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A novel carbon fibre sensor is developed for the spatially resolved strain\udmeasurement. A unique feature of the sensor is the fibre-break resistive\udmeasurement principle and the two-core transmission line design. The\udelectrical time domain reflectometry (ETDR) is used in order to realize a\udspatially resolved measurement of the electrical parameters of the sensor. In\udthis contribution, the process of mapping between the ETDR signals to the\udexisting strain profile is described. Artificial neural networks (ANNs) are\udused to solve the inverse electromagnetic problem. The investigations were\udcarried out with a sensor patch in a cantilever arm configuration. Overall,\ud136 experiments with varying strain distribution over the sensor length were\udperformed to generate the necessary training data to learn the ANN\udmodel. The validation of the ANN highlights the feasibility as well as the\udcurrent limits concerning the quantitative accuracy of mapping ETDR signals\udto strain profiles

Inverse calculation of strain profiles from ETDR measurements using artificial neural networks