The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm

Engineering Structures

Volumn 30, Issue 10, 2008, Pages 2762-2770

  Lam, Heung Fai ^a   Ng, Ching Tai ^b  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Artificial neural networks; Bayesian model class selection; Benchmark study; Structural damage detection

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; BAYESIAN NETWORKS; COMPOSITE BEAMS AND GIRDERS; DAMAGE DETECTION; ELECTRIC FAULT LOCATION; FEATURE EXTRACTION; HEALTH; LIGHTNING; MILITARY OPERATIONS; MODAL ANALYSIS; NEURONS; PARAMETER ESTIMATION; PATTERN RECOGNITION; PROCESS ENGINEERING; STRUCTURAL ANALYSIS; STRUCTURAL HEALTH MONITORING; TECHNOLOGY; TRANSFER FUNCTIONS; VECTORS;

ANN ALGORITHM; ANN MODELING; ARTIFICIAL NEURAL NETWORKS; BAYESIAN; BAYESIAN MODEL CLASS SELECTION; BENCHMARK STRUCTURE; BENCHMARK STUDY; CASE STUDIES; COMPUTATIONALLY EFFICIENT; DESIGN ALGORITHMS; DESIGN TECHNIQUES; DYNAMIC DATA; FEED FORWARD; HIDDEN LAYERS; HIDDEN NEURONS; MODAL PARAMETERS; PROMISING APPROACH; REAL-TIME DESIGN; RECOGNITION PROCESS; RITZ VECTORS; STRUCTURAL DAMAGE DETECTION; STRUCTURAL DAMAGES; TRAINING PROCESSES;

NEURAL NETWORKS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; HEALTH MONITORING; PATTERN RECOGNITION; STRUCTURAL ANALYSIS;

EID: 52749085340     PISSN: 01410296     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engstruct.2008.03.012     Document Type: Article

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