Damage Identification in Steel Plates Using a Detection Algorithm Based on 1D Continuous Wavelet Transform and 2D Vibration Mode Shapes

In order to examine and monitor the health of structures, it is essential to identify and pinpoint the site of damage in structural elements and connections. After timely detection of various damage situations, it is possible to prevent the spread of damage by repairing the damage or, if necessary, replacing damaged elements, thereby mitigating potential social and economic losses. The construction industry is increasingly employing thin steel wall plates, particularly as steel plate shear walls. Damage to plate members, particularly steel plate shear walls, can be transferred to other elements and cause overall structural damage. Consequently, this article discusses detecting and determining various damage positions in the steel plate element. ABAQUS finite element analysis software was employed to model both the damaged and undamaged states of the steel plate. Subsequently, dynamic modal information was extracted, including natural frequencies and vibration mode shapes. The study observed a difference in frequency values between the primary and secondary states and an asymmetry of the angle matrix between the primary and secondary forms of the vibration modes due to the presence of damage. After that, a detection algorithm based on the use of primary and secondary shapes of two-dimensional vibration modes and continuous wavelet transform with a one-dimensional theoretical background was proposed, and the detection indices DI-L (detection index of longitudinal extension) and DI-W (detection index of transverse extension) were posited and calculated using the MATLAB.R2021a program. The graphical results of the investigations pertaining to the two proposed indices demonstrated the effectiveness and capability of two-dimensional detection of various damage situations, as peaks resulting from the values of detection indicators appeared in the form of irregularities and disturbances in the damage situations. In addition, the identification values achieved using the detection index matrix for the longitudinal extension were more accurate than those obtained using the detection index matrix for the transverse extension.