Advanced statistical techniques applied to raw data for structural damage detection

Abstract

Structural Health Monitoring is one of the most promising and challenging areas of research in the field of Civil Engineering. Over the last decades, researchers have focused on the development of consistent and reliable indicators aiming to detect, locate, quantify or even predict damage. More recently, some researchers are focusing on the use of raw time histories extracted from structural dynamic monitoring to build damage indicators. In this sense, this paper has as its main interest the use of high-order statistics (HOS) coupled with clustering techniques i.e. the k-means and c-means algorithms to detect structural modification (damage). The approach is applied directly to dynamic measurements (accelerations) obtained on site. The efficiency of such methodology is attested by means of a numerical study performed on a model of a simply supported beam and a study based on a real case railway bridge, in France. Results show that HOS coupled with clustering techniques are able to differentiate damage scenarios with adequate classification rates.