Identification of Damages in Steel Structures Using Guided Wave Method

In this thesis, a single piezoelectric lead zirconate titanate (PZT) actuator-sensor pair for locating a through-thickness crack within welded zone in thick steel plate is initially investigated through both finite element analysis and experiments to demonstrate GW-based techniques in plates with simple geometries.;The feature extraction method is applied in a welded tubular steel structure (WTSS). A probability-based damage imaging approach is developed. As validation, the approach is employed to predict the presences and locations of multiple slot-like damages in the welding zones of a WTSS. It can be concluded that the identification results using the extracted signal features are comparable, and accuracy when more damage-impaired sensing paths are involved.;An energy-based damage imaging approach is evaluated by identifying a fatigue crack in a thick steel plate. The propagation of GWs in the plate-like structure is complicated by thick geometry, wave dispersion, boundary reflection, and the existing boundary notch used to initiate the fatigue crack, resulting in diverse forms of interference with fatigue crack identification. Hence, signal features are extracted from the wave energy distribution. Simultaneously, the proposed method is demonstrated by FEM and good agreement is obtained between the numerical and experimental results using a new developed fatigue crack model.;The image-based approach is evaluated experimentally by monitoring of a fatigue crack using time reversal method (TRM). Results indicate that several damage-sensitive features extracted in the normalized captured signals and different pattern recognition techniques are effective for monitoring of fatigue crack propagation in the steel plate, such as TRM, transmission coefficient and principal component analysis (PCA). Some of the experimental results are verified by FEM results.;PCA is validated by monitoring of the propagation of a surface fatigue crack in a welded steel angle structure (WSAS) using GWs generated by a PZT sensor network which is surface-mounted to classify and distinguish different structural conditions due to fatigue crack initiation and propagation. Instead of directly comparing the changes between a series of specific signal segments, signal statistical parameters extracted from the frequency domain are demonstrated to have the capability of monitoring fatigue crack in welded steel structures.