Regression-Based Algorithms for Structural Damage Identification and Localization

Abstract

Early damage detection and localization is very important for maintenance and retrofit of civil structures. In the past decades, a lot of research has been conducted on structural condition prognosis using vibration measurements, which can be very conveniently procured in large quantities at a moderate cost. Many of these approaches, however, concern only the identification of structural damage existence, and do not attempt higher level damage detection. In this paper, three regressionbased damage detection algorithms are be presented and applied for damage identification in a two-span steel girder in the lab. All of them can perform local damage detection and evaluation to a certain extent. They have different modeling complexities, and thus have different performance levels. Damage identification/localization/severity evaluation results obtained from these algorithms are compared and contrasted.