Machine Learning Techniques in Fracture Mechanics a Comparative Study of Linear Regression, Random Forest, and Ada Boost Model

Machine learning approaches have revolutionized the analysis of fracture mechanics by providing efficient alternatives to traditional analytical and empirical methods. This research explores the use of machine learning methods fracture mechanics, focusing on their applications in predicting material behavior and crack propagation. This research evaluates three machine learning models: linear regression, random forest regression, and Ada boost regression, comparing their performance in training and testing phases. Analysis of model parameters, including tree depth, number of trees, and leaf nodes, reveals significant correlations with prediction accuracy and model stability. The results demonstrate that ABR achieved superior performance during training, followed by RFR (R² = 0.98006) and LR (R² = 0.96297). However, experimental data showed mixed results, with LR demonstrating better generalization capabilities (R² = 0.45561) compared to RFR (R² = 0.15210) and ABR (R² = 0.02597). This study highlights the importance of balancing model complexity with computational efficiency and addresses challenges such as data scarcity and know