Predicting the variability of natural frequencies and its causes by Second-Order Blind Identification

Abstract

Structural aging, degradation phenomena, and damage due to hazardous events are common causes of failure in civil structures and infrastructures. The increasing need of extending the structure lifespan for sustainability and economic reasons motivated the rapid development of remote, fully automated structural health monitoring systems. Different approaches have been developed for damage detection based on the incoming data. Modal-based damage detection is probably one of the most popular procedures for structural health monitoring of civil structures, also thanks to the development of robust automated operational modal analysis algorithms in the last decade. However, the sensitivity of modal parameter estimates and the associated damage features to environmental and operational factors represents a significant drawback to the extensive application of this technology. Thus, effective damage detection cannot skip the preliminary compensation of the effect of those variables on modal properties. Different approaches to compensate the environmental influence on modal property estimates are reported in the literature. In this article, the use of Second-Order Blind Identification is proposed. It is applied to a number of case studies in order to validate its effectiveness in the presence of one or more environmental or operational variables. Results demonstrate that it can model the variability of natural frequency estimates in operational conditions and, above all, it can give a fundamental insight in determining the causes of such variability.

Keywords

Vibration-based structural health monitoring environmental and operational variables Second-Order Blind Identification principal component analysis regression models

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