A probabilistic method for the detection of obstructed cracks of beam-type structures using spatial wavelet transform

This paper reports both the theoretical development and the numerical verification of a practical wavelet-based crack detection method, which identifies first the number of cracks and then the corresponding crack locations and extents. The value of the proposed method lies in its ability to detect obstructed cracks when measurement at or close to the cracked region is not possible. In such situations, most nonmodel-based methods, which rely on the abnormal change of certain indicators (e.g., curvature and strain mode shapes) at or close to the cracks, cannot be used. Most model-based methods follow the model updating approach. That is, they treat the crack location and extent as model parameters and identify them by minimizing the discrepancy between the modelled and measured dynamic responses. Most model-based methods in the literature can only be used in single- or multi-crack cases with a given number of cracks. One of the objectives of this paper is to develop a model-based crack detection method that is applicable in a general situation when the number of cracks is not known in advance.

To explicitly handle the uncertainties associated with measurement noise and modelling error, the proposed method uses the Bayesian probabilistic approach. In particular, the method aims to calculate the posterior (updated) probability density function (PDF) of the crack locations and the corresponding extents.

The proposed wavelet-based crack detection method is verified and demonstrated through a comprehensive series of numerical case studies, in which noisy data were generated by a Bernoulli–Euler beam with semi-rigid connections. The results show that the method can correctly identify the number of cracks even when the crack extent is small. The effects of the number of cracks and the crack extents on the results of crack detection are also studied and discussed in this paper.