Spiking response model for uniaxial carbon concrete experimental data

In engineering related tasks, multiple types of neural networks are common methods of solution. Beside the different kinds of artificial neural networks, spiking neural networks (SNN) represent a continuative development in information processing within the computational units of a net. The properties of this neural type is utilized in this contribution in order to evaluate a uniaxial tension test of carbon reinforced specimen regarding the appearance of cracks in the composite structure during the experiment. The crack detection is considered as showcase for further development of evaluation methods based on SNNs with the focal point to engineering related experiments. This contribution is divided into five main parts, whereas the initial brief introduction is devoted to give an overview of neural networks and their computational units, particularly with regard to the classification of spiking neural networks. Since the proposed application of SNNs targets the evaluation of experimental data - especially crack detection - the uniaxial tension test of carbon reinforced concrete specimen is introduced, which is the basis for the experimental data. The utilized spike response model (SRM) is further presented in order to conclusively apply the method to experimental data for the purpose of crack occurrence detection within the data.