On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework

Alessio Datteo, Giorgio Busca, Gianluca Quattromani and Alfredo Cigada

Reliability Engineering and System Safety, 2018, vol. 170, issue C, 99-115

Abstract: This paper proposes a complete sensitivity analysis of the use of Autoregressive models (AR) and Mahalanobis Squared Distance in the field of Structural Health Monitoring (SHM). Autoregressive models come from econometrics and their use for modelling the response of a physical system has been well established in the last twenty years. However, their aware application in engineering should be supported by knowledge about how they describe phenomena which are well defined by physics. Since autoregressive models are estimated by a least square minimization, statistical tools like Global Sensitivity Analysis and uncertainty propagation are powerful methods to investigate the performance of AR models applied to SHM.

Keywords: Structural Health Monitoring; Autoregressive model; Mahalanobis Squared Distance; Uncertainty propagation; Global Sensitivity Analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:170:y:2018:i:c:p:99-115

DOI: 10.1016/j.ress.2017.10.017

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