Issue 75

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

Figure 2: ML process for crack length prediction.

Selected ML algorithms To accurately predict crack lengths from SIF data under Modes I, II, and III, five distinct regression-based ML algorithms were implemented and evaluated. These models include Support Vector Regressor (SVR), Random Forest Regressor (RF), Extra Trees Regressor (ETR), Decision Tree Regressor (DTR), and Gradient Boosting Regressor (GBR). The selection of these models was guided by their proven ability to handle non-linear relationships, noise robustness, interpretability, and computational efficiency factors critical for structural health monitoring (SHM) and FM. The selected ML algorithms: SVR, RF, ETR, DTR, and GBR are widely used in structural and fracture-related prediction tasks due to their ability to model complex, non-linear relationships. Prior studies such as Yao et al. [23], Omar et al. [25], and Jan et al. [24] have successfully applied these methods for crack behavior prediction, fatigue life estimation, and stress based modeling. Their robust performance in noisy or limited data scenarios makes them suitable choices for the current study. The input features for model training were normalized SIF values derived from theoretical calculation. The target variable was the corresponding crack length, categorized into discrete classes (5 mm, 10 mm, 15 mm, and 20 mm). A regression-to classification approach was employed: each model was trained to predict continuous crack lengths, and these predictions were then mapped to the nearest discrete class to enable both quantitative and classification-based evaluation. Support vector regression This algorithm is a kernel-based algorithm derived from support vector machines, designed to perform regression within an ε -insensitive margin. SVR model minimizes the objective function:   2 * , 1 1 min 2 n i i w b i w C       (8)

Subject to:

y ᵢ - (w ᵀ φ (x ᵢ ) + b) ≤ ε + ξ ᵢ (w ᵀ φ (x ᵢ ) + b) - y ᵢ ≤ ε + ξ ᵢ *

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