Stochastic damage detection method for building structures with parametric uncertainties

Uncertainties, such as modeling errors and measurement errors, are inevitably involved in damage detection of a building structure. Most deterministic damage detection methods, however, do not consider uncertainties, thus limiting their practical application. A new stochastic damage detection method is therefore proposed in this paper for damage detection of building structures with parametric uncertainties. The proposed method contains two basic steps. The first step is to determine the probability density functions (PDFs) of the structural stiffness parameters before and after damage occurrence by integrating the statistical moment-based damage detection method with the probability density evolution method. In the second step, based on a special probability function calculated using the obtained PDFs, new damage indices are proposed and both damage locations and damage severities are identified. The feasibility and effectiveness of the proposed method are numerically demonstrated through a shear building structure with three damage scenarios. The first modal damping ratio of the building structure is regarded as a random parameter with a lognormal distribution. Numerical results show that both damage locations and damage severities can be identified satisfactorily. One of the advantages of the proposed method lies in that it can deal with uncertainty parameters of non-normal distributions.