Issue 65

V. Le-Ngoc et alii, Frattura ed Integrità Strutturale, 65 (2023) 300-319; DOI: 10.3221/IGF-ESIS.65.20

the training time compared to an ANN algorithm. Therefore, we choose the bagged decision tree algorithm despite ANN to assess the extent of the cuts. This study used TreeBagger's supervised machine learning function in Matlab software to analyze and model the extracted feature data. In this implementation, we use a dataset including 6338 samples and train a TreeBagger model with 50 trees. Fig. 14 shows a decision tree made up of the training dataset. We then compute the model's out-of-bag (OBB) error, which estimates the classification error on new data. We use 702 samples, including 11 damage scenarios, to test the reliability of the decision tree. The results are represented by the confusion matrix shown in Fig. 15.

Figure 13: Output of the proposed ANN for damaged locations: a) Intact beam; b) Beam with one cut at 4/8 position; c) Beam with two cuts at 4/8 and 7/8 position; d) Beam with three cuts at 1/8; 4/8 and 7/8 position. The confusion matrix is a table with rows representing the true class labels and columns representing the predicted class labels. The four outcomes of the classification problem are:  True positive (TP): the model correctly predicted the positive class.  False positive (FP): the model incorrectly predicted the positive class.  True negative (TN): the model correctly predicted the negative class.  False negative (FN): the model incorrectly predicted the negative class. Several performance metrics are calculated from these four results to show the performance of the decision tree:  Accuracy: The proportion of correctly classified samples in the data set. It is calculated as follows:

TP+TN

accuracy=

(17)

(TP+TN+FP+FN)

314