Structural Health Monitoring with Self-Organizing Maps and Artificial Neural Networks

被引：10

作者：

Avci, Onur ^{[1
]}

Abdeljaber, Osama ^{[1
]}

Kiranyaz, Serkan ^{[2
]}

Inman, Daniel ^{[3
]}

机构：

[1] Qatar Univ, Dept Civil Engn, Doha, Qatar

[2] Qatar Univ, Dept Elect Engn, Doha, Qatar

[3] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA

来源：

TOPICS IN MODAL ANALYSIS & TESTING, VOL 8 | 2020年

关键词：

Structural health monitoring; Self-organizing maps; Artificial neural networks; Structural damage detection; Damage localization;

D O I：

10.1007/978-3-030-12684-1_24

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently developed a nonparametric structural damage detection algorithm for extracting damage indices from the ambient vibration response of a structure. The algorithm is based on self-organizing maps with a multilayer feedforward pattern recognition neural network. After the training of the self-organizing maps, the algorithm was tested analytically under various damage scenarios based on stiffness reduction of beam members and boundary condition changes of a grid structure. The results indicated that proposed algorithm can successfully locate and quantify damage on the structure.

引用

页码：237 / 246

页数：10

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