Determining the position of two cracks in a cantilever beam using artificial neural networks

Abstract

During functioning time, structures such as beams are subjected to a variety of loads caused by the working conditions and environment, which can lead to the development of cracks. The current research is concerned with detecting the presence and location of two transverse breathing cracks, in cantilever beams. Starting from the known fact that damages produce a stiffness degradation in structures altering their dynamic parameters, we performed modal simulations of damaged beams to determine their natural frequencies. By using the simulation data, we trained an artificial neural network (ANN), using the feedforward backpropagation algorithm, that is capable to detect the presence of the cracks, their position and for the case when the damages are in proximity, the model can determine if the cracks occur on the same face or opposite faces.

Detection of transverse cracks in steel beams using damage location coefficients and artificial neural networks

“…Once the geometry, constraints, and material properties are set, the mesh is generated by using hexahedral elements of 2 mm maximum edge size, and the simulations are performed to extract the transverse vibration natural frequencies [12].…”