of prestressing tendons can provide useful information on the health of the bridge under service loads, detecting possible fatigue, corrosion and damage/deterioration processes.

TL;DR: This work uses the principal components of the autoregressive model parameters as indicators that can effectively describe some important environmental effects, 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.

TL;DR: In this paper, the authors present a real case from the system design, its birth, and its proper use for damage detection, up to the detection of a structural failure, showing that a trade-off must be looked for between the big redundancy offered by the actual networks and the need of a simple and prompt information, granting the structure safety.