Issue 75

A. Aabid et alii, Fracture and Structural Integrity, 75 (2025) 55-75; DOI: 10.3221/IGF-ESIS.75.06

(b) Mode II

(c) Mode III Figure 6: Confusion matrix for training and testing data for DTR

Gradient booster equation Similar to the above models, Fig. 7 presents the confusion matrix for the GBR within all three fracture modes for both training and testing datasets. In Mode I, GBR performs reasonably well in training, especially for 5 mm and 20 mm, though some confusion persists between 10 mm and 15 mm. The test matrix shows similar challenges, with noticeable misclassifications among mid-range classes. Next in Mode II, the training matrix remains mostly diagonal, but a few 10 mm and 15 mm cases are incorrectly classified, suggesting mode-related complexity. The testing matrix reflects this trend with a wider spread in misclassification, though 20 mm predictions remain consistent. In the third mode (Mode III), the confusion increases. The training matrix shows more misclassifications across all classes, especially 15 mm and 20 mm. The testing matrix is more scattered, confirming GBR's declining prediction stability under complex out-of-plane loading seen in Mode III. A comparative evaluation of the five selected ML algorithms was conducted across all three fracture modes. Based on the confusion matrix obtained from both training and testing data, it is evident that the classification performance of each model varies significantly depending on the mode and complexity of the fracture behavior. The ETR model consistently demonstrated superior performance, exhibiting strong diagonal dominance across all modes and maintaining high accuracy during testing, particularly in Mode I and II. In comparison, the RF and DTR models displayed relatively poor generalization capabilities, with their testing confusion matrix showing considerable misclassification, especially in Mode III.

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