Intelligent Identification of Structural Damage Based on the Curvature Mode and Wavelet Analysis Theory
Author(s) -
Longsheng Bao,
Yue Cao,
Xiaowei Zhang
Publication year - 2021
Publication title -
advances in civil engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 25
eISSN - 1687-8094
pISSN - 1687-8086
DOI - 10.1155/2021/8847524
Subject(s) - curvature , identification (biology) , computer science , wavelet , finite element method , process (computing) , mode (computer interface) , structural health monitoring , experimental data , software , structural engineering , wavelet transform , data mining , algorithm , reliability engineering , artificial intelligence , mathematics , engineering , statistics , botany , geometry , biology , programming language , operating system
Structural health monitoring is extensively used in new and old structures. During the process of monitoring, a large amount of data is generated. The selection of the appropriate methodology for the analysis of these data constitutes a major problem for maintenance personnel. Therefore, the purpose of this study is to programme data according to the curvature mode and wavelet transform theory to achieve automatic identification of structural damage and explore its applicability. First, the damage model is established with finite element software, and the applicability of the theory is verified by analysing the wavelet coefficients before and after structural damage. Thereafter, a programme is written to achieve the automatic output of the structural damage location based on the finite element results and basic theory. Data fitting is then performed to estimate the degree of structural damage. To evaluate the effects of the practical application of the programme, experimental verification is conducted. The experimental results demonstrate that the programme can automatically output the damage location and avoid the occurrence of calculation errors.
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