## 3 Experimental Study
In cases BL1 and BL2, only the four strain gauges near the right joint are considered to be loosened data and the rest eight strain gauge data are considered healthy data. Therefore, each of BL1 and BL2 will produce 40 loosened data and 80 healthy data in 10 trials. Compared to the healthy data set, there were fewer loosened data; therefore, 10 more trials were carried out for BL1 and BL2 and only the loosened data were considered. Finally, a data pool was constructed with 280 healthy and 160 loosened data. The entire data distribution for the classification is given in Table 3. It is assumed that sensor data reasonably close to the loosening of bolts will have some different features compared to the sensor data away from the loosened bolted joint because of the local chattering of bolts, washers, and members. A sample strain time history collected at the location of SG2 is depicted in Figure 5a indicating that a significant portion of the data are not useful for this study. Hence, the informative data recorded after the impact was used for this research work and depicted in Figure 5b. A typical example of time history data obtained at SG2 for the UL case and its effective portion is shown in Figure 5a,b. Figure 4: Bol loosening cases (**a**) BL1, (**b**) BL2, (**c**) BL3, (**d**) BL4, (**e**) BL5 and (**f**) BL6. The normalized time history data were then utilized to determine the features as discussed in Section 2. After the collection of all feature sets from all the data, SVM was trained with the feature set distribution as given in Table 3. Finally, the remaining feature set was utilized for validation. A 10-fold cross-validation experiment was carried out to check whether the algorithm was really trained or just memorizing information. The confusion matrix shown in Table 4 indicates its capability to categorize the data into healthy or loosened cases with a 10-fold average of 93% training and 89% validation accuracy. The validation was also carried out with uncorrelated data as each trial had its own set of random errors. The arrangement of data for different damaged and undamaged cases is given in Table 3. Table 4 depicts that in the case of UL, there are 120 healthy data sets. Among those 120 data sets, 40 were used for training and the remaining 80 were considered for validation. The confusion matrix indicates that all 40 data sets were identified as healthy after training and, the validation results successfully identify all 80 healthy cases. Figure 5: A representative unloosened strain data were collected at SG2: (**a**) entire time history data and (**b**) useful data for the study. \begin{table}
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Similarly, for BL1, there were 80 healthy and 80 loosened data sets. Among 80 healthy data sets, 30 were considered for training and the remaining 50 were considered for validation. The training results show that all 30 were identified as healthy. In the case of validation, out of 50 data sets, 40 were identified as healthy and 10 were incorrectly identified as loosened. For the loosened data sets, 50 data sets were considered for training. The training results indicate that 35 were identified as loosened and 15 were wrongly identified as healthy. In the case of validation, out of 30 data sets, 19 were identified as loosened and 11 were wrongly identified as healthy. Similarly, the numbers of BL2 represent the training and validation results. Further, it was also found that some hand-tight cases show bias towards the healthy case. As four bolts are attached to a very small area and these are hand-tight, there is little movement between the members which may affect the results. Further, with this trained algorithm, different loosening cases (BL3 to BL6) as mentioned in Table 3 were studied. Table 5 shows that although the network was not trained with the data obtained from left joint loosened cases, it classifies the data with 79% accuracy.