  Sensitivity-based Finite Element Model Updating (FEMU) is one of the widely
accepted techniques used for damage identification in structures. FEMU can be
formulated as a numerical optimization problem and solved iteratively making
automatic updating of the uncertain model parameters by minimizing the
difference between measured and analytical structural properties. However, in
the presence of noise in the measurements, the updating results are usually
prone to errors. This is mathematically described as instability of the damage
identification as an inverse problem. One way to resolve this problem is by
using regularization. In this paper we investigate regularization methods based
on the minimization of the total variation of the uncertain model parameters
and compare this solution with a rather frequently used regularization based on
an interpolation technique. For well-localized damages the results show a clear
advantage of the proposed solution in terms of the identified location and
severity of damage compared with the interpolation based solution.
  For a practical test of the proposed method we use a reinforced concrete
plate. Measurements and analysis were repeated first on an undamaged plate, and
then after applying four different degrees of damage.
