• Steel and Composite Structures
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• v.16 no.6
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• pp.559-576
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• 2014

A new mathematical model and its finite element formulation for the non-linear stress-strain analysis of a planar beam strengthened with plates bolted or adhesively bonded to its lateral sides is presented. The connection between the layers is considered to be flexible in both the longitudinal and the transversal direction. The following assumptions are also adopted in the model: for each layer (i.e., the beam and the side plates) the geometrically linear and materially non-linear Bernoulli's beam theory is assumed, all of the layers are made of different homogeneous non-linear materials, the debonding of the beam from the side-plates due to, for example, a local buckling of the side plate, is prevented. The suitability of the theory is verified by the comparison of the present numerical results with experimental and numerical results from literature. The mechanical response arising from the theoretical model and its numerical formulation has been found realistic and the numerical model has been proven to be reliable and computationally effective. Finally, the present formulation is employed in the analysis of the effects of two different realizations of strengthening of a characteristic simply supported flexural beam (plates on the sides of the beam versus the tension-face plates). The analysis reveals that side plates efficiently enhance the bearing capacity of the flexural beam and can, in some cases, outperform the tensile-face plates in a lower loss of ductility, especially, if the connection between the beam and the side plates is sufficiently stiff.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.57-65
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• 2023

A vibration response-based detection system was used to investigate the adhesive areas of single-lap joints using a nonlinear transformation approach for deep learning. In industry or engineering fields, it is difficult to know the condition of an invisible part within a structure that cannot easily be disassembled and the conditions of adhesive areas of adhesively bonded structures. To address these issues, a detection method was devised that uses nonlinear transformation to determine the adhesive areas of various single-lap-jointed specimens from the vibration response of the reference specimen. In this study, a frequency response function with nonlinear transformation was employed to identify the vibration characteristics, and a virtual spectrogram was used for classification in convolutional neural network based deep learning. Moreover, a vibration experiment, an analytical solution, and a finite-element analysis were performed to verify the developed method with aluminum, carbon fiber composite, and ultra-high-molecular-weight polyethylene specimens.