Application of Wavelet Packet Transform and Neural Network to Detect Damage of Elastic Thin Plate

Abstract

In recent years,most existing engineering structures which have approached their normal life span, such as concrete plate, concrete beam etc., Almost all of these architecture structures are subjected to damage due to external loads, initial design defect etc. Structural damage detection and assessment has been becoming a focus of increasing interest in civil engineering field. However,At present, the study on structural damage detection is still at initial stage and the adopted main approaches are theoretical analysis and numerical simulation, but physical models are scarce. This leads to the yielded theories and methods are not sufficiently applicable for practical engineering application. Aiming at this, this paper focuses on developing effective methods of using wavelet and neural networks to detect the damage of elastic thin plate due to their extensive applications in civil engineering.

Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation

“…Under the heading of crack identification using the CWT and DWT, WPT methods have also been employed in damage detection subjected to moving loads. Many researchers have conducted damage identification, location, and quantification using wavelet packet decomposition [91,92,93,94,95]. Some have used the WPT for damage detection in beam-like bridges subjected to an impact load or cyclic combined loading [96,97].…”

“…This study provides deep exploration and comprehensive examination of methods for identifying damage in bridges from the perspective of processing dynamic responses to moving loads. The tools of signal processing are related to the Fourier transform (FT) [39,40,41,42,43,44,45,46,47,48,49,50,51,52,53], wavelet transform (WT) [54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99], Hilbert-Huang transform (HHT) [100,101,102,103,104,…”