Neural networks-based damage detection for symmetrical structures considering modal feature similarity

Abstract

Symmetric structures are commonly used in various fields. To ensure the safety of these structures, it is crucial to employ appropriate damage detection methods. Among the methods used for structural health detection, the combination of structural dynamics and neural networks based on finite element models has gained popularity due to its non-destructive nature. This study presents a neural network-based damage detection approach for symmetrical structures, taking into account the similarity of modal features. By considering the interference of modal feature similarity, the proposed method effectively detects damage in symmetrical structures. In this approach, the rates of mode shape change before and after damage are used as inputs to the neural network, as they are found to be less sensitive to modeling errors compared to the mode shapes themselves. The effectiveness and adaptability of the method are validated through a numerical case analysis of a symmetrical load-bearing base plate used in walkers for patients with spinal cord injuries.