PERFORMANCE OF THE GENERALIZED DELTA-RULE IN STRUCTURAL DAMAGE DETECTION

被引：15

作者：

BARAI, SV ^{[1
]}

PANDEY, PC ^{[1
]}

机构：

[1] INDIAN INST SCI,DEPT CIVIL ENGN,BANGALORE 560012,KARNATAKA,INDIA

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 1995年 / 8卷 / 02期

关键词：

ARTIFICIAL NEURAL NETWORKS (ANN); BACKPROPAGATION ALGORITHM; BRIDGE STRUCTURE; DAMAGE DETECTION; FEM; GENERALIZED DELTA RULE (GDR); MULTILAYER PERCEPTRONS; NETWORK ARCHITECTURE; STRUCTURAL IDENTIFICATION; TESTING PATTERNS; TRAINING PATTERNS;

D O I：

10.1016/0952-1976(94)00002-5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

引用

页码：211 / 221

页数：11

共 12 条

[1] [Anonymous], 1987, LEARNING INTERNAL RE
[2] AYO JTP, 1985, SAFETY RELIABILITY E
[3] BARAI SV, 1995, 1995 IABSE C KNOWL S
[4] BARAI SV, 1995, IN PRESS J COMPUT CI
[5] Elkordy M.F., 1993, J COMPUT CIVIL ENG, V7, P130, DOI [10.1061/(ASCE)0887-3801(1993)7:2(130), DOI 10.1061/(ASCE)0887-3801(1993)7:2(130)]
[6] NEURAL NETWORKS IN CIVIL ENGINEERING .1. PRINCIPLES AND UNDERSTANDING
FLOOD, I
KARTAM, N
[J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 1994, 8 (02) : 131 - 148
[7] PANDEY PC, 1995, IN PRESS COMPUT STRU
[8] Pao YH, 1989, ADAPTIVE PATTERN REC
[9] SANYAYEI M, 1991, AIAA J, V29, P1174
[10] SIMPSON PK, 1992, ARTIFICIAL NEURAL NE, P3

← 1 2 →