Sensitivity Evaluation of Subspace-Based Damage Detection Method to Different Types of Damage

Abstract

In this paper we investigate a damage detection technique based on the subspace method by applying it to an existing bridge structure model. A reference state of the structure is evaluated using this technique and subsequently its modal parameters are indirectly compared to the current state of the structure. There are no modal parameters estimated in this method. A subspacebased residual between the reference and possibly damaged states is defined independently from the input excitations employing a ߯ ଶ test and then is compared to a threshold corresponding to the reference state. This technique is applied to a model of the bridge structure located in Reibersdorf, Austria. The structure is excited randomly with white noise at different locations and the output data is generated at typical locations instrumented and measured in a bridge. Various damages are simulated in different elements and the sensitivity of the method to each type and ratio of damages is assessed. This evaluation is performed by comparing the prediction of the damage state using this technique and the simulated damage of the structure. It can be inferred from the results that in general the statistical subspace-based damage detection technique recognizes most of the damage cases, except the ones with insignificant change in the global dynamic behaviour.

On the Influence of Sample Length and Measurement Noise on the Stochastic Subspace Damage Detection Technique

“…The effect of number of samples can be seen in residual (17) both explicitly in terms of √ܰ and implicitly e.g. its variance and the change in the system parameter.…”

“…The reason of pre-multiplying the square root of number of samples in the residual vector is that based on the Central Limit Theorem, the resultant product, i.e. (17), is distributed asymptotically normal as stated in (13) and 18, with its covariance being independent of the number of samples. Moreover, this framework allows for a tradeoff between number of samples and damage size: the χ 2 test variable may have the same value either using a longer dataset with a smaller damage, or using a shorter dataset with a bigger damage.…”

“…It was demonstrated that SSDD technique can perform robustly under ambient excitations with changing statistics [11,14]. In our previous studies, the effect of measurement noise ratio [15][16], type of element damaged [17] and number of samples [16] were studied briefly by examining it for a model of real bridge, the S101 Bridge. It was shown that this technique can deal with even very high ratios of noise in the data.…”