On the effectiveness of sparse regularization for structural damage diagnosis using dynamic strain measurements

Strain measurement is considered inherently suitable for structural health monitoring due to the ease of extracting damage-sensitive features. However, the need for pre- and post-damage feature extraction, especially in the absence of baseline data for aging infrastructures in service, has engendered skepticism concerning the practical efficacy of strain-based damage diagnosis. To address this, this study incorporates limited prior information on structures and sparse regularization to ascertain the locations and severities of structural damages based on dynamic strain response data. A model-assisted approach is presented, including the typical three-step process of damage feature extraction, finite element modeling, and analytical sensitivity-based model updating. In particular, sparse regularization technique aids in robustly identifying structural damage by tackling the hindrance encountered during model updating, involving ill-posedness and the impact of measurement and modeling uncertainties. The approach’s effectiveness is substantiated through a numerical investigation of a Bailey truss bridge model exposed to operating loads and an experiment with a steel beam equipped with an array of fiber Bragg grating sensors. The results demonstrate that using the proposed damage diagnosis approach enables accurately locating and quantifying element-level damages, even when significant modeling errors or a lack of baseline features exist.