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The single linear shape sensor response filters were placed at the center of the test region. Five linear shape sensor filters were used and a arithmetic average of their outputs was calculated for each load step in order to compensate for nonspecific effects due to crack growth, as well as the influence of the plastic zone (not shown in Figure 7). The load values for each step were averaged to determine the stress increment and used to create a learning dataset for an artificial neural network. Figure 8 shows the network architecture performed for this experiment.

The results of the ANN for the single linear shape sensor experiment are shown in Figure 9. The learned and tested data results do not coincide. The neural controller with a linear shape sensor may cause such results because it does not compensate for extraneous sources such as the plastic zone and thus extraneous signals may be reflected to the controller. These extraneous signals, in turn, introduce a taint into the learned values leading to the divergence.

The results of the ANN for the four sensors in line experiment are shown in Figure 10. The sensors are clearly lined up, and the learning responses are the same. This experiment shows that the plastic zone does not influence the waveforms of the strain waves and thus cannot be a source of extraneous signals.

The data were then compared to a neural network controller by a neural network classifier to evaluate relative accuracy and precision of the neural network as a predictor of crack extension (see Figure 11). Two different neural network architectures were used. The first architecture used one hidden layer with twenty neurons in the hidden layer. The second architecture used three hidden layers, and each had thirty neurons in the hidden layer. There was no post processing with this data. The data were compared to a dot matrix algorithm. The dot matrix algorithm was created using MATLAB. This neural net algorithm uses a piecewise polynomial function and is designed to match the desired crack extension curve. The dot matrix algorithm had two knots which were chosen to coincide with the two load points in which the load was increased at a rate of one half load per interval between load points. d2c66b5586

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