Damage identification in a ship's structure using neural networks

被引：67

作者：

Zubaydi, A ^{[1
]}

Haddara, MR ^{[1
]}

Swamidas, ASJ ^{[1
]}

机构：

[1] Mem Univ Newfoundland, Fac Engn & Appl Sci, St Johns, NF A1B 3X5, Canada

来源：

OCEAN ENGINEERING | 2002年 / 29卷 / 10期

关键词：

vibration of stiffened plates; autocorrelation functions; neural networks; parametric identification; crack detection;

D O I：

10.1016/S0029-8018(01)00077-4

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

Visual inspection of large and complex structures such as a ship is difficult and costly due to problems of accessibility. In this paper, a neural network technique is developed for identifying the damage occurrence in the side shell of a ship's structure. The side shell is modeled as a stiffened plate. The input to the network is the autocorrelation function of the vibration response of the structure. The response was obtained using a finite element model of the structure. The output is a single function G(r)(z(r),z(r)), which was formed by adding together the damping and a part of the restoring forces. The function is used to identify not only the damage occurrence in the model but also its extent and location. The results show that the method presented in this work is successful in identifying the occurrence of damage. The detection of the extent and location of damage is promising, however, more work has to be done in this area. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：1187 / 1200

页数：14

共 50 条

[21] Ensemble neural networks for structural damage identification using modal data
Hakim, S. J. S.
Razak, H. Abdul
Ravanfar, S. A.
INTERNATIONAL JOURNAL OF DAMAGE MECHANICS, 2016, 25 (03) : 400 - 430
[22] Seismic damage identification in buildings using neural networks and modal data
Gonzalez, Maria P.
Zapico, Jose L.
COMPUTERS & STRUCTURES, 2008, 86 (3-5) : 416 - 426
[23] Identification of Yeast's Interactome Using Neural Networks
Rehman, Hafeez Ur
Habib, Usman
Ijaz, Umer
Islam, Naveed
Khan, Atta Ur Rehman
Nawaz, Raheel
IEEE ACCESS, 2019, 7 : 179634 - 179645
[24] Chaos identification using Variable Structure Recurrent Neural Networks
Felix, RA
Sanchez, EN
ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE, 2002, : 262 - 266
[25] A ship's minimum-time maneuvering system using neural networks
Mizuno, N
Tamai, Y
Okazaki, T
Ohtsu, K
IECON-2002: PROCEEDINGS OF THE 2002 28TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, 2002, : 1848 - 1853
[26] Structural damage identification by using physics-guided residual neural networks
Wang, Ruhua
Li, Jun
Li, Ling
An, Senjian
Ezard, Bradley
Li, Qilin
Hao, Hong
ENGINEERING STRUCTURES, 2024, 318
[27] Damage mechanisms identification in FRP using acoustic emission and artificial neural networks
de Oliveira, R.
Marques, A. T.
ADVANCED MATERIALS FORUM III, PTS 1 AND 2, 2006, 514-516 : 789 - 793
[28] INFLUENCE OF MEASUREMENT ERRORS ON STRUCTURAL DAMAGE IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKS
王柏生
倪一清
高赞明
Journal of Zhejiang University Science, 2000, (03) : 54 - 62
[29] Damage identification of multi-story steel frames using neural networks
Loh, CH
Huang, CC
STRUCTURAL HEALTH MONTORING 2000, 1999, : 390 - 399
[30] Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain
Machavaram, Rajendra
Shankar, K.
ADVANCES IN STRUCTURAL ENGINEERING, 2012, 15 (10) : 1689 - 1703

← 1 2 3 4 5 →