Adaptive neural networks for model updating of structures the radiological dating of injuries
A trained neural network can potentially detect, locate, and quantify structural damage in a short period, and, hence, it can be used for real time damage assessment.
Damage detection by ANN has the advantage that it is a general approach.
This measure can be constructed in the time, frequency, or modal domains, and the latter two are the most broadly used.
However, these algorithms run quite slowly, and the damage assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health monitoring applications.
The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms [1–5].
Model updating is an inverse method that identifies the uncertain parameters in a numerical model and is commonly formulated as an inverse optimization problem.
Among all of the dynamic responses, the FRF is one of the easiest to obtain in real time because the in situ measurement is straightforward.
However, the number of spatial response locations and spectral lines is overly large for neural network applications.