On the use of symbolic vibration data for robust structural health monitoring

Abstract

Structural health monitoring is based on the development of reliable and robust indicators able to detect, locate, quantify or even predict damage. Studies related to damage detection in civil engineering structures are of interest to researches in this area. Indeed, the detection of structural changes likely to become critical can prevent the occurrence of major dysfunction associated with social, economic and environmental consequences. Recently, many researchers have focused on dynamic assessment as part of structural diagnosis. Most of the studied techniques are based on time or frequency domain analyses to extract information from modal characteristics or based on indicators built from those parameters. The main goal of this study relies on the application of symbolic data analysis coupled with classification methods to detect structural damage, especially using raw data (i.e. in situ measurements). Modal parameters, such as natural frequencies and mode shapes, are also considered in the analysis. In order to attest to the efficiency of the proposed approach, experimental investigations in the laboratory and on two real case studies – railway and motorway bridges – are carried out. It is shown that symbolic data analysis coupled with classification methods is able to distinguish structural conditions with very encouraging results.