Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity

Engineering Structures

Volumn 39, Issue , 2012, Pages 210-220

  Min, Jiyoung ^a   Park, Seunghee ^b   Yun, Chung Bang ^a   Lee, Chang Geun ^c   Lee, Changgil ^b  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Sungkyunkwan University   (South Korea)

^c Express and Transportation Research Institute   (South Korea)

Author keywords

Damage identification; Electromechanical impedance; Frequency range selection; Neural network; Piezoelectric sensors

Indexed keywords

ALUMINUM BEAMS; DAMAGE IDENTIFICATION; DAMAGE INDEX; DAMAGE TYPE; ELECTROMECHANICAL IMPEDANCE; FREQUENCY RANGE SELECTION; FREQUENCY RANGES; IMPEDANCE SENSORS; NEURAL NETWORK TECHNIQUES; NN-BASED APPROACH; OPTIMAL FREQUENCY; PATTERN ANALYSIS; PIEZOELECTRIC SENSORS; PIPE STRUCTURE; QUANTITATIVE INFORMATION; REAL APPLICATIONS; STRUCTURAL DAMAGES;

ALUMINUM BEAMS AND GIRDERS; NEURAL NETWORKS; OPTIMIZATION; SENSOR NODES; STRUCTURAL ANALYSIS;

DAMAGE DETECTION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DAMAGE MECHANICS; IDENTIFICATION METHOD; MONITORING; PIEZOELECTRICITY; PIPE; SENSOR; STRUCTURAL COMPONENT; STRUCTURAL RESPONSE;

EID: 84862826942     PISSN: 01410296     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engstruct.2012.01.012     Document Type: Article

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